• It represented the first new Associate-level AWS certification in over a decade! (The Solutions Architect, Developer, and SysOps Administrator Associate exams all debuted with the advent of the AWS global certification program back in 2013.)

• It signaled the beginning of a shift in the AWS certification landscape. Shortly after announcing the new AWS Certified Data Engineer – Associate certification, AWS also announced that it would be retiring three of its Specialty-level exams: the AWS Certified Data Analytics – Specialty, AWS Certified Database – Specialty, and AWS Certified SAP on AWS – Specialty. With fewer Specialty-level certifications, AWS appears to now be prioritizing job role-based certifications at the foundational, associate, and professional levels.

The AWS Certified Data Engineer – Associate exam was available to sit as a beta between November 27, 2023 and January 12, 2024, and pass-fail results have now been shared with everyone who sat the beta exam during that time. AWS is now ready to release the final version of this exam on March 12, 2024!

In this blog post, I’m going to answer some questions you might have about this brand-new exam, including:

• Who should take the new DEA-C01 exam?
• When will the new DEA-C01 exam be available?
• What is the format of the new DEA-C01 exam?
• What information will be covered on the new DEA-C01 exam?
• How can Cloud Academy help me prepare for the new DEA-C01 exam?

Who should take the new DEA-C01 exam?

According to the new DEA-C01 exam guide, the new AWS Certified Data Engineer – Associate (DEA-C01) exam is geared towards individuals with 2-3 years of experience in data engineering and at least 1-2 years of hands-on experience working with AWS services. The exam guide also states that candidates for this certification should understand “the effects of volume, variety, and velocity on data ingestion, transformation, modeling, security, governance, privacy, schema design, and optimal data store design” and promises to validate a candidate’s ability to perform the following tasks:

• Ingest and transform data, and orchestrate data pipelines while applying programming concepts.
• Choose an optimal data store, design data models, catalog data schemas, and manage data lifecycles.
• Operationalize, maintain, and monitor data pipelines.
• Analyze data and ensure data quality.
• Implement appropriate authentication, authorization, data encryption, privacy, and governance.

Passing this exam will require experience and expertise as an IT on-premises or cloud data engineer who works with extract, transform, and load (ETL) pipelines, uses data lakes for data storage, and understands how to analyze data and ensure data quality and consistency.

That being said, there are no formal prerequisites for this–or any other–AWS certification exam, so anyone can register for and sit this exam.

When will the new DEA-C01 exam be available?

Registration opens for the DEA-C01 exam on Tuesday, March 12, 2024. The exam will be available to sit beginning that same day.

What is the format of the new DEA-C01 exam?

Like the other three Associate-level AWS certifications, the new AWS Certified Data Engineer – Associate (DEA-C01) exam will cost $150 USD and contains 65 multiple choice and multiple response questions. Most questions will have 4 possible answer options where you must select one correct answer, while others may have 5 or 6 answer options from which you must select two or three correct answers. Of the 65 questions in the exam, only 50 of them will count towards your score. The other 15 questions are used by AWS for evaluation purposes and do not affect your score in any way. There is no way to tell which questions are scored or unscored but there is also no penalty for guessing, so always be sure to answer every question, even if it’s just an educated guess!

The exam has a pass-fail designation and is scored on a scale of 100-1,000, with a minimum score of 720 required to pass, just like the other Associate-level exams.

What information will be covered on the new DEA-C01 exam?

The new DEA-C01 exam guide references four domains, which are shown in the table below.

DEA-C01 Domains

Let’s briefly discuss what’s covered within each of these four domains in a little more detail.

DEA-C01 Domain 1: Data Ingestion and Transformation (34%)

This domain accounts for over one-third of the overall exam content and focuses on ingesting, transforming, and processing data, as well as orchestrating ETL pipelines for your data. This includes knowing how to read data from AWS services that stream data such as Kinesis, Redshift, and DynamoDB streams, then transforming it based on your requirements using services like Lambda, EventBridge, and AWS Glue workflows. You’ll also need to understand some basic programming concepts such as infrastructure as code, SQL query optimization, and continuous integration and continuous delivery, or CI/CD, when testing and deploying your data pipelines.

DEA-C01 Domain 2: Data Store Management (26%)

In this domain, you’ll need to know how to store and catalog your data. This involves everything from modeling your data to defining schemas for your data, which could be structured, unstructured, or semi-structured. You should have a thorough understanding of all AWS storage platforms and know how to determine the best data store for your needs based on availability and throughput requirements. You’ll also need to manage the lifecycle of your data in a way that is cost efficient, secure, and resilient to failure.

DEA-C01 Domain 3: Data Operations and Support (22%)

This domain will assess your ability to use AWS services to analyze your data, ensuring data quality as you automate the processing of your data. This includes configuring appropriate monitoring and logging of your data pipelines, using services like CloudTrail and CloudWatch to assist in troubleshooting any issues that may arise. You should also be familiar with AWS Glue DataBrew and understand how it can be used for everything from preparing data to be transformed to defining data quality rules, verifying, and cleaning data.

DEA-C01 Domain 4: Data Security and Governance (18%)

This final domain is all about data privacy, authorization, and compliance. You should understand the role of security within an AWS architecture and understand how to implement security in both your VPC network infrastructure as well as your users with AWS Identity and Access Management. This includes knowing the principle of least privilege and how to apply role-based, attribute-based, and policy-based security when appropriate. You’ll also need to understand encryption and how to leverage the AWS Key Management Service to encrypt and decrypt your data.

How can Cloud Academy help me prepare for the new DEA-C01 exam?

As soon as the new AWS Certified Data Engineer – Associate exam was announced last year, our team here at Cloud Academy began to assess the content in our library to help curate a brand new AWS Certified Data Engineer – Associate (DEA-C01) Certification Preparation course that fully covers all aspects of the new DEA-C01 exam guide.

We also sat the beta version of this exam when it was available (it was very challenging for an Associate-level exam!) and leveraged our collective knowledge and experience to build new and updated lessons, hands-on labs, and assessments covering all of the topics that are emphasized in the new DEA-C01 exam. Our new course is currently available in preview, but will be fully updated and published by March 12, 2024!

To find out the latest information about this exam, as well as to learn more about updates to other AWS certification exams, you can visit the Coming Soon to AWS Certification page. From there, you can review the exam guide for the new DEA-C01 exam.

For training preparation on all AWS certifications, I encourage you to browse our entire library of AWS certification content.

Best of luck with your studying, and keep an eye on this space for more AWS certification updates in the months ahead. If you have any questions, please feel free to reach out to me and I’ll be happy to help!

Danny

The post New AWS Certified Data Engineer – Associate (DEA-C01) exam goes live on March 12th, 2024! appeared first on Cloud Academy.

Part 1:

• Artificial Intelligence
• Machine Learning
• Artificial Neural Networks (ANN)
• Deep Learning
• Generative AI (GAI)
• Foundation Models
• Large Language Models
• Natural Language Processing (NLP)
• Transformer Model
• Generative Pretrained Transformer (GPT)

Part 2:

• Responsible AI
• Labelled data
• Supervised learning
• Unsupervised learning
• Semi-supervised learning
• Prompt engineering
• Prompt chaining
• Retrieval augmented generation (RAG)
• Parameters
• Fine Tuning

Bias

When it comes to machine learning, Bias is considered to be an issue in which elements of the data set being used to train the model have weighted distortion of statistical data.  This may unfairly and inaccurately sway the measurement and analysis of the training data, and therefore will produce biassed and prejudiced results.  This makes it essential to have high quality data when training models, as data that is incomplete and of low quality can produce unexpected and unreliable algorithm results due to inaccurate assumptions.

Hallucination

AI hallucinations occur when an AI program falsy generates responses that are made to appear factual and true.  Although hallucinations can be a rare occurrence, this is one good reason as to why you shouldn’t take all responses as granted.  Causes of hallucinations could be create through the adoption of biassed data, or simply generated using unjustified responses through the misinterpretation of data when training.  The term hallucination is used as it’s similar to the way humans can hallucinate by experiencing something that isn’t real.       

Temperature

When it comes to AI, temperature is a parameter that allows you to adjust how random the response output from your models will be.  Depending on how the temperature is set will determine how focused or convoluted the output that is generated will be.  The temperature range is typically between 0 and 1, with a default value of 0.7.  When it’s set closer to 0, the more concentrated the response, as the number gets higher, then the more diverse it will be.

Anthropomorphism

Anthropomorphism is that way in which the assignment of the human form, such as emotions, behaviours and characteristics are attributed to non-human ‘things’, including machines, animals, inanimate objects, the environment and more.  Through the use of AI, and as it develops further and becomes more complex and powerful, people can begin to anthropomorphize with computer programmes, even after very short exposures to it, which can influence people’s behaviours interacting with it.  

Completion

The term completion is used specifically within the realms of NLP models to describe the output that is generated from a response.  For example, if you were using ChatGTP, and you asked it a question, the response generated and returned to you as the user would be considered the ‘completion’ of that interaction.

Tokens

A token can be seen as words and text supplied as an input to a prompt, it can be a whole word, just the beginning or the word, the end, spaces, single characters and anything in between, depending on the tokenization method being used.  These tokens are classed as small basic units used by LLMs to process and analyse input requests allowing it to generate a response based upon the tokens and patterns detected.  Different LLMs will have different token capacities for both the input and output of data which is defined as the context window.   

Emergence in AI

Emergence in AI will typically happen when a model scales in such size with an increasing number of parameters being used that it leads to unexpected behaviours that would not be possible to identify within a smaller model.  It develops an ability to learn and adjust without being specifically trained to do so in that way.  Risks and complications can arise in emergence behaviour in AI, for example, the system could develop its own response to a specific event which could lead to damaging and harmful consequences which it has not been explicitly trained to do.

Embeddings

AI embeddings are numerical representations of objects, words, or entities in a multi-dimensional space. Generated through machine learning algorithms, embeddings capture semantic relationships and similarities. In natural language processing, word embeddings convert words into vectors, enabling algorithms to understand context and meaning. Similarly, in image processing, embeddings represent images as vectors for analysis. These compact representations enhance computational efficiency, enabling AI systems to perform tasks such as language understanding, image recognition, and recommendation more effectively.

Text Classification

Text classification involves training a model to categorise and assign predefined labels to input text based on its content. Using techniques like natural language processing, the system learns patterns and context to analyse the structure from the input text and make accurate predictions on its sentiment, topic categorization and intent. AI text classifiers generally possess a wide understanding of different languages and contexts, which enables them to handle various tasks across different domains with adaptability and efficiency.

Context Window

The context window refers to how much text or information that an AI model can process and respond with through prompts.  This closely relates to the number of tokens that are used within the model, and this number will vary depending on which model you are using, and so will ultimately determine the size of the context window. Prompt engineering plays an important role when working within the confines of a specific content window.

That now brings me to the end of this blog series and so I hope you now have a greater understanding of some of the common vocabulary used when discussing generative AI, and artificial intelligence.

The post Navigating the Vocabulary of Generative AI Series (3 of 3) appeared first on Cloud Academy.

• Artificial Intelligence
• Machine Learning
• Artificial Neural Networks (ANN)
• Deep Learning
• Generative AI (GAI)
• Foundation Models
• Large Language Models
• Natural Language Processing (NLP)
• Transformer Model
• Generative Pretrained Transformer (GPT)

Responsible AI

Responsible AI is designed to set out the principles and practices when working with artificial intelligence to ensure that it is adopted, implemented and executed fairly, lawfully, ethically ensuring trust and transparency is given to the business and its customers.  Considerations to how AI is used and how it may affect humanity must be governed and controlled by rules and frameworks.  Trust, assurance, faith and confidence should be embedded with any models and applications that are built upon AI. 

Labelled Data

Labelled data is used to help machine learning models and algorithms process and learn from raw material.  The data is ‘labelled’ as it contains tags and features associated with the target data which provides useful and informative information about it, for example if you had a photo of a tiger, it could be labelled with ‘Tiger’. This helps to provide context to the raw data which the ML model can then use and extract to help it to learn and recognise other images of tigers.  This raw input data can be in the form of text, images, videos and more and requires human intervention to label the data correctly.

Supervised learning

Supervised learning is a training method used within machine learning which uses a vast amount of labelled datasets in order to be able to predict output variables.  Over time, the algorithms learn how to define the relationship between the labelled input data and the predicted output data using mapping functions.  As it learns, the algorithm is corrected if it makes an incorrect output mapping from the input data, and therefore the learning process is considered to be ‘supervised’.  For example, if it saw a photo of a lion and classified it as a tiger, the algorithm would be corrected and the data sent back to retrain.

Unsupervised learning

Unsupervised learning differs from supervised learning in that supervised learning uses labelled data, and unsupervised learning does not.  Instead it is given full autonomy in identifying characteristics about the unlabeled data and differences, structure and relationships between each data point.  For example, if the unlabeled data contained images of tigers, elephants and giraffes, the machine learning model would need to establish and classify specific features and attributes from each picture to determine the difference between the images, such as colour, patterns, facial features, size and shape.

Semi-supervised learning

This is a method of learning that uses a combination of both supervised and unsupervised learning techniques and so uses both labelled and unlabeled data in its process.  Typically when using this method, you have a smaller data set of labelled data compared to a larger data set of unlabelled data, this prevents you having to tag a huge amount of data.  As a result this enables you to use the smaller set of supervised learning to assist in the training of the model and so aids in the classification of data points using the unsupervised learning technique.  

Prompt Engineering

Prompt engineering allows you to facilitate the refinement of input prompts when working with large language models to generate the most appropriate outputs.  The technique of prompt engineering enables you to enhance the performance of your generative AI models to carry out specific tasks by optimising prompts.  By making adjustments and alterations to input prompts you can manipulate the output and behaviour of the AI responses making them more relevant. Prompt engineering is a principle that is allowing us to transform how humans are interacting with AI.

Prompt Chaining

Prompt chaining is a technique used when working with large language models and NLP, which allows for conversational interactions to occur based on previous responses and inputs.  This creates a contextual awareness through a succession of continuous prompts creating a human-like exchange of language and interaction.  As a result, this is often successfully implemented with chat-bots.  This enhances the user’s experience by responding to bite-sized blocks of data (multiple prompts) instead of working with a single and comprehensive prompt which could be difficult to respond to.

Retrieval augmented generation (RAG)

RAG is a framework used within AI that enables you to supply additional factual data to a foundation model as an external source to help it generate responses using up-to-date information.  A foundation model is only as good as the data that it has been trained on, and so if there are irregularities in your responses, you can supplement the model with additional external data which allows the model to have the most recent, reliable and accurate data to work with.  For example, if you asked ‘what’s the latest stock information for Amazon’ RAG would take that question and discover this information using external sources, before generating the response. This up-to-date information would not be stored within the associated foundation model being used

Parameters

AI parameters are the variables within a machine learning model that the algorithm adjusts during training to enable it to optimise its performance to generalise the patterns from data, and therefore making them more efficient. These values dictate the model’s behaviour and minimise the difference between predicted and actual outcomes.

Fine Tuning

Fine-tuning is the technique of adjusting a pre-trained model on a particular task or data set to improve and enhance its performance.  Initially trained on a broad data set, the model can be fine-tuned using a smaller, and more task-specific data set. This technique allows the model to alter and adapt its parameters to better suit the nuances of the new data, improving its accuracy and effectiveness for the targeted application.

In my next post I continue to focus on AI, and I will be talking about the following topics:

• Bias
• Hallucinations
• Temperature
• Anthropomorphism
• Completion
• Tokens
• Emergence in AI
• Embeddings
• Text Classification
• Context Window

The post Navigating the Vocabulary of Generative AI Series (2 of 3) appeared first on Cloud Academy.

Gen AI is already rapidly changing our daily lives and will continue to do so as the technology is being adopted at an exponential rate. Those within the tech industry need to be aware of the fundamentals and understand how it fits together, and to do this you need to know what a few components are. You can easily become lost in a conversation if you are unaware of what a foundation model (FM), large language model (LLM), or what prompt engineering is and why it’s important.  

In this blog series, I want to start by taking it back to some of the fundamental components of artificial intelligence (AI) and looking at the subset of technologies that have been derived from AI and then dive deeper as we go.

If you want to deep dive into AI, Cloud Academy has a whole dedicated section in its training library. Also, if you’re looking to channel the power of AI in your business, request a free demo today!

Artificial intelligence (AI)

AI can be defined as the simulation of our own human intelligence that is managed and processed by computer systems.  AI can be embedded as code within a small application on your phone, or perhaps at the other end of the scale, implemented within a large-scale enterprise application hosted within the cloud and accessed by millions of customers.  Either way, it has the capabilities to complete tasks and activities that may have previously required human intelligence to complete.  

Machine Learning (ML)

Machine learning is a subset of AI, and is used as a means to enable computer-based systems to be taught based upon experience and data using mathematical algorithms.  Over time, performance is improved and accuracy is increased as it learns from additional sampled data enabling patterns to be established and predictions to be made.  This creates an-going cycle which enables ML to learn, grow, evolve and remodel without human invention.

Artificial Neural Network (ANN)

Neural networks are a subset of Machine Learning that are used to instruct and train computers to learn how to develop and recognize patterns using a network designed not dis-similar to that of the human brain. Using a network consisting of complex and convoluted layered and interconnected artificial nodes and neurons, it is capable of responding to different input data to generate the best possible results, learning from mistakes to enhance its accuracy in delivering results.  

Deep Learning (DL)

Deep learning uses artificial neural networks to detect, identify, and classify data by analysing patterns, and is commonly used across sound, text, and image files.  For example, it can identify and describe objects within a picture, or it can transcribe an audio file into a text file.  Using multiple layers of the neural network, it can dive ‘deep’ to highlight complex patterns using supervised, unsupervised, or semi-supervised learning models

Generative AI (GAI)

Generative AI, or Gen AI is a subset of deep learning and refers to models that are capable of producing new and original content that has never been created before, this could be an image, some text, new audio, code, video and more.  The creation of this content is generated using huge amounts of training data within foundation models, and as a result it creates output that is similar to this existing data, which could be mistaken to have been created by humans.

Foundation Model (FM)

Foundation models are trained on monumental unlabeled broad data sets and underpin the capabilities of Gen AI, this makes them considerably bigger than traditional ML models which are generally used for more specific functions.  FMs are used as the baseline starting point for developing and creating models which can be used to interpret and understand language, converse in conversational messaging, and also create and generate images.  Different foundation models can specialise in different areas, for example the Stable Diffusion model by Stability AI is great for image generation, and the GPT-4 model is used by ChatGPT for natural language.  FMs are able to produce a range of outputs based on prompts with high levels of accuracy.  

Large Language Model (LLM)  

Large language models are used by generative AI to generate text based on a series of probabilities, enabling them to predict, identify and translate consent.  Trained on transformer models using billions of parameters, they focus on patterns and algorithms that are used to distinguish and simulate how humans use language through natural language processing (NLP).  LLMs are often used to summarise large blocks of text, or in text classification to determine its sentiment, and to create chatbots and AI assistants.

Natural Language Processing (NLP)

NLP is a discipline that focuses on linguistics and provides the capacity for computer based systems to understand and interpret how language is used in both written and verbal forms, as if a human was writing or speaking it.  Natural language understanding (NLU), looks at the understanding of the sentiment, intent, and meaning in language, whilst natural language generation (NLG) focuses on the generation of language, both written and verbal, allowing text-to-speech and speech-to-text output.

Transformer Model

A transformer model is used within deep learning architecture and can be found supporting the root of many large language models due to its ability to process text using mathematical techniques in addition to capturing the relationships between the text. This long-term memory allows the model to transfer text from one language to another. It can also identify relationships between different mediums of data, allowing applications to ‘transform’ text (input), into an image (output).  

Generative Pretrained Transformer (GPT)

Generative pre-trained transformers use the Transformer model based upon deep learning to create human-like capabilities to generate content primarily using text, images, and audio using natural language processing techniques.  Used extensively in Gen AI use cases such as text summarization, chatbots, and more.  You will likely have heard of ChatGPT, which is a based on a generative pretrained transformer model.

In my next post I continue to focus on AI, and I will be talking about the following topics:

• Responsible AI
• Labelled Data
• Supervised learning
• Unsupervised learning
• Semi-supervised learning
• Prompt engineering
• Prompt chaining
• Retrieval Augmented Generation (RAG)
• Parameters
• Fine Tuning

The post Navigating the Vocabulary of Generative AI Series (1 of 3) appeared first on Cloud Academy.

What makes this AI model different, is that it was built from the ground up to be multimodal.  That means Gemini can understand and process information from various sources, including text, images, audio, video, and code.  It can also transform any type of input into any type of output.  This sets it apart from earlier models that were limited to handling specific types of data.

Capabilities

As a result, Gemini can:

• Generate text and images: This should result in more engaging and interactive experiences, and even open the doors to new forms of artistic expression.
• Answer complex questions: With its multimodal understanding, Gemini is able to tackle intricate queries that span multiple domains.
• Explain complex concepts: Through its sophisticated reasoning abilities, Gemini can break down complicated ideas into easily digestible explanations.
• Write code: Gemini can understand and generate code in multiple languages, making it a valuable tool for programmers.
• Surpass human experts: On the MMLU benchmark, Gemini outperformed human experts, demonstrating its superior knowledge and problem-solving skills in over 50 different domains.

Applications

If all this is true, the applications could be almost endless.

• Science: By analyzing vast amounts of data, Gemini could accelerate scientific discoveries and breakthroughs.
• Education: With Gemini’s ability to understand diverse information, personalized learning experiences could be tailor-built to match individual needs.
• Healthcare: Gemini could assist with medical diagnosis and treatment by analyzing complex data and making custom recommendations.
• Arts: Gemini could empower artists and creators to explore new forms of expression and push the boundaries of creativity.

Versions

Gemini will be available in three sizes:

• Gemini Ultra: The largest and most powerful model for highly complex tasks.
• Gemini Pro: Best performing model for a wide range of tasks.
• Gemini Nano: The most efficient model for use on mobile devices.

Availability

Starting December 2023, Gemini will be integrated into various Google products and services including:

• Bard: Google’s AI chatbot is already utilizing Gemini Pro for advanced reasoning and understanding.  Gemini Ultra will be added to Bard early next year to create a new experience called Bard Advanced.
• Pixel: Pixel 8 Pro will be the first smartphone to run Gemini Nano, powering new features like Summarize in the Recorder app.
• Search: Gemini will be used to provide more relevant and informative search results.
• Ads: Gemini will optimize ad targeting for greater effectiveness.
• Chrome: Gemini will enhance the browsing experience with personalized features.
• Duet AI: Gemini will power Duet AI for more seamless and natural interactions.

The Future of AI

With its exceptional capabilities, Gemini could be a significant leap forward in AI development.  It just might have the potential to transform the way we live, work, and interact with the world.

Additional resources

• Read the official announcement: https://blog.google/technology/ai/google-gemini-ai/
• Try the new Bard chatbot powered by Gemini Pro: https://bard.google.com/

The post Google Unveils Gemini AI appeared first on Cloud Academy.

After a, now traditional, “The Matrix” introduction and overall theme, Werner went into the topic of cost management and he went deep! I highly recommend this keynote to those old-school IT professionals with software development or data center management experience. You’re in for a treat!

Alright, let’s get into the details:

At this point I didn’t know the entire presentation was going to be centered around cost-management in the cloud but I was intrigued by the book “The Frugal Architect” that he kept referring to, which is a book about designing applications that use resources efficiently to save computing power, memory and in turn: operational expenses. A quick Amazon search revealed: such a book does not exist. More on that later.

Once it was  clear that his entire presentation was going to be around this topic, it all started falling into place. He started to hit specific points and then expanded on those. Here’s a taste.

Align cost to Business

I really loved this point. In the AWS world, we can get super excited about features: high-availability, auto scaling and serverless. 

But we should never forget that if our company’s profit depends on low-cost computing, then perhaps we shouldn’t be under utilizing a super-expensive 4xlarge EC2 instance if we could be doing the same job with a group of smaller, spot instances.

This may not be evident at first, but as the business grows you really don’t want surprises in terms of expenses that directly affect the company’s revenue.

This is something that I already do , due to my Software Development background: Keep costs in mind and by ‘costs’, I mean everything: CPU cycles, storage, number of servers, and so on.

I agree with Werner that Amazon Web Services is an amazing service for all your computing needs, just don’t let that monthly bill run away from you by accepting defaults or wasting resources.

Observability

One of his points was that an application that isn’t tracked and measured will incur in hidden or unexpected costs and this point was a nice segway to introduce CloudWatch Applications signals, a new feature to track application-specific cost and usage.

Languages

At one point, he was very specific about programming languages and their overall footprint and impact in the speed of our code.  Faster, more efficient languages lead to better code that can get the job done faster. He went as far as saying we should be coding in Rust. This is due to its efficiency and speed. I could argue against this:

Granted, Python, Java and .NET Languages are quite heavy due to their underlying support platform — making them pointless for short, transactional programs. But, he failed to account for Development Costs, long-term maintenance and Time-to-Market.  Finding Python and Java developers is quite simple as these are popular languages all over the world. Finding Rust developers? not so sure about this one.

Of course, if we shift our focus back to his point: Operational cost.

A program in Rust, C or C++ that can run in a 100 milliseconds will always outperform the same program written in Python, Java or C# simply because of the super-long load time of the environment itself. So, he is 100% correct in terms of cost savings and sustainability.

He also touched on the phrase “but, we’ve always done things this way…”, trying to say that we shouldn’t be afraid of a new programming language or technology to get the job done in a much more efficient and sustainable way. While I agree with this, not all businesses can afford to transform their Senior Python developers into Junior Rust developers while expecting the same level of output from them, so, your mileage may vary!

Gen AI

When we got to this part of the conversation, I thought “Oh boy, here we go!” and I was expecting the conversation to tangent wildly into language models, image generation, Amazon Q and so on, but no! It was the complete opposite of what I had in mind.

Instead, he showed us use-cases of traditional AI (Machine Learning, SageMaker, Vision) to solve real-world problems, such as interpreting radiology scans, correctly identifying grains of rice for germination and analyzing image data to find and help victims of child abuse.  

By the way, about that software that checks those x-rays images, Dr Vogels has a background in the health industry before making the move to technology, so, he wrote the initial code himself using Python before it was delegated. This code is now open source and much more feature-rich. 

Even in this part of the conversation he stayed traditional as opposed to jumping in the bandwagon of Generative AI. I love it!

Although, not gonna lie: I am a huge advocate of using the Cloud Development Kit and he happened to mention that there are new constructs available, specific to GenAI to help us quickly deploy these solutions for our own, custom needs.

AI predicts, humans decide

He also emphasized that “AI Predicts, but ultimately humans make the decisions”, implying that machines aren’t going to take our jobs, replace our doctors or grow food for us, but they can certainly assist us to help keep up with an ever-growing population. 

As part of his closing argument, he recommends reading his short ebook, The Frugal Architect to help us remember the main points of his conversation.

To wrap this up: It was great! It was certainly geared at old-timers from the very start. In fact, in the first minute he looked at a screen and said “is that a PERL script?”, I couldn’t help but laugh out loud at this one.

Even after the close it was still hilarious: “Hey Werner,  Can I scan my container builds for vulnerabilities in my CI/CD pipeline?” “You can now!” — nice way to sneak in one more new feature which I will certainly look into right away, since I am a DevOps guy.

Now, go build something!

Useful resources from this presentation

In the end he casually dropped this ebook that he wrote, which summarizes the same bullet points that he hit during the presentation. this information is great regardless of cloud computing or not. So, even if you’re not in the cloud yet, you should check it out.

By the way, it is a really short read, so, I highly recommend you take a few minutes of your time and go check it out right now!

CDK

This is the new set of constructs that I mentioned, if you are in need of deploying custom, generative AI solutions in a hurry, you should seriously take a look at this: https://github.com/awslabs/generative-ai-cdk-constructs

The post New AWS re:Invent Announcements: Dr. Werner Vogels Keynote appeared first on Cloud Academy.

• Monday Night Live with Peter DeSantis
• Tuesday’s keynote from Adam Selipsky 
• Wednesday’s keynote from Dr. Swami Sivasubramanian

To wrap up Wednesday’s announcements, we had a keynote delivered by Dr. Ruba Borno, ​​Vice President of AWS Worldwide Channels and Alliances. This keynote was primarily focused on the relationship between AWS and AWS Partners and how they can deliver value to their shared customers. Throughout the keynote, we not only had new announcements, but tons of great use cases and stories from partnerships between AWS, AWS Partners, and customers. 

The theme of the keynote was to help customers make the impossible seem possible by working with the right AWS Partner.

Let’s get into some of the new launches in the AWS Partner Space.

AWS Customer Engagement Incentive

There are so many new opportunities and prospects that have yet to move to AWS. This tool is designed to better address those opportunities. The AWS Customer Engagement Incentive will help AWS Partners engage companies that are new to AWS or in the early stage of adoption. 

Not only does it offset the cost and funds of every part of the sale cycle, it provides a simple global framework that Partners can use to focus on the initial customer workload, better drive the growth and spend of new AWS customers, and scale over time. 

It’s worth mentioning that this incentive is not a new announcement for re:Invent, however, it was launched earlier this year, which is new enough to still get a spot on my blog post. Since it was introduced, Partners have launched 87% of eligible opportunities in the pipeline.

The Generative AI Center of Excellence for AWS Partners

The pace of innovation in the generative AI space has been dizzying. It can be difficult to keep up with the rapid development of these tools. On top of that, you have a huge demand from customers who want to transform their businesses by implementing generative AI into their own technology stacks. 

AWS hopes The Generative AI Center of Excellence for AWS Partners will make it easier for Partners to keep up with the generative AI space to better serve customer needs. It provides both technical and non-technical enablement content and training, example customer use cases, forums for knowledge sharing, and best practices. 

With the center of excellence, AWS Partners can: 

• Familiarize themselves with a wide range of generative AI offerings
• Connect with leaders across the Amazon Partner Network on specialized generative AI applications
• Build applications that go beyond optimization and understand considerations like fairness, privacy, and safety
• Leverage data-driven insights and tools to accelerate customers’ generative AI journeys

Accenture and AWS Expanded Partnership

Accenture has consistently been an early adopter to the latest AWS technology. For example, when Amazon CodeWhisperer launched, Accenture adopted the technology and saw a 30% boost in performance in their development efforts. 

Accenture has announced that they will continue integrating AWS AI services into Accenture’s automation platforms. To scale this, Accenture has committed to training 50,000 development engineers on AWS AI services, such as Amazon Q and Amazon CodeWhisperer. 

Accenture is just one of the many companies that will contribute to content in The AWS Generative AI center for Excellence for AWS Partners, among other leaders in the space such as Anthropic, NVIDIA, and more. 

Additionally, Accenture also has its own Center for Advanced AI, which also offers accelerators, best practices, and hands-on training to better help their clients maximize the value of Amazon Q and other AWS AI services.

C3 Generative AI: AWS Marketplace Edition

C3 AI announced the availability of C3 Generative AI on the AWS Marketplace. This is a no-code, self-service environment that provides a fully functional generative AI application. Users can use this application to get better insight into their data, and start asking questions about their data in just minutes of setup time. 

In fact, not only is it functional, it also takes care of some of those pesky problems that large language models often experience. C3 AI claims that there are no hallucinations, no cyber security risks, and no IP liability problems. 

If you’re interested in potentially using this tool, you can use the QR code located in the photo above to join the private preview.

ServiceNow and AWS Expanded Partnership

ServiceNow and AWS have been brainstorming about how to bring AWS’ reach, data, and scalability and combine it with ServiceNow’s intelligent platform for digital transformation. 

In January 2024, ServiceNow and its full suite of solutions will be available as a Software-as-a-Service (SaaS) offering in the AWS Marketplace. 

Additionally, AWS and ServiceNow are teaming up to launch industry-specific AI tooling to list in the AWS Marketplace. For example, the two companies are currently working on a supply chain solution by integrating AWS Supply Chain with ServiceNow to better streamline supply chain management.

New AWS Partner Competencies

The AWS Competency Partner Program is a way for customers to validate that AWS Partners have the appropriate skills and technical expertise to to help customers in a specific area. AWS has released three new areas of AWS Competency specialization, including:  

• The AWS Resilience Competency Partners, which helps customers improve the availability and resilience of their workloads. 
• The Cyber Insurance Competency Partners, so that customers can find policies from insurers. AWS customers get a quote for the cyber insurance coverage they need within 2 business days.
• The Built-in Competency Partner Solutions, that provides an infrastructure as code solution to install, configure, and integrate Partner software with foundational AWS services.

AWS Marketplace SaaS Quick Launch

SaaS Quick Launch is a new feature in the AWS Marketplace which makes it easy for customers to quickly configure, deploy, and launch SaaS products on AWS. It does this by using CloudFormation templates that are defined by the software vendor and validated by both the software vendor and AWS to ensure the software adheres to the latest security standards. 

In the AWS Marketplace, you can find the SaaS products that use this feature by looking for the “Quick Launch” tag in the product description. Once you click on Quick Launch for the product of your choice, you’ll be able to easily deploy the software. This feature is generally available, so keep an eye out for the Quick Launch tag in the AWS Marketplace.

AWS Marketplace APIs for Sellers

By using AWS Marketplace APIs for Sellers, you can now enable AWS Marketplace access through your current applications. Using your existing applications, you can build AWS Marketplace products, offer, resale authorization, and agreements workflows directly into your own systems. This leads to a greater efficiency for the Partner, and can also encourage customers to make Marketplace purchases through your domain. This feature is generally available and ready to use now.

Pricing Adjustments to AWS Marketplace

AWS is lowering pricing for the AWS Marketplace effective January 2024. The new pricing model will be a flat fee structure of 3 percent. In some cases, it may be even lower. 

Here are the updates to the pricing model: 

• For private offers under a million, it’s now a 3% fee. 
• For deals between $1 million and $10 million, it’s now a 2% fee. 
• For private offers greater than $10 million, it’s now a 1.5% fee. 
• For renewal fees for private software and data, it’s now a 1.5% fee.

AWS and Salesforce Partnership Expansion

AWS and Salesforce have announced an expansion to their partnership so that customers can better benefit from the combined value of both products. 

As part of the announcement, the companies released the following information: 

• Salesforce will begin offering their products on the AWS Marketplace, including products such as Data Cloud, Service Cloud, Heroku, Tableau, Mulesoft, and many more. This is available now with expanded product support planned for 2024. 
• AWS and Salesforce are making Data Cloud a SaaS offering. This enables customers to use AWS services to process and analyze data stored in Data Cloud.

AWS Partner Central Enhancements

Recently, a new AWS Partner Central experience was launched. The new enhancements personalize the experience for the AWS Partner and the roles in their business. This includes automated prescriptive guidance with tasks and next best actions customized for the Partner. 

For AWS Partners enrolled in the software path, AWS is also providing an integrating experience between AWS Partner Central and AWS Marketplace. This means that AWS Partners can create AWS Marketplace listings from AWS Partner Central. By linking your AWS Partner Central and AWS Marketplace accounts and users, it also enables you to see AWS Marketplace analytics in your Partner Analytics Dashboard. 

Finally, they’ve introduced a new co-sell experience through APN Customer Engagements (ACE) that is customized towards the AWS Partner and their co-sell needs. This enables Partners to better maintain pipeline hygiene, by prioritizing opportunities where AWS sales support is needed, providing insight on net new engagements, and stardizing the information gathered in the Partner referral and AWS referral experience. 

Further, Partner ACE pipeline is now integrated with AWS Marketplace Private Offers, providing real time sales insights and the ability to unlock new growth opportunities more easily.

AWS Partner CRM Connector Now Supports AWS Marketplace

The AWS Partner CRM Connector now supports AWS Marketplace, better enabling Partners to manage and publish Marketplace private offers and resale authorizations. The CRM Connector also supports AWS Partner Central ACE Pipeline Manager capabilities, including an enhanced feature to view a summary of AWS Marketplace private offers.

The AWS Partner CRM Connector is available at no-cost on the Salesforce AppExchange.

AWS Sustainability Goals

Finally, we ended the keynote with a talk on sustainability. It’s no secret that AWS is committed to sustainability, with plans to be net zero carbon by 2040, water positive by 2030, and 100% renewable energy-powered by 2025. 

Sustainability goals are not just top of mind for AWS, they’re becoming increasingly important to customers as well. In fact, 1 in 3 customers say that sustainability is a key priority for their business. To meet this customer need, AWS Partners have created over 1,000 sustainability solutions hosted in the AWS Solutions Library and AWS Marketplace. 

Finally, AWS has encouraged AWS Partners to join The Climate Pledge. Over 450 Companies have joined, and the number is expected to grow.

That brings us to the end of this keynote. The AWS Partner space has been innovating at a significant pace this year. Hopefully, these improvements make the lives of AWS Partners much easier. Enjoy the rest of the week!

The post New AWS re:Invent Announcements: AWS Partner Keynote with Dr Ruba Borno appeared first on Cloud Academy.

Today we heard Dr. Swami Sivasubramanian’s keynote address at re:Invent 2023. Dr. Sivasubramanian is the Vice President of Data and AI at AWS. Now more than ever, with the recent proliferation of generative AI services and offerings, this space is ripe for innovation and new service releases. Let’s see what this year has in store!

Swami began his keynote by outlining how over 200 years of technological innovation and progress in the fields of mathematical computation, new architectures and algorithms, and new programming languages has led us to this current inflection point with generative AI. He challenged everyone to look at the opportunities that generative AI presents in terms of intelligence augmentation. By combining data with generative AI, together in a symbiotic relationship with human beings, we can accelerate new innovations and unleash our creativity.

Each of today’s announcements can be viewed through the lens of one or more of the core elements of this symbiotic relationship between data, generative AI, and humans. To that end, Swami provided a list of the following essentials for building a generative AI application:

• Access to a variety of foundation models
• Private environment to leverage your data
• Easy-to-use tools to build and deploy applications
• Purpose-built ML infrastructure

In this post, I will be highlighting the main announcements from Swami’s keynote, including:

• Support for Anthropic’s Claude 2.1 foundation model in Amazon Bedrock
• Amazon Titan Multimodal Embeddings, Text models, and Image Generator now available in Amazon Bedrock
• Amazon SageMaker HyperPod
• Vector engine for Amazon OpenSearch Serverless
• Vector search for Amazon DocumentDB (with MongoDB compatibility) and Amazon MemoryDB for Redis
• Amazon Neptune Analytics
• Amazon OpenSearch Service zero-ETL integration with Amazon S3
• AWS Clean Rooms ML
• New AI capabilities in Amazon Redshift
• Amazon Q generative SQL in Amazon Redshift
• Amazon Q data integration in AWS Glue
• Model Evaluation on Amazon Bedrock

Let’s begin by discussing some of the new foundation models now available in Amazon Bedrock!

Anthropic Claude 2.1

Just last week, Anthropic announced the release of its latest model, Claude 2.1. Today, this model is now available within Amazon Bedrock. It offers significant benefits over prior versions of Claude, including:

• A 200,000 token context window
• A 2x reduction in the model hallucination rate
• A 25% reduction in the cost of prompts and completions on Bedrock

These enhancements help to enhance the reliability and trustworthiness of generative AI applications built on Bedrock. Swami also noted how having access to a variety of foundation models (FMs) is vital and that “no one model will rule them all.” To that end, Bedrock offers support for a broad range of FMs, including Meta’s Llama 2 70B, which was also announced today.

Amazon Titan Multimodal Embeddings, Text models, and Image Generator now available in Amazon Bedrock

Swami introduced the concept of vector embeddings, which are numerical representations of text. These embeddings are critical when customizing and enhancing generative AI applications with things like multimodal search, which could involve a text-based query along with uploaded images, video, or audio. To that end, he introduced Amazon Titan Multimodal Embeddings, which can accept text, images, or a combination of both to provide search, recommendation, and personalization capabilities within generative AI applications. He then demonstrated an example application that leverages multimodal search to assist customers in finding the necessary tools and resources to complete a household remodeling project based on a user’s text input and image-based design choices.

He also announced the general availability of Amazon Titan Text Lite and Amazon Titan Text Express. Titan Text Lite is useful for performing tasks like summarizing text and copywriting, while Titan Text Express can be used for open-ended text generation and conversational chat. Titan Text Express also supports retrieval-augmented generation, or RAG, which is useful when training your own FMs based on your organization’s data.

He then introduced Titan Image Generator and showed how it can be used to both generate new images from scratch and edit existing images based on natural language prompts. Titan Image Generator also supports the responsible use of AI by embedding an invisible watermark within every image it generates indicating that the image was generated by AI.

Amazon SageMaker HyperPod

Swami then moved on to a discussion about the complexities and challenges faced by organizations when training their own FMs. These include needing to break up large datasets into chunks that are then spread across nodes within a training cluster. It’s also necessary to implement checkpoints along the way to guard against data loss from a node failure, adding further delays to an already time and resource-intensive process. SageMaker HyperPod reduces the time required to train FMs by allowing you to split your training data and model across resilient nodes, allowing you to train FMs for months at a time while taking full advantage of your cluster’s compute and network infrastructure, reducing the time required to train models by up to 40%.

Vector engine for Amazon OpenSearch Serverless

Returning to the subject of vectors, Swami explained the need for a strong data foundation that is comprehensive, integrated, and governed when building generative AI applications. In support of this effort, AWS has developed a set of services for your organization’s data foundation that includes investments in storing vectors and data together in an integrated fashion. This allows you to use familiar tools, avoid additional licensing and management requirements, provide a faster experience to end users, and reduce the need for data movement and synchronization. AWS is investing heavily in enabling vector search across all of its services. The first announcement related to this investment is the general availability of the vector engine for Amazon OpenSearch Serverless, which allows you to store and query embeddings directly alongside your business data, enabling more relevant similarity searches while also providing a 20x improvement in queries per second, all without needing to worry about maintaining a separate underlying vector database.

Vector search for Amazon DocumentDB (with MongoDB compatibility) and Amazon MemoryDB for Redis

Vector search capabilities were also announced for Amazon DocumentDB (with MongoDB compatibility) and Amazon MemoryDB for Redis, joining their existing offering of vector search within DynamoDB. These vector search offerings all provide support for both high throughput and high recall, with millisecond response times even at concurrency rates of tens of thousands of queries per second. This level of performance is especially important within applications involving fraud detection or interactive chatbots, where any degree of delay may be costly.

Amazon Neptune Analytics

Staying within the realm of AWS database services, the next announcement centered around Amazon Neptune, a graph database that allows you to represent relationships and connections between data entities. Today’s announcement of the general availability of Amazon Neptune Analytics makes it faster and easier for data scientists to quickly analyze large volumes of data stored within Neptune. Much like the other vector search capabilities mentioned above, Neptune Analytics enables faster vector searching by storing your graph and vector data together. This allows you to find and unlock insights within your graph data up to 80x faster than with existing AWS solutions by analyzing tens of billions of connections within seconds using built-in graph algorithms.

Amazon OpenSearch Service zero-ETL integration with Amazon S3

In addition to enabling vector search across AWS database services, Swami also outlined AWS’ commitment to a “zero-ETL” future, without the need for complicated and expensive extract, transform, and load, or ETL pipeline development. AWS has already announced a number of new zero-ETL integrations this week, including Amazon DynamoDB zero-ETL integration with Amazon OpenSearch Service and various zero-ETL integrations with Amazon Redshift. Today, Swami announced another new zero-ETL integration, this time between Amazon OpenSearch Service and Amazon S3. Now available in preview, this integration allows you to seamlessly search, analyze, and visualize your operational data stored in S3, such as VPC Flow Logs and Elastic Load Balancing logs, as well as S3-based data lakes. You’ll also be able to leverage OpenSearch’s out of the box dashboards and visualizations.

AWS Clean Rooms ML

Swami went on to discuss AWS Clean Rooms, which were introduced earlier this year and allow AWS customers to securely collaborate with partners in “clean rooms” that do not require you to copy or share any of your underlying raw data. Today, AWS announced a preview release of AWS Clean Rooms ML, extending the clean rooms paradigm to include collaboration on machine learning models through the use of AWS-managed lookalike models. This allows you to train your own custom models and work with partners without needing to share any of your own raw data. AWS also plans to release a healthcare model for use within Clean Rooms ML within the next few months.

New AI capabilities in Amazon Redshift

The next two announcements both involve Amazon Redshift, beginning with some AI-driven scaling and optimizations in Amazon Redshift Serverless. These enhancements include intelligent auto-scaling for dynamic workloads, which offers proactive scaling based on usage patterns that include the complexity and frequency of your queries along with the size of your data sets. This allows you to focus on deriving important insights from your data rather than worrying about performance tuning your data warehouse. You can set price-performance targets and take advantage of ML-driven tailored optimizations that can do everything from adjusting your compute to modifying the underlying schema of your database, allowing you to optimize for cost, performance, or a balance between the two based on your requirements.

Amazon Q generative SQL in Amazon Redshift

The next Redshift announcement is definitely one of my favorites. Following yesterday’s announcements about Amazon Q, Amazon’s new generative AI-powered assistant that can be tailored to your specific business needs and data, today we learned about Amazon Q generative SQL in Amazon Redshift. Much like the “natural language to code” capabilities of Amazon Q that were unveiled yesterday with Amazon Q Code Transformation, Amazon Q generative SQL in Amazon Redshift allows you to write natural language queries against data that’s stored in Redshift. Amazon Q uses contextual information about your database, its schema, and any query history against your database to generate the necessary SQL queries based on your request. You can even configure Amazon Q to leverage the query history of other users within your AWS account when generating SQL. You can also ask questions of your data, such as “what was the top selling item in October” or “show me the 5 highest rated products in our catalog,” without needing to understand your underlying table structure, schema, or any complicated SQL syntax.

Amazon Q data integration in AWS Glue

One additional Amazon Q-related announcement involved an upcoming data integration in AWS Glue. This promising feature will simplify the process of constructing custom ETL pipelines in scenarios where AWS does not yet offer a zero-ETL integration, leveraging agents for Amazon Bedrock to break down a natural language prompt into a series of tasks. For instance, you could ask Amazon Q to “write a Glue ETL job that reads data from S3, removes all null records, and loads the data into Redshift” and it will handle the rest for you automatically.

Model Evaluation on Amazon Bedrock

Swami’s final announcement circled back to the variety of foundation models that are available within Amazon Bedrock and his earlier assertion that “no one model will rule them all.” Because of this, model evaluations are an important tool that should be performed frequently by generative AI application developers. Today’s preview release of Model Evaluation on Amazon Bedrock allows you to evaluate, compare, and select the best FM for your use case. You can choose to use automatic evaluation based on metrics such as accuracy and toxicity, or human evaluation for things like style and appropriate “brand voice.” Once an evaluation job is complete, Model Evaluation will produce a model evaluation report that contains a summary of metrics detailing the model’s performance.

Swami concluded his keynote by addressing the human element of generative AI and reaffirming his belief that generative AI applications will accelerate human productivity. After all, it is humans who must provide the essential inputs necessary for generative AI applications to be useful and relevant. The symbiotic relationship between data, generative AI, and humans creates longevity, with collaboration strengthening each element over time. He concluded by asserting that humans can leverage data and generative AI to “create a flywheel of success.” With the impending generative AI revolution, human soft skills such as creativity, ethics, and adaptability will be more important than ever. According to a World Economic Forum survey, nearly 75% of companies will adopt generative AI by the year 2027. While generative AI may eliminate the need for some roles, countless new roles and opportunities will no doubt emerge in the years to come.

I entered today’s keynote full of excitement and anticipation, and as usual, Swami did not disappoint. I’ve been thoroughly impressed by the breadth and depth of announcements and new feature releases already this week, and it’s only Wednesday! Keep an eye on our blog for more exciting keynote announcements from re:Invent 2023!

The post New AWS re:Invent Announcements: Swami Sivasubramanian Keynote appeared first on Cloud Academy.

I think we were all expecting there to be emphasis on generative AI announcements, and this keynote certainly delivered on this point! With gen AI making up a large quantity of the new announcements, we can clearly see that AWS is taking this technology by the horns and not letting go. 

In this post I want to review and highlight the main announcements which include:

• Amazon S3 Express One Zone
• AWS Graviton 4
• R8g instances for EC2
• AWS Trainium2
• Amazon Bedrock customization capabilities
  • Fine Tuning – Cohere Command Light, Meta Llama 2, Amazon Titan Text Lite and Express
  • Amazon Bedrock Retrieval Augmented Generation (RAG) with Knowledge Bases
  • Continued Pre-training for Amazon Titan Text Lite and Express
• Guardrails for Amazon Bedrock
• Agents for Amazon Bedrock
• Amazon Q
• Amazon Q Code Transformation
• Amazon Q in Amazon QuickSight
• Amazon Q in Amazon Connect
• Amazon DataZone AI recommendations
• Amazon DynamoDB zero-ETL integration with Amazon OpenSearch Service
• Zero-ETL integrations with Amazon Redshift

So let’s get started from the top, with the first announcement of Amazon S3 Express One Zone.

Amazon S3 Express One Zone

This is a brand new Amazon S3 storage class designed with exceptional performance in mind. It is aimed to deliver single digit millisecond latency for workloads and applications that require such demands, such as AI/ML training, media processing, HPC and more. When compared to the S3 Standard storage class, Express One Zone (EOZ) can bring you savings of up to 50% and increase your performance by 10x. That means you now have a storage solution that seamlessly handles millions of requests per second with unparalleled efficiency, all while maintaining a consistently low, single-digit millisecond latency.  

This storage class will ensure your data is stored within a single availability zone, as expected, and replicate your data multiple times within that AZ maintaining the durability and availability of your data that we have come to expect from Amazon S3. As a part of this new storage class, AWS has also introduced a new bucket type in order to ensure its performance efficiency.  These new ‘directory buckets’ have been created to support thousands of requests per second, as a result this is specific to the EOZ class.

AWS Graviton4

AWS always has a focus on optimizing performance and cost, and this is what drove them to develop the Graviton series of chips for its EC2 compute offerings. This year, we have another Graviton chip to add to the family, AWS Graviton 4. Boasting performance of 96 Neoverse V2 cores, 2 MB of L2 cache per core, and 12 DDR5-5600 channels, this chip–which is now the most powerful and energy efficient chip offered by AWS–gives its customers even more options when it comes to selecting the right compute power. When compared with the performance of its predecessor, Graviton3, the new Graviton4 chip is 40% faster for databases, 30% faster for web applications, and 45% faster for large Java applications.

R8g Instances for EC2

This announcement followed on nicely from the release of the new chip, as this 8th generation Rxg EC2 instance type, the R8g, was built using Graviton4. It will be released in a variety of sizes and will contain 3 times as many vCPUs and 3 times as much memory as an R7g EC2 instance, making this the best price performance for memory-optimized workloads. This EC2 instance is ideal for any workloads that are memory-intensive, such as high-resolution streaming video, ML/AI, and real-time analytics. As always, security is a number one priority, and with the R8g being built using AWS Graviton4 processors, it also comes with enhanced security and encryption.

AWS Trainium2

With the final announcement from a chip and EC2 instance perspective, Adam announced the new purpose-built chips for generative AI and ML learning, AWS Trainium2. AWS developed Trainium to improve performance and reduce cost for training workloads. They are heavily used for deep learning, large language models (LLMs), and generative AI models. With the new emphasis of gen AI in the industry, this chip will be great news for a lot of businesses looking to harness the benefits that this technology can bring. This new chip has been optimized for training foundation models with hundreds of billions, or even trillions of parameters, and is up to 4x faster than the previous Trn1 chip.

There were a number of announcements made around the customization capabilities of Amazon Bedrock, which is a service that allows you to access different foundation models (FMs) via API calls, including those curated by AWS in addition to those provided by 3rd parties. These announcements included:

• Fine Tuning – Cohere Command Light, Meta Llama 2, Amazon Titan Text Lite and Express
• Amazon Bedrock Retrieval Augmented Generation (RAG) with Knowledge Bases
• Continued Pre-training for Amazon Titan Text Lite and Express

Fine-Tuning: Creating a Fine-tuned model with Amazon Bedrock provides you with the ability to increase the accuracy of your models by allowing you to provide your own labeled business training data for different tasks. This customization training allows your models to learn the most appropriate response that’s specific to your own organizational tasks.

RAG with Knowledge Bases: RAG is a framework used within AI that enables you to supply additional factual data to a foundation model from an external source to help it generate responses using up-to-date information. A foundation model is only as good as the data that it has been trained on, so if there are irregularities in your responses, you can supplement the model with additional external data which allows the model to have the most recent, reliable and accurate data to work with. Knowledge Bases for Amazon Bedrock is a new feature that simplifies the implementation and management of RAG. It’s a fully-managed capability that manages custom data sources, data ingestion, and prompt augmentation, preventing you from having to implement your own custom integrations.
Continued Pre-training: When creating a Continued Pre-training model, you can use your own unlabeled data to train your model with content and knowledge that does not currently exist in the underlying foundation models. This allows you to use company documents, processes, and other documents that contain organization-specific data to improve the accuracy and effectiveness of your trained model. You also have the ability to add more and more unlabeled data to your model to allow you to retrain it to keep it as relevant and accurate as possible.

Guardrails for Amazon Bedrock

Responsible AI is designed to set out the principles and practices when working with artificial intelligence to ensure that it is adopted, implemented, and executed fairly, lawfully, and ethically, ensuring trust and transparency is given to the business and its customers. Considerations to how AI is used and how it may affect humanity must be governed and controlled by rules and frameworks. Trust, assurance, faith, and confidence should be embedded with any models and applications that are built upon AI.

With this in mind, AWS has released Guardrails for Amazon Bedrock, which has been designed to promote responsible AI when building applications on top of foundation models. Using different customized safeguards you can define topics, categories, and content with different filters, ensuring that only relevant content is presented to your users while protecting them from harmful and unacceptable content. These safeguards can be aligned to your own internal organization or company policies, allowing you to maintain your own principles.

Agents for Amazon Bedrock

Amazon Bedrock Agents allow you to implement and define your own autonomous agents within your gen AI applications to help with task automation. These agents can then be used to facilitate end-users in completing tasks based on your own organizational data. The agents will also manage the different interactions between the end user, data sources, and the underlying foundation models, and can also trigger APIs to reach out to different knowledge bases for more customized responses. When configuring Agents, you can give your Agent specific instructions on what it is designed to do and how to interact. Using advanced prompts, you can provide your Agent with additional instructions at each step of the orchestration of your application.

Amazon Q

There were 4 different ‘Q’ services announced during the Keynote. The first was Amazon Q.  

Amazon Q is a generative AI-powered assistant powered by Amazon Bedrock that is designed and tailored specifically for your own internal business. Using your own company data to form a part of its knowledge base, it can be used to help you resolve queries, solve problems, and take action, all by interacting with Q via a chat prompt. By understanding your business processes, policies and more, it can be used by anyone to help you refine, streamline, and optimize your day to day operations and tasks by getting fast and immediate information found throughout your organization’s documentation and information. Connecting it to company data sources is made easy using 40 built-in connectors that connect to platforms such as Slack, Github, Microsoft Teams, and Jira.

Amazon Q Code Transformation

Falling under the same umbrella as Amazon Q, this is a new AI-assistant offering that will allow application developers to streamline and simplify the daunting tasks that are involved when it comes to upgrading your application code. Application upgrades of code can take days or even weeks depending on how many applications you need to upgrade; however, using Amazon Q Code Transformation, this can be reduced to just minutes. If you are running Java version 8 or 11 application code, then you can use Amazon Q Code Transformation to upgrade your code to Java version 17. AWS is also working on the capability to also upgrade from Windows-based .NET frameworks to cross-platform .NET in the coming months. When automating code upgrades, Code Transformation will analyze your existing code, formulate a plan, update packages and dependencies, deprecate inefficient code elements, and adopt security best practices. Upon completion, you are able to review and accept any changes before deploying the upgrade into your production environment.

Amazon Q in Amazon QuickSight

The 3rd Amazon Q announcement brings you Amazon Q in Amazon QuickSight, providing you a generative AI-powered business intelligence assistant. This makes understanding your data within QuickSight easy to navigate and present. Asking Q to provide, discover, and analyze data gets you the results you need quickly and conveniently thanks to natural language processing of your user input. You can continue to converse with Q in QuickSight, refining your requirements based on the results as if you were having a conversation as it contextualizes your previous requests. This enables anyone to be able to create their own dashboards, collect visuals, and gain actionable insights from your company data without requiring BI teams to perform any data manipulation tasks for you.

Amazon Q in Amazon Connect

The final installment of Amazon Q was Amazon Q in Amazon Connect, which is designed to enhance the experience between contact centers and their customers using large language models (LLMs). LLMs are used by generative AI to generate text based on a series of probabilities, enabling them to predict, identify, and translate consent. They are often used to summarize large blocks of text, to classify text to determine its sentiment, and to create chatbots and AI assistants. This enables Amazon Q in Amazon Connect to detect customer intent and use data sources containing organizational information, such as product manuals or catalog references, to respond with recommended content for the customer support agent to communicate back to the customer. These recommended responses and actions are delivered quickly, helping to reduce the waiting time for the customer and increase customer satisfaction, enhancing the customer experience.

Amazon DataZone AI Recommendations

The final announcement involving generative AI was Amazon DataZone AI recommendations. Now available in preview, this new feature enhances Amazon DataZone’s ability to generate a catalog for your business data that’s stored in S3, RDS, or third-party applications like Salesforce and Snowflake by leveraging generative AI and LLMs within Amazon Bedrock to create meaningful business descriptions for your data and its schemas. Previously, DataZone could only generate table and column names for your business data catalog. This added capability provides additional context by describing the meaning of the fields within your tables and their schemas, helping data scientists and engineers analyze data that may not otherwise have enough metadata to properly clarify its meaning. This promises to streamline the process of data discovery and analysis, making your business data more accessible and easier to understand.

Amazon DynamoDB zero-ETL integration with Amazon OpenSearch Service

The final two announcements both involved new zero-ETL integrations with AWS services, the first being Amazon DynamoDB zero-ETL integration with Amazon OpenSearch Service. ETL, short for “extract, transform, and load,” refers to the often time-consuming and expensive process of building data pipelines to sanitize and normalize data that may come from many disparate sources in order to perform further analysis on the data or to use it in your AI or ML workloads. This new feature is now generally available and allows you to leverage the power of Amazon OpenSearch Service, including full-text, fuzzy, and vector search, to query data you have stored in DynamoDB without needing to build a costly ETL pipeline first. You can enable this integration directly within the DynamoDB console, which leverages DynamoDB Streams and point-in-time recovery to synchronize data using Amazon OpenSearch Ingestion. You can specify mappings between fields in multiple DynamoDB tables and Amazon OpenSearch Service indexes. According to AWS, data in DynamoDB will be synchronized to your Amazon OpenSearch Service managed cluster or serverless collection within seconds.

Zero-ETL integrations with Amazon Redshift

Last but not least are some additional new zero-ETL integrations, this time with Amazon Redshift. Many organizations leverage Redshift to perform data analytics, but have generally needed to build ETL pipelines to connect data from sources such as Amazon Aurora, RDS, and DynamoDB. With this announcement, customers can now take advantage of new features to replicate their data from the following sources directly into Amazon Redshift:

• Amazon Aurora MySQL (generally available) – this offering supports both provisioned DB instances as well as Aurora Serverless v2 DB instances, provides near real-time analytics, and is capable of processing over 1 million transactions each minute
• Amazon Aurora PostgreSQL (preview) – this offers near real-time analytics and machine learning on your data stored in Amazon Aurora PostgreSQL
• Amazon RDS for MySQL (preview) – this zero-ETL integration performs seamless data replication between RDS for MySQL and Redshift including ongoing change synchronization and schema replication
• Amazon DynamoDB (limited preview) – this zero-ETL integration offers a fully-managed replication solution between DynamoDB and Redshift without consuming any DynamoDB Read Capacity Units (RCUs)

All of these integrations offer fully-managed solutions for replicating your data into Redshift data warehouses, unlocking the potential for data analysts to gain insight into your business data using analytics queries and machine learning models.

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