https://aws.amazon.com/blogs/machine-learning/announcing-new-tools-for-building-with-generative-ai-on-aws/

This is the announcement with info on what this means.

So like, what exactly is this? I’m seeing a lot of buzzwords, but no real information here.

I just qucikly read the announcements because I don’t have enough knowledge to understand what half of the stuff means anyway.

Here are the more interesting bits:

Section:

Announcing Amazon Bedrock and Amazon Titan models, the easiest way to build and scale generative AI applications with FMs

Interesting bit:

We took all of that feedback from customers, and today we are excited to announce Amazon Bedrock, a new service that makes FMs from AI21 Labs, Anthropic, Stability AI, and Amazon accessible via an API. Bedrock is the easiest way for customers to build and scale generative AI-based applications using FMs, democratizing access for all builders. Bedrock will offer the ability to access a range of powerful FMs for text and images—including Amazon’s Titan FMs, which consist of two new LLMs we’re also announcing today—through a scalable, reliable, and secure AWS managed service. With Bedrock’s serverless experience, customers can easily find the right model for what they’re trying to get done, get started quickly, privately customize FMs with their own data, and easily integrate and deploy them into their applications using the AWS tools and capabilities they are familiar with, without having to manage any infrastructure (including integrations with Amazon SageMaker ML features like Experiments to test different models and Pipelines to manage their FMs at scale).

Section:

Announcing the general availability of Amazon EC2 Trn1n instances powered by AWS Trainium and Amazon EC2 Inf2 instances powered by AWS Inferentia2, the most cost-effective cloud infrastructure for generative AI

Interesting bit:

Trn1 instances, powered by Trainium, can deliver up to 50% savings on training costs over any other EC2 instance, and are optimized to distribute training across multiple servers connected with 800 Gbps of second-generation Elastic Fabric Adapter (EFA) networking. Customers can deploy Trn1 instances in UltraClusters that can scale up to 30,000 Trainium chips (more than 6 exaflops of compute) located in the same AWS Availability Zone with petabit scale networking. Many AWS customers, including Helixon, Money Forward, and the Amazon Search team, use Trn1 instances to help reduce the time required to train the largest-scale deep learning models from months to weeks or even days while lowering their costs. 800 Gbps is a lot of bandwidth, but we have continued to innovate to deliver more, and today we are announcing the general availability of new, network-optimized Trn1n instances, which offer 1600 Gbps of network bandwidth and are designed to deliver 20% higher performance over Trn1 for large, network-intensive models.

Second interesting bit:

That’s why we’re announcing today the general availability of Inf2 instances powered by AWS Inferentia2, which are optimized specifically for large-scale generative AI applications with models containing hundreds of billions of parameters. Inf2 instances deliver up to 4x higher throughput and up to 10x lower latency compared to the prior generation Inferentia-based instances. They also have ultra-high-speed connectivity between accelerators to support large-scale distributed inference. These capabilities drive up to 40% better inference price performance than other comparable Amazon EC2 instances and the lowest cost for inference in the cloud. Customers like Runway are seeing up to 2x higher throughput with Inf2 than comparable Amazon EC2 instances for some of their models. This high-performance, low-cost inference will enable Runway to introduce more features, deploy more complex models, and ultimately deliver a better experience for the millions of creators using Runway.

Section:

Announcing the general availability of Amazon CodeWhisperer, free for individual developers

Interesting bit:

Generative AI can take this heavy lifting out of the equation by “writing” much of the undifferentiated code, allowing developers to build faster while freeing them up to focus on the more creative aspects of coding. This is why, last year, we announced the preview of Amazon CodeWhisperer, an AI coding companion that uses a FM under the hood to radically improve developer productivity by generating code suggestions in real-time based on developers’ comments in natural language and prior code in their Integrated Development Environment (IDE). Developers can simply tell CodeWhisperer to do a task, such as “parse a CSV string of songs” and ask it to return a structured list based on values such as artist, title, and highest chart rank. CodeWhisperer provides a productivity boost by generating an entire function that parses the string and returns the list as specified. Developer response to the preview has been overwhelmingly positive, and we continue to believe that helping developers code could end up being one of the most powerful uses of generative AI we’ll see in the coming years. During the preview, we ran a productivity challenge, and participants who used CodeWhisperer completed tasks 57% faster, on average, and were 27% more likely to complete them successfully than those who didn’t use CodeWhisperer. This is a giant leap forward in developer productivity, and we believe this is only the beginning.

Today, we’re excited to announce the general availability of Amazon CodeWhisperer for Python, Java, JavaScript, TypeScript, and C#—plus ten new languages, including Go, Kotlin, Rust, PHP, and SQL. CodeWhisperer can be accessed from IDEs such as VS Code, IntelliJ IDEA, AWS Cloud9, and many more via the AWS Toolkit IDE extensions. CodeWhisperer is also available in the AWS Lambda console. In addition to learning from the billions of lines of publicly available code, CodeWhisperer has been trained on Amazon code. We believe CodeWhisperer is now the most accurate, fastest, and most secure way to generate code for AWS services, including Amazon EC2, AWS Lambda, and Amazon S3.