Introduction
Amazon has thousands of engineers working on machine learning research because it is the key to their future success. By using AI and machine learning, Amazon can improve its services for customers, make its operations more efficient, and stay ahead of competitors.
Amazon Cloud Technology has released several new AIGC products, including Amazon Bedrock, Amazon EC2 Trn1n, Amazon EC2 Inf2, Titan AI, and Amazon CodeWhisperer. This has caused a lot of excitement in the market, with many technology giants trying to enter the game and become the latest trend in artificial intelligence. AIGC has become the technology outlet closest to commercial landing, making it a blue ocean market that is about to explode. But the question is, who will break out of the AIGC chaos?
Amazon’s Family Bucket
Amazon Bedrock is a new service that can access basic large models from AI21 Labs, Anthropic, Stability AI, and Amazon itself through API. Bedrock is the basic framework for users to build and extend AI-based generative applications, accessing powerful text and image large model capabilities including Amazon’s Titan FM.
Amazon is also testing the new Titan FM and plans to roll out two Titan models in the coming months. The first is generative LLM, which is used for tasks such as summarization, text generation, classification, open-ended question answering, and information extraction. The second is embedded LLM, which translates text input into a numerical representation that contains the semantics of the text. Amazon also announced Amazon EC2 Trn1n instances powered by AWS Trainium and Amazon EC2 Inf2 instances powered by AWS Inferentia2.
The Trn1 instance can save 50% of the training cost more than any other EC2 instance, using the Trn1 instance to help shorten the time required to train the largest deep learning model from months to weeks or even days. The instances supported by Inferentia2 are optimized for large-scale generative AI applications of models containing hundreds of billions of parameters. Amazon also announced the preview of Amazon CodeWhisperer, an AI programming companion that can generate code suggestions in real time based on the developer’s natural language comments and previous code in the integrated development environment (IDE).
Amazon Launches New Service for Generative AI
Generative AI is a type of artificial intelligence that can create new content and ideas, such as conversations, stories, images, videos, and music. It is powered by machine learning models that are pre-trained on vast amounts of data.
What are the Problems with Generative AI?
Many customers have reported issues with generative AI, including:
Difficulty finding a high-performance machine learning model that meets their needs
Problems with integrating the model into their applications without managing large infrastructure clusters or incurring significant costs
The desire to use their own data to build customized applications while easily obtaining basic machine learning models
Concerns about data protection, security, and privacy
What is Amazon Bedrock?
Amazon has launched a new service called Amazon Bedrock to address these issues. Bedrock provides access to basic large models from AI21 Labs, Anthropic, Stability AI, and Amazon’s own models through an API. It is the easiest way for users to build and scale AI-based generative applications using machine learning models.
What are the Features of Bedrock?
Bedrock offers the following features:
Access to a series of powerful text and image large-scale model capabilities, including Amazon’s Titan FM
Serverless experience for easy model selection, customization, integration, and deployment using AWS tools and capabilities
Customization of the model by pointing Bedrock to a few labeled examples in an Amazon S3 instance
No use of customer data to train the underlying model
What are the Benefits of Bedrock?
Bedrock offers the following benefits:
Customers can choose from the most cutting-edge machine-learning models available today
Customers can easily customize the model for a specific task without needing to annotate large amounts of data
Customers can protect their data assets, ensure security and privacy, and control data sharing and usage methods
What are the Applications of Bedrock?
Bedrock can be used for a variety of applications, such as:
Content marketing
Advertising and campaign copy
Personalization and search
Product search
What is the Preview Version of Bedrock?
Bedrock is rolling out a preview version to select customers. Shishir Mehrotra, co-founder, and CEO of Coda, an American office collaboration service provider, said: “As a long-term satisfied customer of AWS, we are very interested in how Amazon Bedrock can bring quality, scalability, and reliability to Coda AI. Excited about performance and performance. Since all of our data is already on AWS, we are able to use Bedrock to quickly incorporate generative AI with all the security and privacy we need to protect our built-in data.”
What are the New Titan Models?
AWS has been testing its new Titan FM with some customers and plans to roll it out in the coming months, initially with two Titan models:
Generative LLM, which is used for tasks such as summarization, text generation, classification, open-ended question answering, and information extraction
Embedded LLM, which translates text input into a numerical representation that contains the semantics of the text. While this LLM does not generate text, it is useful for applications such as personalization and search.
Amazon CodeWhisperer
As a software developer, you spend a lot of time writing code. But sometimes, the code you write is simple and repetitive. And with new tools and technologies constantly emerging, it can be hard to keep up. This leaves little time for developing new and innovative features.
The Solution: Amazon CodeWhisperer
That’s where Amazon CodeWhisperer comes in. It’s an AI programming companion that can help you write code more efficiently. CodeWhisperer generates code suggestions in real time based on your natural language comments and previous code in the integrated development environment (IDE). For example, if you need to parse a CSV string of songs and return a structured list based on values like artist, title, and top chart rank, you can simply tell CodeWhisperer to do it for you. CodeWhisperer will generate complete functions that parse strings and return specified lists, saving you time and effort.
The Benefits of Using CodeWhisperer
During a preview trial, AWS conducted a productivity challenge where participants who used CodeWhisperer completed tasks on average 57 percent faster and were 27 percent more likely to succeed than participants who did not use CodeWhisperer. This is a huge leap in productivity!
Supported Languages and Security Features
CodeWhisperer is available for Python, Java, TypeScript, C#, and ten new languages, including Go, Kotlin, Rust, PHP, and SQL. It also has built-in security scanning (powered by automated reasoning) for finding hard-to-detect vulnerabilities and recommending remediation. CodeWhisperer filters out biased or unfair code suggestions and can flag code suggestions similar to open-source code that customers may wish to reference or license.
How to Get Started
CodeWhisperer is free for individual users. Anyone can sign up with just an email account and start using it in minutes without even having an AWS account. For enterprise users, AWS offers a CodeWhisperer Professional Tier, which includes management features such as single sign-on (SSO) integration with AWS Identity and Access Management (IAM) and higher security scanning restrictions. In conclusion, Amazon CodeWhisperer is an excellent tool for developers who want to write code more efficiently and save time. With its AI-powered suggestions and security features, CodeWhisperer is a must-have for any developer looking to improve their productivity.
Trn1n Instances
Trn1n instances powered by Trainium can save up to 50% on training costs over any other EC2 instance. They are optimized to distribute training across multiple servers connected to an 800 Gbps second-generation Elastic Fabric Adapter (EFA) network. Customers can deploy Trn1n instances in UltraClusters, which can scale up to 30,000 Trainium chips (computing over 6ExaFLOPS) in the same AWS availability zone, with a PB-level network. Many AWS customers, including the Helixon, Money Forward, and Amazon Search teams, use Trn1n instances to help reduce the time required to train the largest deep learning models from months to weeks or even days while reducing costs.
New Network-Optimized Trn1n Instances
AWS announced the general availability of new network-optimized Trn1n instances, which offer 1600 Gbps of network bandwidth and are designed to serve large network-intensive models with 20% higher performance than Trn1n. According to AWS, most of the time and money spent on FM today goes to training them because many customers are just starting to deploy FM into production. However, when FM is deployed at scale in the future, most of the cost will be related to running the model and doing inference.
Inf2 Instances
Inferentia2 is specifically optimized for large-scale generative AI applications of models containing hundreds of billions of parameters. Inf2 instances deliver 4x higher throughput and 10x lower latency than previous-generation Inferentia-based instances. They also feature ultra-high-speed connections between accelerators to support large-scale distributed inference. These features improve inference price performance by up to 40% compared to other similar Amazon EC2 instances and enable the lowest inference costs in the cloud. For some models, customers like Runway have found that Inf2 achieves 2X higher throughput than comparable Amazon EC2 instances. This high-performance, low-cost inference will enable Runway to introduce more features and deploy more complex models.
With the new cloud infrastructure for generating AI, AWS is making it easier and more cost-effective for businesses to train and run AI models. The Trn1n and Inf2 instances offer high performance and low cost, making it possible for businesses to deploy more complex models and introduce new features. As AI becomes more prevalent in the business world, AWS is leading the way in providing the infrastructure needed to support it.
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