Amazon Web Services recently concluded its highly anticipated re:Invent 2023 event, showcasing a resurgence of big community events in the tech industry after the pandemic-related hiatus. With a record-breaking attendance of around 50,000 participants, re:Invent 2023 was a milestone event with significant announcements and insights for the tech world.
Here’s a summary of top announcements, followed by the keynote from Adam Selipsky, AWS CEO, and in-depth announcements.
Generative AI was, unsurprisingly, the buzzword. AWS rolled out an exciting new AI chip, support for new foundation models, updates to its generative AI platform Amazon Bedrock, and support for vector databases and zero-ETL integrations. They also announced a new generative AI assistant dubbed Amazon Q (in reference to Star Trek’s “Q” character, seemingly). Q will probably have a broad set of use cases in enterprises.
1. Amazon Q: A new generative AI assistant
A Gen AI-powered assistant designed to help get relevant answers, solve problems, and generate content using Retrieval Augmented Generation (RAG). It also integrates with other AWS services:
- AWS Console: Amazon Q simplifies exploring AWS’s framework, best practices, and documentation. It is accessible in the AWS management console. For example, “How to build a web application on AWS?” yields a list of services like AWS Amplify, AWS Lambda, and Amazon EC2, along with their advantages and resources.
- Connect: Cloud-based contact center service ensures scalable customer service operations. Amazon Q enhances customer interactions by understanding needs, minimizing wait times, and delivering real-time responses via API integration.
- Amazon QuickSight: Business intelligence service offering interactive dashboards, paginated reports, and embedded analytics. Amazon Q within QuickSight enhances productivity for business analysts and users by swiftly creating visuals, summarizing insights, answering data queries, and constructing data stories using natural language.
2. Expanded choice of models in Amazon Bedrock
Bedrock is the foundation model library from AWS. It enables the integration of various models that underpin generative AI. AWS have introduced some new models to Amazon Bedrock:
- Meta’s popular LLaMA-2
- Anthropic Claude 2.1: An update to Claude 2, it now offers a 200k token context window (vs. 128k for GPT 4 Turbo), reduced rates of hallucination, and improved accuracy over long documents.
- Amazon Titan Image Generator: Similarly to tools such as Mid-journey, Stable Diffusion, and Dall-E, Titan Image Generator lets you not only create but also improve images with natural language commands. Titan also supports enterprise needs for invisible watermarks on images.
- Amazon Titan Multimodal Embeddings: Improve searches by understanding images and text. For instance, a stock photo company could use it to find specific images based on descriptions or other images, enhancing accuracy and speed.
3. Four New Capabilities for AWS Supply Chain
An application that unifies data and provides ML-powered actionable insights. It incorporates embedded contextual collaboration and demand planning features while seamlessly integrating with your client’s current enterprise resource planning (ERP) and supply chain management systems. Announced three new capabilities:
- Supply planning (Preview): Plans purchases of raw materials, components, and finished goods. This capability considers economic factors, such as holding and liquidation costs.
- Visibility and sustainability (Preview): Extends visibility and insights beyond your client’s organization to your external trading partners. This visibility lets you align and confirm orders with suppliers, improving the accuracy of planning and execution processes.
- Amazon Q (Preview): As mentioned above, Amazon Q is now integrated with Supply Chain services to empower inventory managers and planners with intelligent insights and explore what-if scenarios.
4. SageMaker capabilities (Preview)
- HyperPod: accelerates model training by up to 40%, enabling parallel processing for improved performance.
- Inference: reduces deployment costs and latency by deploying multiple models to the same AWS instance.
- Clarify: supports responsible AI use by evaluating and comparing models based on chosen parameters.
- Canvas: enhancements facilitate seamless integration of generative AI into workflows.
5. Next-generation AWS-designed chips (Preview)
Amazon jumped into the custom cloud-optimized chip fray and introduced two new chips:
- Graviton4: A powerful and energy-efficient chip suitable for various cloud tasks.
- Trainium2: A high-performance computing chip, it helps accelerate model training while making it more cost-effective.
Notable AWS customers like Anthropic, Databricks, Datadog, Epic, Honeycomb, and SAP already use these chips.
6. Amazon S3 Express One Zone
A new purpose-built storage class for running applications that require extremely fast data access.
7. New integrations for a zero-ETL future
Aim to make data access and analysis faster and easier across data stores. Four new integrations are:
- Amazon Aurora PostgreSQL, Amazon DynamoDB, and Amazon RDS for MySQL integrations with Amazon Redshift: Query and analyze data from multiple relational and non-relational databases in Amazon Redshift without building and maintaining custom data pipelines.
- Amazon DynamoDB integration with Amazon OpenSearch Service: Perform full-text and vector search on DynamoDB data in near real-time.
To learn more about all those exciting news, please check the AWS Re:Invent keynote blog.
