7 Key Takeaways From AWS re:Invent Event

Table of Contents

7 Key Takeaways From AWS re:Invent Event

1. Specialized Hardware For Generative AI

2. New Foundation Model

3. Support For Generative AI

4. Amazon’s Response To ChatGPT

5. Democratization of Quantum Computing

6. Cost Optimization is the Name of the Game

7. Zero ETL and Vector Databases

7 Key Takeaways From AWS re:Invent Event

Here are seven key takeaways from AWS re:Invent event.

1. Specialized Hardware For Generative AI

Running AI models requires a lot of resources. To cope up with the growing demand, Amazon is introducing AI powered chips and technological infrastructure such as dedicated server Dallas that can handle the workload. What really makes their chips and solutions stand out is their energy efficiency.  Their Graviton and Trainium chips offer improvements in these regards.

Graviton 4 chips deliver a 30% performance boost over its predecessor and 50% more cores and 75% more bandwidth to deliver seamless AI processing.  Trainium 2 delivers four times faster training capabilities as compared to its predecessors. Amazon also announced its partnership with NVIDIA, support for DGX Cloud and GPU project Ceiba, designed to handle AI workloads.

2. New Foundation Model

Amazon added Titan Text Lite and Titan Text Express to Bedrock. In addition to this, it is also previewing its proprietary image generation model called Amazon Titan Image Generation. Moreover, Amazon also released slew of new features that facilitate enterprises in choosing the right foundation model for them.  Model Evaluation in Amazon Bedrock takes the pain out of the process by doing the heavy lifting. It can help you in setting up model evaluation tools and identify benchmarks and even conduct tests.

3. Support For Generative AI

Training AI models manually can be costly and lead to many issues while taking a lot more time. Amazon provided a solution to this problem at the AWS reInvent event. Amazon SageMaker service got two new members dubbed SageMaker HyperPod and SageMaker Inference. 

Amazon SageMaker HyperPod can slash AI model training time by 40%. On the flip side, Amazon SageMaker Inference is geared towards minimizing model deployment costs and reducing latency in model response. This way, users can not only train and deploy models quickly and in a cost effective manner but the model will deliver a better user experience.

SageMaker Canvas, Amazon low code development platform also got a major uplift now enabling users to prepare data within the canvas to develop their own machine learning models. SageMaker Canvas now supports large language models from Anthropic and Cohere as well as AI21 Labs. SageMaker Clarify, brings model evaluation capabilities along with many new features.

4. Amazon’s Response To ChatGPT

Amazon Q, Amazon’s answer to ChatGPT and Microsoft Co-Pilot was unveiled at the AWS re:Invent event. Just like most of its competitors, it can help businesses in generating code, developing applications, responding to customer queries, acting as an AI assistant and generating business intelligence so you can make data driven decisions. 

5. Democratization of Quantum Computing

Ever wished you could have direct access to some of the fastest computers in the world? Amazon makes your dream come true by making quantum computing easily accessible for the masses. Amazon launched a service called Amazon Braket Direct that provides researchers with private access to quantum computers. Researchers can not only get direct access to quantum processing units but they can get help from an expert team from Amazon AWS on workload and optimal resource utilization.

For accessing those quantum computing units, you might have to pay a hefty amount. For instance, IonQ will cost you $7000 per hour meanwhile, Aspen M-3 will cost you $3000 per hour. If you have a lower budget, you can look at QuEra Aquila, which will cost you $2500 per hour. 

6. Cost Optimization is the Name of the Game

Skyrocketing cloud costs is a major concern for businesses and Amazon knows this as well. That is why Amazon has introduced billing and cost management features to keep cloud costs in check. Users can find all these features in Amazon Cost Optimization Hub. This will help you visualize, quantify and filter out your cloud cost savings. It can even offer cost optimization recommendation actions.

AWS Cloud Financial Management Services includes cost explorer and compute optimizer. It brings customer specific pricing and offers discounts to meet your specific cloud computing needs. You get a clear picture of all the enterprise cloud costs and highlight cloud cost optimization strategies to minimize cloud waste. These features are a godsend for FinOps and infrastructure management teams as it helps them see areas of improvements and minimize their cloud costs easily.

7. Zero ETL and Vector Databases

With most businesses working with large data sets, they continuously perform extract transform and load operations to consolidate data from multiple sources at one place. This is essential so they can load that data set into a data warehouse for analysis purposes. 

The sheet number of data sources enterprises use means that this process of collecting data from all desperate sources takes a lot of time and resources. Since the process also involves cleaning, summarizing and filtering new data, it takes even longer and even more resources. Add to that the cost of maintaining data pipelines and teams to manage the process and it can eat up your budget very quickly.

At AWS re:Invent, Amazon announced Amazon RedShift integration with Amazon Aurora PostgreSQL, Amazon Dynamic DB and Amazon RDS for SQL. This means that now users can perform zero ETL between Aurora PostgreSQL, DynamoDB or RDS for MySQL and RedShift. Thanks to the seamless integration, all the transactions can be replicated into these databases in real time in RedShift.

Which is the big takeaway from AWS re:invent event  in your opinion and why? Share it with us in the comments section below.