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Discover Hidden Gems of AI/ML Sessions in AWS re:Invent 2021

Last Updated on December 1, 2021 by Editorial Team

Author(s): Juv Chan

Originally published on Towards AI the World’s Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses.

Artificial Intelligence

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As an re:Invent attendee, you are absolutely spoilt for choice over the abundance of AI/ML sessions to choose from each time.

At re:Invent 2021 there is more than 100 AI/ML sessions. If you are looking for some guidance or advice on the AI/ML sessions to attend, here are the hidden gems of AI/ML sessions that you could explore and consider.

Accelerate innovation with machine learning (AIM212-L)

Hear from Bratin Saha, Vice President of Machine Learning & Engines at AWS and Kimberly Madia, Senior Manager of Amazon AI at AWS on how AWS AI and ML services can accelerate innovation for customer, e.g. making more accurate predictions, getting deeper insights from their data, reducing operational overhead and improving customer experiences.

Build standout user experiences using ML with Amazon Personalize (AIM204)

Amazon Personalize is a ML service that allow users to easily add customized recommendations and its use cases include giving users recommendations based on their preferences and behavior, personalized re-ranking of results, and personalizing content for emails and notifications.

Learn how easy you could use Amazon Personalize to tailor product and content recommendations to ensure that your users are getting the content they are looking for, leading to increased engagement and retention.

Implementing MLOps practices with Amazon SageMaker, featuring Vanguard (AIM320)

MLOps (Machine Learning Operations) accelerates the delivery of ML production workloads continuously and automatically. More organizations are using or going to use MLOps to optimize the production lead time and other operational metrics of their ML workload. Implementing MLOps practices helps data scientists, machine learning engineers and DevOps engineers collaborate to prepare, build, train, deploy, and manage models at scale via automated workflows and pipelines.

Hear from Vanguard on their journey of implementing MLOps practices to achieve ML at scale for their polyglot model development platforms using Amazon SageMaker features, including SageMaker projects, SageMaker Pipelines, SageMaker Model Registry, and SageMaker Model Monitor.

Achieve high performance and cost-effective model deployment (AIM408)

Performance efficiency and cost optimization are two of the five pillars in the AWS Well-Architected Framework which represent the abilities to use computing resources efficiently to meet system requirements and deliver business value at the lowest price point.

Hear from Goldman Sachs about how they use Amazon SageMaker for fast, low-latency, and scalable ML model deployments to provide relevant research content recommendations for their clients.

How Bloomberg invested in smarter search with Amazon Kendra (AIM206)

Amazon Kendra is an intelligent search service powered by machine learning that enables your users to search unstructured data using natural language. Amazon Kendra uses machine learning to deliver more relevant answers from unstructured data as well as continuously optimize search results based on end-user search patterns and feedback.

Find out how Bloomberg used Amazon Kendra to build an ML-based search engine for a specific content set without needing to integrate with the legacy search infrastructure and their experience working with the service, including implementation challenges and solutions using Amazon API Gateway, AWS Lambda, and Amazon DynamoDB.


Discover Hidden Gems of AI/ML Sessions in AWS re:Invent 2021 was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

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