AWS Modernization Week - AI/ML
Unlock new possibilities with AI & Machine Learning. Learn how to uncover valuable insights to transform your business, streamline operations, automate workflows, and deploy ML models efficiently and at scale. Deep-dive into architectural and deployment best practices, expand and build your technical skills, and learn from AWS experts with step-by-step demos and guides.
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Day 1: Improve Customer Engagement Using AI/MLsection
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Mod Week AI/ML
Automatically uncover insights from your customer conversations
Understanding what your customers are saying is critical to your business. In this session, learn how AWS CCI solutions help you uncover valuable insights from your live and recorded calls.
Enhance customer experience using Amazon Personalize
Learn how to use Amazon Personalize to deliver a wide array of personalization experiences like product and media recommendations, personalized product pre-ranking, and customized direct marketing.
Prevent online fraud while ensuring a frictionless customer experience
Learn how digital platforms of all shapes and sizes can accelerate and improve their ability to identify bad actors with AWS managed services.
Day 2: Improve Internal Processes using AI/MLsection
Boost your IT operational expertise with AIOps
Join this session to learn how you can make the shift towards more automation, more proactive mechanisms and enable your IT team to innovate faster with AWS AIOPs solutions.
AI Workflow Automation for Document Processing
Learn how AI can automate document processing extracting data and insights from insurance claims, mortgage applications, healthcare claims or legal contracts among others.
Find accurate information faster with intelligent search
Create a fully functional search application in just a few clicks with Kendra Experience Builder. Quickly build and deploy your custom search application, without any coding or ML experience.
Day 3: Accelerate ML model developmentsection
Prepare data for ML with ease, speed, and accuracy
Learn how to prepare data for ML using Amazon SageMaker. See a complete data-preparation workflow including how to extract data from multiple data sources, transform it, and create model features.
Spin up Jupyter notebooks at scale and increase your productivity
In this session, learn how to use fully managed Jupyter notebooks complemented by an in-depth demonstration on how to build ML models at scale.
Train deep learning models faster with Amazon SageMaker
In this session, you’ll learn about compiler technologies such as operator fusion and how SageMaker Training Compiler can be used to accelerate your training jobs.
Day 4: Deploy and manage ML models at scalesection
Implementing MLOps practices with Amazon SageMaker
Explore the features in Amazon SageMaker Pipelines that help you increase automation, track data lineage, catalog ML models for production, improve the quality of your end-to-end workflows, and more.
Achieve high performance and cost-effective model deployment
Explore the different inference options available in Amazon SageMaker such as multi-container endpoints, inference pipelines, multi-model endpoints, and more.
Easily deploy models for the best performance and cost using Amazon SageMaker
Learn how Amazon SageMaker Inference Recommender auto-selects the compute instance type, instance count, container parameters, and model optimizations to maximize performance and minimize cost.
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