AWS Innovate - AI/ML Edition 2022 On-Demand

Welcome to AWS Innovate Online Conference - AI & ML Edition, a virtual event designed to inspire and empower you to accelerate your AI/ML journey. Whether you are new to AI &ML or an advanced user, AWS Innovate has the right sessions for you to apply AI & ML in your organization, and take your ML skills to the next level.

40:07

Opening Keynote: Accelerate innovation with ML

In this session, explore how AWS services can help you move from idea to production with ML and an end-to-end data strategy.

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Industry and Customer Use Cases for Decision Makers
section
1:16:56

Rethink possible: AI/ML innovation stories

Discover the innovation stories changing the way we live, work, play, look after the planet, and accelerate space exploration.

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29:52

Remove unnecessary onboarding friction with real-time fraud detection

Identity theft and the ability for fraudulent users to gain access to digital platforms is a prominent concern.

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31:53

Elevate customer experiences with AWS Contact Center Intelligence

AWS Contact Center Intelligence (CCI) solutions empower you to elevate the customer experience, reduce agent attrition rate, and improve operational efficiency in the contact center of your choice.

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26:33

Build unique user experiences through personalization

Learn how organizations in the retail and media & entertainment industries can easily apply the same AI capabilities.

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Industry and Customer Use Cases for Developers
section
27:12

Extract data and insights from your documents

Organizations across all industries are still manually processing documents, which is time consuming, prone to error, and costly.

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27:47

Adding identity verification to your application

The desire to address this risk is leading to greater adoption of facial recognition to bolster the onboarding or know-your-customer (KYC) efforts of digital platforms.

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27:38

Create real-time personalized user experiences faster at scale

As part of this session, Public Broadcasting Service (PBS) shares their personalization story and its impact.

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27:52

Find accurate information faster with intelligent search

With Amazon Kendra, you can build an intelligent search solution, powered by ML, to find accurate answers from the unstructured content in your enterprise.

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30:39

Streamline content moderation workflows with AI/ML

In this session, learn how automation and AI services from AWS can help your customer service and media teams reclaim up to 95 percent of their time spent doing manual moderation.

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20:22

Three ways ML can transform your developer operations

Join this session to learn how to make the shift toward more automation and proactive mechanisms with ML-powered insights that can help your developer teams innovate faster.

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ML for All
section
26:10

Generate ML predictions without writing any code

Amazon SageMaker Canvas is a visual, point-and-click service that makes it easy for business analysts to build ML models and generate accurate predictions without writing code or having ML expertise.

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25:02

Prepare data for ML with ease, speed, and accuracy

Join this session to learn how to prepare data for ML in minutes using Amazon SageMaker. SageMaker offers tools to simplify data preparation so that you can label, prepare, and understand your data.

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32:08

Amazon SageMaker: Train models with tens or hundreds of billions of parameters

State-of-the-art models are rapidly increasing in size and complexity. These models can be difficult to train because of cost, time, and skill sets required to optimize memory and compute.

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45:39

Use Amazon SageMaker to build high-quality ML models faster

Amazon SageMaker provides all the tools and libraries you need to build ML models.

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32:29

Achieve high-performance and cost-effective model deployment

High-performance and cost-effective techniques, including real-time, asynchronous, and batch, are needed to scale model deployments to maximize your ML investments.

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31:48

Implementing MLOps practices with Amazon SageMaker

MLOps practices help data scientists & IT operations professionals collaborate & manage the production ML workflow, including data preparation & building and training, deploying, & monitoring models

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High-Performance, Low-Cost ML Infrastructure
section
32:41

Choosing the right ML instances for your training and inference deployments

In this session, explore how to choose the proper instance for ML training and inference based on model size, complexity, and performance requirements.

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25:07

Build custom deep learning environments with AWS Deep Learning Containers

In this session, learn how to use Deep Learning Containers to build your custom ML environment and how to implement model training and inference with Deep Learning Containers in Amazon SageMaker.

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27:27

ML at the edge with Amazon SageMaker

In this chalk talk, dive into building computer vision (CV) applications at the edge for predictive maintenance, industrial IoT, and more.

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AWS Innovate - AI/ML Edition 2022 On-Demand

Opening Keynote: Accelerate innovation with ML

In this session, explore how AWS services can help you move from idea to production with ML and an end-to-end data strategy.

Rethink possible: AI/ML innovation stories

Discover the innovation stories changing the way we live, work, play, look after the planet, and accelerate space exploration.

Remove unnecessary onboarding friction with real-time fraud detection

Identity theft and the ability for fraudulent users to gain access to digital platforms is a prominent concern.

Elevate customer experiences with AWS Contact Center Intelligence

AWS Contact Center Intelligence (CCI) solutions empower you to elevate the customer experience, reduce agent attrition rate, and improve operational efficiency in the contact center of your choice.

Build unique user experiences through personalization

Learn how organizations in the retail and media & entertainment industries can easily apply the same AI capabilities.

Extract data and insights from your documents

Organizations across all industries are still manually processing documents, which is time consuming, prone to error, and costly.

Adding identity verification to your application

The desire to address this risk is leading to greater adoption of facial recognition to bolster the onboarding or know-your-customer (KYC) efforts of digital platforms.

Create real-time personalized user experiences faster at scale

As part of this session, Public Broadcasting Service (PBS) shares their personalization story and its impact.

Find accurate information faster with intelligent search

With Amazon Kendra, you can build an intelligent search solution, powered by ML, to find accurate answers from the unstructured content in your enterprise.

Streamline content moderation workflows with AI/ML

In this session, learn how automation and AI services from AWS can help your customer service and media teams reclaim up to 95 percent of their time spent doing manual moderation.

Three ways ML can transform your developer operations

Join this session to learn how to make the shift toward more automation and proactive mechanisms with ML-powered insights that can help your developer teams innovate faster.

Generate ML predictions without writing any code

Amazon SageMaker Canvas is a visual, point-and-click service that makes it easy for business analysts to build ML models and generate accurate predictions without writing code or having ML expertise.

Prepare data for ML with ease, speed, and accuracy

Join this session to learn how to prepare data for ML in minutes using Amazon SageMaker. SageMaker offers tools to simplify data preparation so that you can label, prepare, and understand your data.

Amazon SageMaker: Train models with tens or hundreds of billions of parameters

State-of-the-art models are rapidly increasing in size and complexity. These models can be difficult to train because of cost, time, and skill sets required to optimize memory and compute.

Use Amazon SageMaker to build high-quality ML models faster

Amazon SageMaker provides all the tools and libraries you need to build ML models.

Achieve high-performance and cost-effective model deployment

High-performance and cost-effective techniques, including real-time, asynchronous, and batch, are needed to scale model deployments to maximize your ML investments.

Implementing MLOps practices with Amazon SageMaker

MLOps practices help data scientists & IT operations professionals collaborate & manage the production ML workflow, including data preparation & building and training, deploying, & monitoring models

Choosing the right ML instances for your training and inference deployments

In this session, explore how to choose the proper instance for ML training and inference based on model size, complexity, and performance requirements.

Build custom deep learning environments with AWS Deep Learning Containers

In this session, learn how to use Deep Learning Containers to build your custom ML environment and how to implement model training and inference with Deep Learning Containers in Amazon SageMaker.

ML at the edge with Amazon SageMaker

In this chalk talk, dive into building computer vision (CV) applications at the edge for predictive maintenance, industrial IoT, and more.