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. Walk through a complete data-preparation workflow, including how to label training datasets using SageMaker Ground Truth as well as how to extract data from multiple data sources, transform it using the prebuilt visualization templates in SageMaker Data Wrangler, and create model features.
40:07In this session, explore how AWS services can help you move from idea to production with ML and an end-to-end data strategy.
1:16:56Discover the innovation stories changing the way we live, work, play, look after the planet, and accelerate space exploration.
29:52Identity theft and the ability for fraudulent users to gain access to digital platforms is a prominent concern.
31:53AWS 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.
26:33Learn how organizations in the retail and media & entertainment industries can easily apply the same AI capabilities.
27:12Organizations across all industries are still manually processing documents, which is time consuming, prone to error, and costly.
27:47The 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.
27:38As part of this session, Public Broadcasting Service (PBS) shares their personalization story and its impact.
27:52With Amazon Kendra, you can build an intelligent search solution, powered by ML, to find accurate answers from the unstructured content in your enterprise.
30:39In 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.
20:22Join 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.
26:10Amazon 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.
32:08State-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.
45:39Amazon SageMaker provides all the tools and libraries you need to build ML models.
32:29High-performance and cost-effective techniques, including real-time, asynchronous, and batch, are needed to scale model deployments to maximize your ML investments.
31:48MLOps practices help data scientists & IT operations professionals collaborate & manage the production ML workflow, including data preparation & building and training, deploying, & monitoring models
32:41In this session, explore how to choose the proper instance for ML training and inference based on model size, complexity, and performance requirements.
25:07In 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.
27:27In this chalk talk, dive into building computer vision (CV) applications at the edge for predictive maintenance, industrial IoT, and more.
32:07Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning.
