Table of Contents
1. Developing Machine Learning ModelsView notebook44:51
Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality ML models quickly by bringing together a broad set of capabilities purpose-built for ML. Data Scientists and Developers, join us as we walk through a hands-on demo to show you how to develop an ML model end-to-end using SageMaker.
- How to prepare data for machine learning
- How to train and tune models
- How to deploy models to make highly accurate predictions
2. Automating Machine Learning PipelinesView notebook43:55
Amazon SageMaker makes it easy to deploy ML models into production. You can create automated workflows to support development of models in the thousands with scalable infrastructure and continuous integration and continuous delivery (CI/CD) pipelines. With SageMaker, you can build dozens of models per week, prepare massive volumes of data, manage thousands of training experiments, and track lineage for hundreds of model versions. You can share and re-use workflows to recreate or optimize models, helping you scale ML throughout your organization.
- How to establish best practices for MLOps
- How to set up continuous integration and continuous delivery using CI/CD for machine learning
- How to create workflows to automate common machine learning tasks
3. Using AutoMLView notebook44:31
AutoML automates each step of the ML lifecycle so that it’s easier to use machine learning. Amazon SageMaker can automatically build, train, and tune the best ML models based on your data, while allowing you to maintain full control and visibility of your model. You simply provide a tabular dataset, and SageMaker will create models and rank them by performance. You can then use SageMaker to iterate on the generated models to further improve model quality and directly deploy the model to production with just one click.
- How to send a dataset to SageMaker for use in AutoML
- How models are automatically generated by SageMaker
- How to explore the automatically generated notebooks and refine and recreate them for your use case
- How to deploy models with just a few clicks
4. What's New with Amazon FSxOrganizations are migrating their file storage to the cloud to increase agility and lower total cost of ownership. Amazon FSx offers fully managed, feature-rich, and performant file systems with Amazon FSx for Windows File Server for business applications, and FSx for Lustre for compute-intensive workloads. In this session, discover the latest Amazon FSx features to help you simplify your file migration, improve your organizational efficiency, and reduce costs.27:40
- About why more and more customers are moving from on-premises Network Attached Storage (NAS) to fully managed Amazon FSx
- About the latest announcements for your workloads
- About how to use AWS Backup with Amazon FSx for centralized backup and compliance for your Amazon FSx file systems
5. What's New with Amazon Elastic File System (Amazon EFS)In this session, you'll learn about new Amazon Elastic File System (Amazon EFS) features you can use to modernize your applications with persistent file storage for containers and serverless, migrate your Linux file-based workloads to the AWS Cloud, simplify your Amazon EFS console experience, and lower your costs.19:21
- About Amazon Elastic File System (Amazon EFS) enterprise capabilities, AWS integrations, and performance enhancements
- About quickly creating new Amazon EFS file systems from the Amazon EC2 Launch Instance Wizard without leaving the Amazon EC2 console
- About Amazon EFS integrations with Amazon Elastic Container Service (ECS), Amazon Elastic Kubernetes Service (EKS), AWS Fargate, and AWS Lambda
6. What's New with Amazon Elastic Block Store (Amazon EBS)Customers need high-performance block storage for their most critical applications. Join this session to learn how Amazon Elastic Block Store (Amazon EBS) delivers industry leading availability, security, and performance for your workloads at scale. We will discuss the latest capabilities that will help you optimize security, performance, and backups for your business critical applications including enterprise applications, relational and non-relational databases, containerized applications, and big data analytics engines.18:20
- About simple, scalable, high-performance block storage
- About the continuous innovation from Amazon EBS
- About how customers are leveraging EBS for a wide range of workloads
7. What's New in Hybrid Cloud, Edge Computing, and Data TransferIn this session, you'll learn about the latest developments around hybrid cloud storage, edge computing, and data transfer pertaining to AWS Storage Gateway, AWS DataSync, AWS Transfer Family, and AWS Snow Family. We will highlight enhancements that help you optimize your data migration, improve your data transfer security and functionality, and strengthen your edge computing capabilities.28:14
- About how AWS provides a portfolio of data transfer services to provide the right solution for any data migration project
- About the new features AWS has launched across hybrid cloud storage, edge computing, and data transfer services as part of Storage Day
- About moving on-premises data to AWS for migrations or ongoing workflows
8. Evolve Now With AWS Storage Services In The CloudJoin Jeff Barr, VP AWS Evangelism, as he gives his unique perspective on all the new AWS Storage capabilities and solutions that enable you to innovate continuously, extract value from data efficiently, and modernize IT rapidly.3:16
- About the key Storage Day launch themes focused on cost, performance, management, and integration
- About the things you can build and how you can make your existing storage solutions better
- About how to head out there, get building, and let us know how we can help
With Amazon SageMaker, you can get started faster with machine learning (ML). Join our hosts Julien Simon and Segolene Dessertine-Panhard for interactive sessions of live code, demos, conversations, and more. This season of SageMaker Fridays will demonstrate how to solve major challenges specific to your industry using machine learning.
Learn how to prepare, build, train, and deploy high-quality ML models quickly.
Learn how build dozens of models per week, prepare massive volumes of data, manage thousands of training experiments, and track lineage for hundreds of model versions.
Learn how to automates each step of the ML lifecycle so that it’s easier to use machine learning.
Who Should Watch?
- Data Scientists, ML Developers, and IT Ops professionals in the Financial Services industry