Amazon Web Services (AWS) offers a comprehensive suite of services for running data science workloads in the cloud at scale. These services include Amazon SageMaker, a pre-built suite of machine learning (ML) tools comprised of Textract, Polly, Lex, Comprehend, AWS Glue, Amazon EMR, and Amazon EC2.
In this 1-hour webinar, senior AWS instructor and consultant Michael Forrester discusses the benefits of transitioning data science workloads to AWS and walks through the steps involved in the migration process. By the end of this webinar, attendees will have a solid understanding of how to migrate their data science workloads to the AWS cloud.
In this 1-hour session, Michael will cover how to:
- Decide among the various options for developing, managing, and deploying data science workloads in AWS
- Transition on-premises notebooks to AWS
- Store data used in model development
- Obtain real-time and batch predictions
- General steps to earn the AWS Certified Machine Learning – Specialty certification
Decision makers and/or those in technical roles who want to understand the options and tools that AWS has related to moving Data Science Workloads to AWS.
Basic understanding of ML/AI workloads, including sentiment analysis, probability forecasting, and a basic understanding of AWS and its core services.
Duration: 60 minutes
About the Presenter:
Michael Forrester is an Infrastructure Engineer with over 20 years of experience in the IT industry, specializing in all things DevOps. As an Amazon Authorized Instructor and trainer for almost a decade, he is deeply committed to cultivating DevOps expertise, aiming to optimize software delivery through efficiency, speed, and transparency. A big believer in people as the enabler, his extensive experience in DevOps also includes MLOps and AIOps, positioning him as a comprehensive authority in both traditional and emerging IT operations disciplines.