Join SoftServe to learn how to deliver a machine learning (ML) solution in days with AWS SageMaker. In this session, SoftServe will share design recommendations and best practices in building large-scale ML systems on Amazon SageMaker using practical examples and real-life use cases.
Amazon SageMaker eliminates most machine learning challenges by providing a fully managed ML infrastructure, tooling, and AutoML capabilities that empower state-of-the-art ML solution delivery in a time-efficient manner—and with minimal effort. AWS SageMaker is an end-to-end solution that assists during all stages of the ML model lifecycle.
You will learn how to address modern business and technical challenges, as well as how to bridge the gap between data, science, IT, business stakeholders, and end-users.
Why join? Learn About Amazon SageMaker and how to address modern business and technical challenges, as well as how to bridge the gap between data, science, IT, business stakeholders, and end-users.
SoftServe’s Data Science Practice Leader, Iurii Milovanov, will lead while also sharing his insights from over a decade of experience building AI, big data, and advanced analytics solutions.
Who should attend: Director of Engineering, Engineering Project Manager, Data Management Director, Director of ML
Agenda: Intro-> Presentation (with polls throughout)-> Demo-> Q&A