The past several years have seen MLOps become an integral part of any organization that plans to take machine learning seriously. However, it can be difficult to know how to effectively implement successful MLOps when you are first starting out. Noah Gift’s upcoming Ai+ Virtual Live Training, Getting Started with Practical MLOps, will address some of the challenges and questions that you might face while building out your organization’s MLOps.
Noah Gift is an MLOps expert with many years experience in both industry and academia. He is the founder of Pragmatic A.I. Labs, and has held roles such as CTO, consulting CTO, cloud architect, and general manager. He is also currently an adjunct professor at Duke MIDS & Northwestern Graduate Data Science & AI, as well as the author of several books and the originator of videos and courses on cloud machine learning, DevOps, Python, Data Science, Big Data and AI.
Getting Started with Practical MLOps is an immersive, hands-on course that will guide you through several topics including what MLOps is, how to get started using MLOps, and best practices for MLOps. By the end of this 4 hour training session you’ll be able to: perform continuous integration for Python ML projects, use the AWS Cloud for MLOps development, create containerized workflows for MLOps, and create Flask and CLI Services for Python ML Projects.
The course comprises the following modules:
Getting Started with MLOps
This first module will be a mix of hands-on exercises and presentations designed to better understand your experience level, introduce MLOps, and explain why you would want to use cloud based development environments.
Building Containerized MLOps Command-Line Tool
Module 2 will introduce you to containers and Docker and guide you through the reasons for choosing Docker Containers over Virtual Machines, as well as give you hands-on practice using Docker Containers.
Build Containerized ML Web Microservice Applications
In this module, you will learn how to build prediction containers through a combination of presentations and hands-on exercises building a Flask Docker sklearn prediction container.
Continuous Delivery Containerized App
The fourth and last module will focus on giving you practice building containers automatically through several exercises focused on deploying Docker prediction containers.
Register Now for this Live Training on Practical MLOps
If you are a Python programmer, data scientist, or software engineer, or you work with data, software, or machine learning and want to become a practical MLOps practitioner and help improve the effectiveness of machine learning in your organization, don’t miss this chance to learn from one of the leading experts in the field. Register soon to save 10% on your ticket.