MLOps Foundation
Overview
Prepare to build enterprise-grade machine learning services and infrastructure in MLOps Foundation, a 2-day instructor-led course tailored for data professionals. This comprehensive program covers essential topics, including the principles of MLOps architecture, deployment strategies, and technologies for machine learning processes. Gain hands-on experience with Seldon Core, Kubeflow and Ray. Prerequisites include intermediate programming skills, mathematics foundation, data analysis familiarity, and a willingness to review pre-course materials. Elevate your skills and apply machine learning effectively in operational contexts. Enroll today to advance your career and stay competitive in the ever-evolving field of MLOps.
Duration
2 Days
Who Should Take This Course
Audience
Data Scientists, Data Engineers, Developers, IT and QA Staff, Technical Managers, DevOps Engineers.
Prerequisites
Participants should have intermediate programming skills (preferably in Python), a foundational understanding of mathematics and statistics, experience with data analysis using tools like Pandas, a grasp of computer science fundamentals, familiarity with common operating systems and basic command-line operations, and a willingness to review pre-course materials to ensure they have a foundational understanding of machine learning concepts before the course begins.
These prerequisites will help participants engage effectively with the course material and hands-on labs, making the learning experience more rewarding. Attendees will also need to be able to ssh into a supplied cloud instance during the course to complete the lab work.
Why You Should Take This Course
Upon completing this course, participants will be able to:
- Understand the fundamental concepts of incorporating machine learning into production processes
- Gain hands-on experience with battle-tested frameworks for training, deploying, serving and
- monitoring machine learning models
- Learn how to adopt a CI/CD approach to machine learning workflows
- Become familiar with the architecture of machine learning processes and services
- Gain exposure to prevailing technologies in the rapidly developing space of MLOps
- Learn how to implement scalable solutions to support machine learning workflows
- Deploy machine learning models using various strategies, including on-premises and cloud deployment
Course Outline
MLOps Foundation
Day 1: Machine Learning Processes
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- MLOps Overview
- Machine Learning Service Management
- Machine Learning Architecture and Tooling
- CI/CD for Machine Learning
Day 2: Deploying and Using Machine Learning Infrastructure
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- Kubernetes Overview
- Orchestration with Kubernetes and Kubeflow
- Scalable Computation with Ray
- Model Serving with Seldon Core