Machine Learning Operations
The Machine Learning Operations (MLOps) Certificate is designed for data scientists, machine learning engineers, and software developers eager to bridge the gap between machine learning models and real-world applications. This program offers a comprehensive introduction to the machine learning pipeline, emphasizing the practical skills needed to deploy and manage models in production environments.
Whether you're a professional seeking to expand your expertise or a student with foundational knowledge in machine learning, this course equips you with the tools and techniques to excel in the rapidly evolving field of MLOps. Learn to streamline workflows, ensure model reliability, and bring cutting-edge machine learning solutions to life.
Participants are expected to be familiar with basic statistics and Python.
Learning Objectives:
- Understand the principles and practices of MLOps, including experiment tracking, model reproducibility and deployment, and model monitoring.
- Be able to design and implement workflows for deploying machine learning models to production environments.
- Develop skills to monitor, maintain, and govern machine learning models in production, ensuring scalability, reliability, and compliance.
- Gain practical experience with tools like Docker, Kubernetes, MLflow, and cloud platforms such as AWS, GCP, or Azure.
- Integrate machine learning operations with existing DevOps practices, enhancing collaboration between data science and engineering teams.
Details
Dates: TBD
Schedule: TBD
Location: Online
Instructor: Robert Clements
Continuing Education Units: 2
Cost: $1195, $795 for USF alumni, $295 for USF students
Data Institute
San Francisco, CA 94105
Mon-Fri, 9 a.m. - 5 p.m.