With both machine learning and DevOps at the forefront these days, Milecia McGregor helps engineers understand how to apply key DevOps principles to their machine learning projects.
When teams are working with machine learning models, changing features, different data sets, new algorithms, and unique computing resources all influence a machine learning model’s performance. Tracking all of these items can be complicated. With tools such as DVC, MLFlow, AWS, you can meet the challenge. Milecia McGregor demonstrates how to use MLOps tools to improve machine learning and automate some of the steps in the process.
About the Instructor:
Milecia McGregor is a software generalist that has worked in numerous areas of tech over the past decade. She has a master’s degree in mechanical and aerospace engineering and has done machine learning work for human-computer interfaces on autonomous vehicles. She has done work on the front-end and back-end, data science, robotics, DevOps, cybersecurity, VR, and all the other areas. Milecia has worked on projects like the Mozilla VPN and apps that work with brain signals. She is also an international speaker in the tech community with talks covering a variety of topics across multiple programming languages.
- Beginner to Intermediate
What You Will Learn:
Developers and Engineers will learn how to:
- Capitalize on MLOps as an emerging field. Data-focused companies are looking for engineers with these skill sets.
- Build a basic MLOps pipeline from scratch with open-source tools – take a working template with you for your own projects
- Take ChatGPT into account to provide a practical bridge for engineers and DevOps teams.
Who Should Take This Course:
Job titles: Machine learning engineer, Data Engineer, DevOps teams
Pre-requisites: Familiarity with building ML models in Python, and managing data in AWS S3 buckets. Also, familiarity with Git and GitHub.