After you've applied and been accepted to the next cohort, copy and paste this email template below to send to your manager for reimbursement for the course. Feel free to add any additional details as you see fit.
Subject: Reimbursement for Made With ML's MLOps Course
Hope you're doing well. I was recently accepted into Made With ML's MLOps course, which is an interactive project-based course on MLOps fundamentals. The course costs $1,250 but the value I'll gain for myself and our team/company will pay for the course almost immediately. I added some key information about the course below and would love to get this career development opportunity reimbursed.
Course page: https://madewithml.com/
What is the course?
An interactive project-based course to learn and apply the fundamentals of MLOps. I'll be learning to combine machine learning with software engineering best practices which I want to extend to build and improve our own systems. This course brings all of the MLOps best practices into one place, allowing me to quickly (and properly) learn it. And best of all, the course can be done before and after work, so it won't be interfering during work hours.
Here's a quick look at the curriculum:
Who's teaching the course?
The course is from Made With ML, one of the top ML repositories on GitHub (30K+ stars) with a growing community (30K+) and is a highly recommended resource used by industry. Their content not only covers MLOps concepts but they go deep into actually implementing everything with production quality code.
How will this help me?
I'll be learning the foundation I need to responsibly develop ML systems. This includes producing clean, production-grade code, testing my work, understanding MLOps (experiment management, monitoring, systems design, etc.) and data engineering (data stack, orchestration, feature stores) concepts.
How will this help our company?
What I learn will directly translate to better quality ML systems in our products. I'll also be able to engage in conversations with peers and management as we traverse this space to build what's right for us. And, most important of all, I'll be able to pass on what I learn as I collaborate with others in our team so we're all working towards building reliable ML systems.