MLOps is a core function of Machine Learning engineering, focused on streamlining the process of taking machine learning models to production, and then maintaining and monitoring them. MLOps is a collaborative function, often comprising data scientists, devops engineers, and IT.
Major Phases — what it takes to master MLOps
- Framing ML problems from business objectives. …
- Architect ML and data solutions for the problem. …
- Data preparation and processing — part of data engineering. …
- Model training and experimentation — data science. …
- Building and automating ML pipelines.
Here are some of the technical skills required to become an MLOps engineer:
- Ability to design and implement cloud solutions (AWS, Azure, or GCP)
- Experience with Docker and Kubernetes.
- Ability to build MLOps pipelines.
- Good understanding of Linux.
- Knowledge of frameworks such as Keras, PyTorch, Tensorflow.
MLOps Certified Professional (MLOCP) certification program is designed for meeting a challenges of MLOps in the project. Certified professional would be able to implement MLOps in the project easily.
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I really enjoyed reading this announcement about the MLOps Certified Professional (MLOCP) program. It clearly explains what MLOps is and why it matters in the world of machine learning. MLOps helps bridge the gap between building models and taking them into real production, which is often one of the hardest parts of working with machine learning. The blog nicely lists the skills and steps needed to become an MLOps engineer, such as cloud tools like AWS, Docker, Kubernetes, and pipeline automation. This certification seems to be designed for people who want practical knowledge and the ability to streamline ML workflows from start to finish. Overall, it’s a helpful and motivating introduction for anyone thinking about moving into this growing field of MLOps.
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