ML Ops topped LinkedIns Emerging Jobs ranking, with a recorded growth of 9.8 times in five years.
Most individuals looking to enter the data industry possess machine learning skills. However, most data scientists are unable to put the models they build into production. As a result, companies are now starting to see a gap between models and production. Most machine learning models built in these companies are not usable, as they do not reach the end users hands. ML Ops engineering is a new role that bridges this gap and allows companies to productionize their data science models to get value out of them.
This is a rapidly growing field, as more companies are starting to realize that data scientists alone are not sufficient to get value out of machine learning models. It does not matter how highly accurate a machine learning model is if it is unusable in a production setting.
Most people looking to break into the data industry tend to focus on data science. It is a good idea to shift your focus to ML Ops since it is an equally high paying field that is not highly saturated yet.
Learn ML Ops from the ground up! ML Ops can be described as the techniques for implementing and automating continuous integration, continuous delivery, and continuous training for machine learning systems. As most of you know, the majority of ML models never see life outside of the whiteboard or Jupyter notebook. This course is the first step in changing that!
Take your ML ideas from the whiteboard to production by learning how to deploy ML models to the cloud! This includes learning how to interact with ML models locally, then creating an API (FastAPI and gRPC), containerize (Docker), and then deploy (AWS and GCP). At the end of this course you will have the foundational knowledge to productionize your ML workflows and models.
Course outline:
Introduction
Environment set up
PyTorch model inference
Tensorflow model inference
API introduction
FastAPI
gRPC
Containerize our APIs using Docker
Deploy containers to AWS
Deploy containers to GCP
Conclusion
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SKILLS YOU WILL GAIN
ML Ops introduction
Deploy ML model to AWS and GCP via EC2 and VMs
Use a computer vision model made from PyTorch and Tensorflow frameworks
Make an API utilizing FastAPI
WHAT YOU WILL LEARN
Introduction to gRPC in Python and make your own gRPC API
Docker intro
Take your ML ideas to production
Containerize your ML apps