Building Machine Learning Pipelines in Python with Scikit-learn

Free Live ML Workshop #6 on Dec 3 - Register Now

dotsdots

MLOps for Scaling TinyML

Description

Are you ready to scale your (tiny) machine learning application? Do you have the infrastructure in place to grow? Do you know what resources you need to take your product from a proof-of-concept algorithm on a device to a substantial business?

This course introduces learners to Machine Learning Operations (MLOps) through the lens of TinyML (Tiny Machine Learning). Learners explore best practices to deploy, monitor, and maintain (tiny) Machine Learning models in production at scale.Read more.

This resource is offered by an affiliate partner. If you pay for training, we may earn a commission to support this site.

Career Relevance by Data Role

The techniques and tools covered in MLOps for Scaling TinyML are most similar to the requirements found in Data Scientist job advertisements.

Similarity Scores (Out of 100)