With ever-growing data, data scientists are required to run more machine learning algorithms and at scale to answer complicated business questions. Machine Learning lifecycles can be complex and require robust processes to keep track of multiple iterations and their results to find the value from data in hand. Without a proper framework, this entire process could be a nightmare and wasted effort.

MLflow is an open-source framework that provides best practices for experimentation, managing environments, and deploying models in your machine learning lifecycle.

What we cover:

• Challenges faced in Machine Learning Lifecycle
• About MLflow
• Benefits of using MLflow
• Demo of MLflow

Is it for you?

• Do you manage machine learning projects?
• Are you looking to scale your machine learning experiments?