MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
A master guide to unleash the full potential of MLflow to optimize MLOps. Streamline MLOps workflows using MLflow tool
What you’ll learn
Explore the fundamentals of MLOps and how it overcomes the challenges inherent in the traditional ML lifecycle.
Gain a deep understanding of MLflow and the role of its 4 components in managing the end-to-end Machine learning operations (MLOps).
Learn how to efficiently Track experiments, Package code, Register and reproduce models in the realm of MLOps using MLflow tool.
MLflow Tracking – To log, organize, and compare Machine learning experiments effortlessly.
MLflow Model – For efficient model packaging into distinct flavors allowing to streamline model deployment and integration into production systems.
MLflow Project – To create structured, reproducible, and easily shareable Machine Learning workflows.
MLflow Registry – For efficient model management, version tracking in order to maintain model quality and performance over time.
A complete end-to-end ML project demonstrating MLflow integration with AWS cloud.
Build, Train, Test and Deploy a Machine learning model in AWS cloud using AWS Sagemaker and MLflow.
Requirements
Basic Machine Leaning knowledge
Basic Python knowledge
Description
Who this course is for:
Data Scientists,Machine Learning Engineers,MLOps Engineers,Operations Engineers
For More Courses Visit & Bookmark Your Preferred Language Blog
From Here: – – – – – – – –
