MLflow in Action – Master the art of MLOps using MLflow tool
MLflow in Action - Master the art of MLOps using MLflow tool
A master guide to unleash the full potential of MLflow to optimize MLOps. Streamline MLOps workflows using MLflow tool
Product Brand: Udemy
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Udemy Coupon Code for MLflow in Action – Master the art of MLOps using MLflow tool Course. A master guide to unleash the full potential of MLflow to optimize MLOps. Streamline MLOps workflows using MLflow tool
Created by J Garg – Real Time Learning | 9.5 hours on-demand video course
MLflow Course Overview
MLflow in Action – Master the art of MLOps using MLflow tool
MLOps is the backbone of modern Machine learning workflows. It solves the pressing problem of operationalizing the ML models in production systems. Pushing the ML models to production which could traditionally take months can now be operationalized in few days using MLOps tools. As per the tech talks in market, 2024 is the year of MLOps and would become the mandate skill for Enterprise ML projects.
Why MLflow tool for MLOps ?
MLflow is the ultimate tool for MLOps as of 2023 because it streamlines the entire machine learning lifecycle. It allows you to efficiently track experiments, package code, register versions and deploy models, all within one unified platform. Unlike other tools, MLflow simplifies the process, enabling you to transition from development to deployment seamlessly.
MLflow’s popularity is evident from the thousands of organizations, ranging from startups to Fortune 500 companies, that have integrated MLflow into their MLOps workflows.
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.
- A range of MLflow logging functions to effectively track and record experiments, runs, artifacts, parameters, code, metrics etc.
- 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.
Recommended MLOps Course
MLOps Bootcamp: Mastering AI Operations for Success – AIOps
MLOps Bootcamp: Mastering AI Operations for Success – AIOps
Welcome to our extensive MLOps Bootcamp (AI Ops Bootcamp), a transformative learning journey designed to equip you with the skills and knowledge essential for success in the dynamic field of Machine Learning Operations (MLOps). This MLOps Bootcamp: Mastering AI Operations for Success – AIOps course program covers a diverse range of topics, from Python and Data Science fundamentals to advanced Machine Learning workflows, Git essentials, Docker for Machine Learning, CI/CD pipelines, and beyond.
Complete Machine Learning,NLP Bootcamp MLOPS & Deployment
Complete Machine Learning,NLP Bootcamp MLOPS & Deployment
Are you looking to master Machine Learning (ML) and Natural Language Processing (NLP) from the ground up? This Complete Machine Learning,NLP Bootcamp MLOPS & Deployment course is designed to take you on a journey from understanding the basics to mastering advanced concepts, all while providing practical insights and hands-on experience.
By the end of this course, you’ll have a comprehensive understanding of machine learning and natural language processing, from the basics to advanced concepts. You’ll be able to apply your knowledge to build real-world projects, and you’ll have the skills needed to pursue a career in ML and NLP.
What’s included in this MLflow course ?
- Understand MLOps basics, limitations of traditional ML lifecycles, how MLOps overcomes those limitations.
- Complete MLflow concepts explained from Scratch to Real-Time implementation.
- Learn in practical the 4 core components of MLflow – Tracking, Model, Project, and Registry.
- Various logging functions in MLflow for precise tracking and recording of experiments, runs, artifacts, parameters, code, metrics, and more.
- Learn to handle customized models using Python in MLflow.
- Learn to interact with MLflow using MLflow library, UI, MLflow Client and CLI commands.
- Learn Best practices and Optimization techniques to follow in Real-Time MLOps/MLflow Projects.
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Taught by J Garg
