Mastering Mlops: From Development To Deployment

[oaxino]

Mastering Mlops: From Development To Deployment
Published 4/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 234.23 MB | Duration: 0h 36m

Strategies and Best Practices for Deploying Machine Learning Models at Scale

What you'll learn
Understand the principles of MLOps
Learn how to deploy machine learning models in production
Gain practical experience with MLOps tools and technologies
Develop best practices for managing machine learning models in production
Requirements
Whether you're a seasoned data scientist or a beginner in the field, this course will provide you with the skills and knowledge you need to succeed in the rapidly evolving world of machine learning.
Description
The field of Machine Learning Operations (MLOps) is rapidly gaining importance as more and more organizations seek to deploy and manage machine learning models at scale. This comprehensive course is designed to provide learners with the skills and knowledge they need to successfully manage machine learning models in production environments.Through a combination of lectures, case studies, and hands-on exercises, learners will gain an in-depth understanding of the principles of MLOps, as well as the tools and techniques used in the field. The course covers the entire lifecycle of MLOps, from developing machine learning models to deploying them in production environments.In this course, learners will:Learn about the principles of MLOps, including collaboration between data scientists and IT operations teams, continuous integration and deployment, and monitoring and maintenance of machine learning models in production.Gain hands-on experience with MLOps tools and technologies, including Docker and Kubernetes.Learn how to deploy machine learning models in production environments, including setting up infrastructure, building pipelines, and ensuring security and compliance.Develop best practices for managing machine learning models in production, including monitoring and maintenance, as well as strategies for optimizing performance and reducing costs.Explore real-world case studies and examples, and learn from industry experts who have successfully implemented MLOps in their organizations.By the end of this course, learners will be able to confidently manage machine learning models in production environments and will have the skills and knowledge they need to be successful in the rapidly growing field of MLOps.
Overview
Section 1: Introduction
Lecture 1 Course Features
Lecture 2 Course Overview
Lecture 3 Use-case of MLOps
Lecture 4 Steps of an ML project
Lecture 5 Key Challenges in MLOps
Lecture 6 Deployment Patterns
Lecture 7 Monitoring in MLOps
Lecture 8 Pipeline Monitoring
Python Programmers,Data Scientists

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