What you'll learn
Introduction to MLOps
Introduction to Kubernetes & Docker
MLOps Platform Introduction and Walkthrough
Build an End-to-End ML Use Case
Requirements
Python
Concepts of Machine Learning
Description
Machine Learning Operations (MLOps) provides an end-to-end machine learning development process to design, build and manage reproducible, testable, and evolvable ML-powered software.
It is a set of practices for collaboration and communication between data scientists and operations professionals. Deploying these practices increases the quality, simplifies the management process, and automates the deployment of Machine Learning models in large-scale production environments.
With this course, get introduced to MLOps concepts and best practices for deploying, evaluating, monitoring and operating production ML systems.
This course covers the following topics
What is MLOps?
Lifecycle of an ML System
Activities to Productionize a Model
Maturity Levels in MLOps
What is Docker?
What are Containers, Virtual Machines and Pods?
What is Kubernetes?
Working with Namespaces
MLOps Stack Requirements
MLOps Landscape
AI Model Lifecycle
Introduction to Katonic MLOps Platform
End-to-End use case walkthrough
Creating a workspace
Fetching data and working with notebooks.
Building an ML pipeline
Registering & deploying a model
Building an app using Streamlit
Scheduling a pipeline run
Model Monitoring
Retraining a model
By the end of this course, you will be able to
Understand the concepts of Kubernetes, Docker and MLOps.
Realize the challenges faced in ML model deployments and how MLOps plays a key role in operationalizing AI.
Design an end-to-end ML production system.
Develop a prototype, deploy, monitor and continuously improve a production-sized ML application.
Who this course is for
Data Scientists
Aspiring MLOps Professionals and Enthusiasts
Individuals interested in data and AI industry
https://www.udemy.com/course/katonic-mlops-certification-course/

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