Katonic MLOps Certification Course


Understand the concepts of MLOps, Kubernetes, Docker & learn how to build an E2E use case on Katonic MLOps Platform

What you will learn

Introduction to MLOps

Introduction to Kubernetes & Docker

MLOps Platform Introduction and Walkthrough

Build an End-to-End ML Use Case

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.


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This course covers the following topics:

  1. What is MLOps?
  2. Lifecycle of an ML System
  3. Activities to Productionize a Model
  4. Maturity Levels in MLOps
  5. What is Docker?
  6. What are Containers, Virtual Machines and Pods?
  7. What is Kubernetes?
  8. Working with Namespaces
  9. MLOps Stack Requirements
  10. MLOps Landscape
  11. AI Model Lifecycle
  12. Introduction to Katonic MLOps Platform
  13. End-to-End use case walkthrough
    1. Creating a workspace
    2. Fetching data and working with notebooks.
    3. Building an ML pipeline
    4. Registering & deploying a model
    5. Building an app using Streamlit
    6. Scheduling a pipeline run
    7. Model Monitoring
    8. 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.
English
language

Content

Introduction to MLOps

Introduction to Program
Why MLOps?
Lifecycle of an ML System
Activities to Productionize a Model
What is MLOps?
Maturity Levels in MLOps.mp4

Introduction to Kubernetes & Docker

Instructor Introduction
Why Docker?
What are Containers?
What are Virtual Machines?
What is Docker?
Why Kubernetes?
What are Pods?
Kubernetes Deployment
Working with Namespaces
Walkthrough

MLOps Platform Introduction

MLOps Stack Requirements
MLOps Landscape
Katonic MLOps Platform Introduction
Problem Description & Introduction to Feature Engineering
AI Model Lifecycle
MLOps Platform Overview

End-to-End Use Case Demo

Instructor Introduction
Goals of the End-to-End Demo
Problem Statement Description
AI Model Lifecycle
Use Case Overview
Creating a Workspace
Fetching Data
Notebook Overview
Working with Experiments
Registering a Model
Building an ML Pipeline
Deploy a Model
Building an App using Streamlit
Building an Inference Pipeline
Scheduling a Pipeline Run
Model Monitoring
Retraining a Model

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