Katonic MLOps Certification Course

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

4.05 (188 reviews)
Udemy
platform
English
language
IT Certification
category
Katonic MLOps Certification Course
2,447
students
3 hours
content
May 2022
last update
$54.99
regular price

What you will learn

Introduction to MLOps

Introduction to Kubernetes & Docker

MLOps Platform Introduction and Walkthrough

Build an End-to-End ML Use Case

Why take this course?

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:


  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.


Reviews

Abideen
May 9, 2023
It was good, I understand the concept just that I don't know how or where to run the program. I mean the application I'll use to run it from the laptop.
Shaktiman
April 10, 2023
It was a knowledge and information based course which did not include a hands on task for the students. Good course overall.
Md.
March 1, 2023
Showed full process very clearly. Also give idea that how i can do the full process with other tools .
Manoj
February 27, 2023
It was precise and no beating around the bush kind of session. Hope the upcoming ones are same as well.
Mohammad
November 24, 2022
Good understanding between the Data science team and the Operation team to deploy the project in production
Inuwa
November 18, 2022
The program presents a hands-on experience with MLOps. The details are presented so well by the instructor that learning the complexities of MLOps becomes very easy. I hope to have a great MLOps feature with the Katonic tool.
Aaditya
October 3, 2022
I got to know all the challenges in machine learning models with different phases and cycles. It was quite interesting to know and understand.
Madhan
August 19, 2022
Some of the initial theory sessions were vague and it was interesting when it comes to walk-through, demo. Great tool
VISHAL
August 14, 2022
This was a great course. Especially I liked the various outlining flow-diagrams of the processes and life cycles presented. It gave me a good high-level overview of the entire process of AI/ML model conception and all the way towards deployment & monitoring. All the instructors did good job in the course delivery. Towards the end, the use-case demo, it was totally on the point in taking through various steps involved through the life-cycle of ML Ops. But, I feel we could have gone a bit slower and more detailed at some points, may be because I am new to this I felt so as I come from an enterprise application development, for some one who's already into Data science this level of explanation would be more than sufficient to get going.
Manish
August 12, 2022
I am taking this course as a part of Hackathon. Being a BA I am trying to make myself aware of what is happening in industry.
Manikyalarao
August 11, 2022
I felt like your team can share additional information in few areas ex. step by step process to clone the data from GIT repo, code explanation. Because when I created the same workspace from my end which does not look as similar to the one explained in the course. By providing some more extra inputs in those areas including code explanation for the sample project one you have taken in the course would do better. Moreover, I really appreciate the content creators and the katonic platform for bringing it to us. Thanks!
Anantharaman
August 9, 2022
Good sequencing. right amount of information shared, not overloading, while maintaining all key aspects in overall ML-lifecycle . Thank you.
Ganesh
July 1, 2022
The explanation and practical part was very impressive, where we can get clear idea from start of model building to deployment of ML model.

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4671688
udemy ID
5/4/2022
course created date
6/2/2022
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