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UdemyMachine LearningPaid courseIntermediate levelCertificate

ML Ops: Beginner (Udemy.com)

ML Ops | Serve ML models in production | AWS | GCP | FastAPI | gRPC | Docker | Tensorflow | Keras | PyTorch

3.9
35 reviews

Created by: Mark Dabler

Last updated April 2022

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What you will learn

  • ML Ops introduction
  • Deploy ML model to AWS & GCP via EC2 and VMs
  • Use a computer vision model made from PyTorch and Tensorflow frameworks
  • Make an API utilizing FastAPI
  • Introduction to gRPC in Python and make your own gRPC API
  • Docker intro
  • Take your ML ideas to production
  • Containerize your ML apps

How it compares

Against the other 155 rated Udemy courses in Machine Learning on CourseDuck:

  • Rating3.9 out of 5higher than 13% of them
  • Reviews35#143 of 156 by number of reviews
  • Length3.6 hoursshorter than 74% of them
  • Last updatedApril 2022earlier than 56% of them

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Course Description

ML Ops topped LinkedIn’s Emerging Jobs ranking, with a recorded growth of 9.8 times in five years.

Most individuals looking to enter the data industry possess machine learning skills. However, most data scientists are unable to put the models they build into production. As a result, companies are now starting to see a gap between models and production. Most machine learning models built in these companies are not usable, as they do not reach the end-user’s hands. ML Ops engineering is a new role that bridges this gap and allows companies to productionize their data science models to get value out of them.

This is a rapidly growing field, as more companies are starting to realize that data scientists alone aren’t sufficient to get value out of machine learning models. It doesn’t matter how highly accurate a machine learning model is if it is unusable in a production setting.

Most people looking to break into the data industry tend to focus on data science. It is a good idea to shift your focus to ML Ops since it is an equally high-paying field that isn’t highly saturated yet.

Learn ML Ops from the ground up! ML Ops can be described as the techniques for implementing and automating continuous integration, continuous delivery, and continuous training for machine learning systems. As most of you know, the majority of ML models never see life outside of the whiteboard or Jupyter notebook. This course is the first step in changing that!

Take your ML ideas from the whiteboard to production by learning how to deploy ML models to the cloud! This includes learning how to interact with ML models locally, then creating an API (FastAPI & gRPC), containerize (Docker), and then deploy (AWS & GCP). At the end of this course you will have the foundational knowledge to productionize your ML workflows and models.

Course outline:

  1. Introduction

  2. Environment set up

  3. PyTorch model inference

  4. Tensorflow model inference

  5. API introduction

  6. FastAPI

  7. gRPC

  8. Containerize our APIs using Docker

  9. Deploy containers to AWS

  10. Deploy containers to GCP

  11. Conclusion
Read More Intermediate Intermediate Level product length iconTime: 3.6h

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Instructor Details

Mark Dabler

Fascinated with cutting edge tech! I love bridging computing with meaningful industry and research agendas. My personal passion in tech is AI & ML. The first courses I’m putting up on Udemy are ML Ops related. I'm super excited to teach on Udemy after being a student here for so long. Hope you enjoy the courses!

ML Ops | Tensorflow | PyTorch | GCP | AWS | FastAPI | gRPC | Python

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Reviews

3.9

35 ratings on Udemy

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By Ajith Joseph on 2/19/2025

Audio level bit low

By Avinash P. Kambil on 8/25/2024

Lecture terminates unexpectedly.

By Tommy Eastman on 9/26/2023

i'm not sure where the images are that he mentioned? i downloaded the course files zip but i don't see the images in the repo

By Affan Ahmed on 3/5/2023

Bad image qulaitz in cloud. Unclear cloud permissions, and a lot of skips duirng cloud lessons.

By Chandra Sekhar Vorugunti on 2/9/2023

Nice and clear explanation.

By Dr Yasin BOUANANI on 1/30/2023

well explained , thanks

By Bram Kooiman on 5/16/2022

It's nice how the instructor walks you through the steps in a realistic pace. I found it very helpful! That said there's some things to improve. The voice was too soft, so I end up putting my volume louder, which makes any windows notification sound startle you. The screen is not very well readable, which is important if we're going to be reading along with the commands of the instructor. I appreciate how the instructor goes about the operations the way an engineer would, leaving in all the 'oopsies' and how to fix them, that's what I need as a beginner seeking out introductory courses. However, In the final video - on GCP - there's some holes in the video. I suspect the instructor cut some bits out to keep the video at a reasonable duration. At 13:39 there's an issue that says 'permission denied', and the next second it's just magically fixed. While a limited video duration is nice, I think it's precisely the 'how do I deal with this unexpected problem' way of working that I'm looking for as a total beginner. Don't cut out the solution please. This one was fixable though, run `gcloud auth configure-docker europe-west4-docker.pkg.dev` in the VM cmd. Similar problem at 15:44, there's no disk space left on the device. The instructor appears to do nothing about it, but at 16:12 the problem is magically fixed. What did he do? If he secretly spun up a new instance with a bigger storage unit, a better video would have 'instructor from the future' give a comment at the first time an instance is launched, commenting that it's important to select sufficient storage for this or that reason.

By Ali Raza on 4/20/2022

The video quality was pretty bad. The aws and gcp part was very fast. Overall it was ok,

By Awadelrahman Mohamedelsadig Ali Ahmed on 4/15/2022

Next time you could have enhanced the audio quality a bit, voice is too low! Otherwise the course looks a 5 star :)

By Fanmon Mei on 4/7/2022

This was a very helpful course for me to evaluate my knowledge. The instructor was very well spoken in this course. He did a really good job of presenting the content as well as providing great explanations to understand the content.

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Overall Score : 78 / 100

ML Ops: Beginner
Visit course on Udemy Write a review
  • Video Duration: icon 3.6h
  • Price: $54.99
  • Provider: icon Udemy
  • Updated: icon April 2022
  • Level: Intermediate Intermediate
  • Language: icon English
  • Certificate: icon Yes
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