Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Hands-On Machine Learning Engineering & Operations
Rating: 4.5 out of 5(33 ratings)
384 students
Created bySean Ariel
Last updated 8/2023
English

What you'll learn

  • Gain exposure to the real-world productization process of ML systems through a practical, E2E use case
  • Tackle MLOps' latest theories and get battle-tested insights into its main concepts and ideas
  • Navigate the field more effectively and apply the course learnings towards the development of your own project
  • Build on top of the latest technological stack and deploy your solution at scale

Course content

7 sections20 lectures8h 5m total length
  • Hands-On MLE & MLOPS Trailer4:42
  • Theory - Fundamentals, Pipelines, High & Low level solution Blueprints15:03
  • Live Coding - Practical BM use case, Exploratory Analytics, Resampling23:57

Requirements

  • Foundational knowledge in Data Science: data manipulation with Pandas & Numpy, modeling with Scikit-Learn
  • Foundational knowledge of Python: data structures, control flows, basic OOP
  • Nice to have: understanding of command line, Google Cloud Platform, version control

Description

Transform your PoCs & small projects into scalable AI Systems

You love to kickstart projects, but you always get stuck in the same development stage: a functional notebook - with a promising solution - that no one can access yet. The code is messy; refactoring & deploying the model seems daunting.

So you rummage online and crunch through Medium tutorials to learn about Machine Learning Engineering - but you haven't been able to glue all of the information together.

When it comes to making decisions between technologies and development paths, you get lost. You can't get other developers excited about your project.

Time to learn about MLE & MLOPS.

This training will aim to solve this by taking you through the design and engineering of an end-to-end Machine Learning project on top of the latest Cloud Platform technologies. It will cover a wide variety of concepts, structured in a way that allows you to understand the field step by step.

You'll get access to intuitive Lectures, Live Coding & Guided Labs to solve a practical use case that will serve as an example you can use for any of your future projects. By the end of the course, you should be more confident in your abilities to write efficient code at scale, deploy your models outside of your local environment, an design solutions iteratively.

Who this course is for:

  • Data Scientists - who want to deploy their models and build scalable AI systems
  • Software & Data Engineers - who want to transition toward Machine Learning
  • Data Analysts - who want a practical glimpse into Data Science & Engineering