Hands-On Machine Learning Engineering & Operations
To build and release AI systems at scale with Python, Spark, Airflow, Docker, MLFlow & Google Cloud Platform
Ratings 4.57 / 5.00
What You Will 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
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 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 Should Attend!
- 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