
Learn live feature engineering by building a feature store from raw data, converting features via an API, and speeding processing with multiprocessing and AsyncIO, plus feature expansion and unit testing.
Refactor and version control feature store code by pushing to GitHub, then analyze data in BigQuery, visualize in Sheets, and apply Black, Pylint, Mypy, and profiling for MLOps workflows.
Engage in labs on Google Cloud Storage, Compute Engine, and Vertex AI to scale model development with blob storage, remote VMs, and automated feature-store workflows.
Leverage MLflow for model lifecycle management and experiment tracking, and use Spark on Dataproc for horizontal scaling, production deployment, and artifact packaging.
Refactor notebook code into a deployable script and deploy the model with Google Cloud Functions, retrieving the production model from Google Cloud Storage and serving predictions via a post endpoint.
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.