Hands-on Machine Learning Engineering & Operations

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Last updated 8/2023
Duration: 8h6m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 44.1kHz, 2ch | Size: 4.32 GB
Genre: eLearning | Language: English



To build and release AI systems at scale with Python, Spark, Airflow, Docker, MLFlow & Google Cloud Platform
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
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
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
Homepage
Screenshots

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