MLOps. Machine Learning deployment: AWS, GCP & Apple in 6hrs

 Learn to deploy ML models to multiple environments, Data Engineering best practices and BOOST your career

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What you'll learn

• Productionize Machine Learning Deployment
• Roll out ML Models to Multiple Environments
• Learn MLOps in AWS SageMaker
• Learn to Work in GCP Vertex AI
• Train & Deploy ML Models for Apple Devices
• Create a Solid Case to GET PROMOTED in Your Career
• Ace mlflow + dvc stack
• Data Drifts: discovery strategies & dealing with them

Description

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[Course Updates]:

- 08.2023: + New Extra materials in Chapters #3 & #7

- 10.2023: + "MLOps Market Overview" chapter.

Learn key Market stats, trends + Salaries & Role Expectations

-  11.2023: + "Data Drifts" section (+1 hour of content). Learn to discover data drifts & deal with them.

EvidentlyAI + MLFlow integration

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Would you like to learn best practices of Automation & ML models Deployment?

Maybe you would also like to practice doing it?

You've come to the right place!

There's no better way to achieve that than by creating a strong theoretical foundation and getting hands dirty by applying newly learnt concepts in practice straight away!

MLOps has been helping me automate & roll out robust, easily maintainable and state-of-the-art ML in IT, Food and Travel industries over the last 6 years.

With the help of modern Cloud Computing and open source software I've brought live dozens of ML research projects, successfully solved very complex Business challenges and even changed the country where I live & work!

There are many different technologies powering modern ml ops. Some of them are: AWS Sagemaker, Kubeflow, Azure machine learning, mlflow, GCP Vertex AI, dvc etc. We will cover many of them and see how they work together.

The course will also teach you about Data Drifts: a common issue arising in the world of Machine Learning models. We will learn what these are, how to discover them in a timely manner and what actions to take to mitigate their effect on model's performance.

We will use a variety techniques for that: from simple visual analysis using histograms & box plots all the way to learning EvidentlyAI.

Join me in this fun and Industry-shaped course to get new skills and improve your MLOps & Cloud acumen!

By the end of this course you will be able to:

Set up CI & CD pipelines

Package ML models into Docker

Run AutoML locally & in the Cloud

Train ML models for Apple devices

Monitor and Log ML experiments with mlflow framework

Set up and manage MLOps pipelines in AWS SageMaker

Operate Model Registry & Endpoints in GCP VertexAI

Use EvidentlyAI to discover Data Drifts and conduct advanced analysis of ML model performance

Use EvidentlyAI together with MLFlow to track ML experiments

Boost your Career and MLOps studying efficiency

The course isn't static! I collect students' feedback and periodically update the materials: add new lectures and practice cases!

Who this course is for:

Machine Learning Enthusiasts, Data Scientists, Data Analysts, Developers, AI professionals