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ML Ops | Deployment Specialization - Learning Path

Learn how to deploy models into actual applications and build your own AI product.

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Steps to Learn Deployment Specialization

Step 1

Deploying in EC2 (popular method)

Learn how to convert your ML models to a productionizable app. Host it in AWS EC2 instance performed live and learn all the intermediate steps, purpose behind it and best practices..

20 Lessons

ML Deployment in AWS EC2

Step 2

Deploying in other AWS methods

Learn how to serve machine learning from AWS Lambda and AWS Sagemaker. Undestand their services, set them up and learn how to deploy ML in AWS Lambda and AWS Sagemaker.

10 Lessons

Deploy ML Models in AWS Lamda

30 Lessons

Deploy ML Models in AWS Sagemaker

Step 3

Handle Big Data using PySpark

Learn to leverage distributed computing power for large-scale data processing, manipulate datasets efficiently, and harness advanced analytics techniques to derive meaningful insights.

24 Lessons

PySpark for Data Science - V: ML Pipelines

Step 4

Deployment Optimization Techniques

Learn to streamline your machine learning workflows, track models effectively, and optimize deployment strategies for seamless integration into production environments.

13 Lessons

MLFlow in Action: Hands on guide to ML experiments

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