Machine Learning Deep Learning Model Deployment [Free Online Course] - TechCracked

Deploy Machine Learning Model Python Pickle Flask Serverless REST API TensorFlow Serving PyTorch MLOps MLflow

What you'll learn:

• Machine Learning Deep Learning Model Deployment techniques
• Deploying Machine Learning Models on cloud instances
• TensorFlow Serving and extracting weights from PyTorch Models
• Creating Serverless REST API for Machine Learning models
• Machine Learning experiment and deployment using MLflow

Description

In this course you will learn how to deploy Machine Learning Models using various techniques.

Course Structure:

• Creating a Model
• Saving a Model
• Exporting the Model to another environment
• Creating a REST API and using it locally
• Creating a Machine Learning REST API on a Cloud virtual server
• Creating a Serverless Machine Learning REST API using Cloud Functions
• Deploying TensorFlow and Keras models using TensorFlow Serving
• Deploying PyTorch Models
• Creating REST API for Pytorch Models
• Tracking Model training experiments and deployment with MLfLow

Python basics and Machine Learning model building with Scikit-learn will be covered in this course. TensorFlow and Pytorch model building is not covered so you should have prior knowledge in that. Focus of the course is mainly Model deployment.

Also See : Python 3: From ZERO to GUI programming

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