Machine Learning Deep Learning model deployment

Serving TensorFlow Keras PyTorch Python model Flask Serverless REST API MLOps MLflow Cloud GCP NLP tensorflow.js deploy

What you'll learn:

• Machine Learning Deep Learning Model Deployment techniques
• Simple Model building with Scikit-Learn , TensorFlow and PyTorch
• Deploying Machine Learning Models on cloud instances
• TensorFlow Serving and extracting weights from PyTorch Models
• Creating Serverless REST API for Machine Learning models
• Deploying tf-idf and text classifier models for Twitter sentiment analysis
• Deploying models using TensorFlow js and JavaScript
• Machine Learning experiment and deployment using MLflow

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

Course Structure:

1. Creating a Model

2. Saving a Model

3. Exporting the Model to another environment

4. Creating a REST API and using it locally

5. Creating a Machine Learning REST API on a Cloud virtual server

6. Creating a Serverless Machine Learning REST API using Cloud Functions

7. Deploying TensorFlow and Keras models using TensorFlow Serving

8. Deploying PyTorch Models

9. Converting a PyTorch model to TensorFlow format using ONNX

10. Creating REST API for Pytorch and TensorFlow Models

11. Deploying tf-idf and text classifier models for Twitter sentiment analysis

12. Deploying models using TensorFlow.js and JavaScript

13. Tracking Model training experiments and deployment with MLfLow

Python basics and Machine Learning model building with Scikit-learn will be covered in this course. You will also learn how to build and deploy a Neural Network using TensorFlow Keras and PyTorch. Google Cloud (GCP) free trial account is required to try out some of the labs designed for cloud environment.