Machine Learning Deep Learning Model Deployment


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.

Who this course is for:

  • Machine Learning beginners


  • Prior Machine Learning and Deep Learning background required but not a must have as we are covering Model building process also

Last Updated 12/2020

Download Links

Direct Download

Machine Learning Deep Learning Model (1.5 GB) | Mirror

Torrent Download

Machine Learning Deep Learning Model Deployment.torrent (91 KB) | Mirror

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