Machine Learning Deep Learning Model Deployment

About Machine Learning Deep Learning Model Deployment

In this course you will learn how to deploy Machine Learning Deep Learning Models using various techniques. This course takes you beyond model development and explains how the model can be consumed by different applications with hands-on examples

Course Structure:

1. Creating a Classification Model using Scikit-learn 2. Saving the Model and the standard Scaler 3. Exporting the Model to another environment - Local and Google Colab 4. Creating a REST API using Python Flask 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. Building and Deploying TensorFlow and Keras models using TensorFlow Serving 8. Building and 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 14. Running MLFlow on Colab and Databricks Appendix - Generative AI - Miscellaneous Topics.

OpenAI and the history of GPT models

Creating an OpenAI account and invoking a text-to-speech model from Python code

Invoking OpenAI Chat Completion, Text Generation, Image Generation models from Python code

Creating a Chatbot with OpenAI API and ChatGPT Model using Python on Google Colab

ChatGPT, Large Language Models (LLM) and prompt engineering Python basics and Machine Learning model building with Scikit-learn will be covered in this course. This course is designed for beginners with no prior experience in Machine Learning and Deep Learning 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.