Machine Learning Deep Learning Model Deployment

In this course you will learn how to deploy Machine Learning Deep Learning Models using various techniques.&nbsp; This course takes you beyond model development and explains how the model can be consumed by different applications with hands-on examples Course Structure:Creating a Classification Model using Scikit-Learn Saving the Model and the standard Scaler Exporting the Model to another environment - Local and Google Colab Creating a REST API using Python Flask 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 Building and Deploying TensorFlow and Keras models using TensorFlow Serving Building and Deploying&nbsp; PyTorch Models Converting a PyTorch model to TensorFlow format using ONNX Creating REST API for PyTorch and TensorFlow Models Deploying tf-idf and text classifier models for Twitter sentiment analysis Deploying models using TensorFlow.js and Java Script Tracking Model training experiments and deployment with MLF Low Running ML Flow 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 New Section : Agent-Mode Model Building and Deployment with Git Hub Copilot Vibe Coding: Model Development with Git Hub Copilot Using a Single Prompt<li