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 examplesCourse Structure:Creating a Classification Model using Scikit-learnSaving the Model and the standard Scaler Exporting the Model to another environment - Local and Google ColabCreating a REST API using Python Flask and using it locallyCreating a Machine Learning REST API on a Cloud virtual serverCreating a Serverless Machine Learning REST API using Cloud FunctionsBuilding and Deploying TensorFlow and Keras models using TensorFlow ServingBuilding and Deploying PyTorch ModelsConverting a PyTorch model to TensorFlow format using ONNXCreating REST API for Pytorch and TensorFlow ModelsDeploying tf-idf and text classifier models for Twitter sentiment analysisDeploying models using TensorFlow. js and JavaScriptTracking Model training experiments and deployment with MLFLowRunning MLFlow on Colab and DatabricksAppendix - Generative AI - Miscellaneous Topics.

OpenAI and the history of GPT modelsCreating an OpenAI account and invoking a text-to-speech model from Python codeInvoking OpenAI Chat Completion, Text Generation, Image Generation models from Python codeCreating a Chatbot with OpenAI API and ChatGPT Model using Python on Google ColabChatGPT, Large Language Models (LLM) and prompt engineeringPython 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 LearningYou 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.