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
- Agent-Mode Model Building and Deployment with GitHub Copilot
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:
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 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 JavaScript
Tracking Model training experiments and deployment with MLFLow
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
New Section : Agent-Mode Model Building and Deployment with GitHub Copilot
Vibe Coding: Model Development with GitHub Copilot Using a Single Prompt
Building a REST API for ML Model with a Simple Prompt Using GitHub Copilot
Building Interactive ML Web Apps with Copilot in Agent Mode
Creating a Serverless Machine Learning API with AWS S3, Lambda, and API Gateway
This course is designed for beginners with no prior experience in Machine Learning or Deep Learning. A basic background in Python is required.
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.
This course uses high-quality AI-generated text-to-speech narration to complement the powerful visuals and enhance your learning experience.
