Machine Learning Deep Learning Model Deployment

🌟 Course Title: Machine Learning & Deep Learning Model Deployment with Python, Flask, Serverless REST API, MLOps, MLflow & NLP

🚀 Course Headline:

Deploy Your TensorFlow Keras PyTorch Models into Real-World Applications!

Unlock the Secrets of Model Serving and Bring Your AI to Life! In this comprehensive course, you'll dive deep into the world of Machine Learning (ML) and Deep Learning (DL) model deployment. This isn't just about the math behind models; it's about making them work for real-world applications with practical, 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 🧪✨

Prerequisites & Target Audience:

This course is tailored for beginners with no prior experience in Machine Learning and Deep Learning. We'll cover Python basics and ML model building with Scikit-learn, making this an ideal starting point for those interested in entering the field of AI. A Google Cloud (GCP) free trial account will be required to try out some of the labs designed for a cloud environment.

What You'll Learn:

• How to build and deploy machine learning models using Python, TensorFlow Keras, and PyTorch.
• The process of creating REST APIs to serve your models for various applications.
• The fundamentals of MLOps and MLflow for tracking and managing the lifecycle of ML projects.
• The basics of Natural Language Processing (NLP) with real-world examples, such as sentiment analysis on Twitter.
• How to deploy machine learning models using TensorFlow.js and JavaScript, opening up a world of web and mobile applications.
• The cutting-edge technologies in Generative AI with OpenAI, including text, image generation, and more.

Embark on your journey to becoming an ML expert today! Deploy your models into the real world and see your AI come alive. 🚀