【評判】AI Engineering : Model Deployment, MLOps & Agentic AI

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

AI Engineering is one of the fastest-growing fields in technology today.

From deploying Machine Learning models and building scalable AI APIs to developing LLM-powered applications and autonomous AI Agents, organizations across every industry are looking for engineers who can transform AI models into production-ready systems.

In this course, you will learn how to build, deploy, and serve Machine Learning, Deep Learning, and AI applications using a variety of modern techniques. Going beyond model development, the course demonstrates how trained models and AI-powered applications can be deployed and consumed by real-world systems through hands-on projects and practical examples.

The course also introduces modern AI Engineering concepts, including Large Language Models (LLMs), OpenAI SDK, AI Agents, MLOps, AI-assisted development with GitHub Copilot, Vibe Coding, and emerging AI technologies that are shaping the future of software development.

Course Structure

Machine Learning Model Deployment

• Build a Classification Model using Scikit-learn

• Save and Load Machine Learning Models and Standard Scalers

• Export Models across Environments (Local Machine and Google Colab)

• Build a REST API using Python Flask and test it locally

• Deploy a Machine Learning REST API on a Cloud Virtual Machine

• Create a Serverless Machine Learning REST API using Cloud Functions

Deep Learning Model Deployment

• Build and Deploy TensorFlow and Keras Models using TensorFlow Serving

• Build and Deploy PyTorch Models

• Convert PyTorch Models to TensorFlow format using ONNX

• Build REST APIs for TensorFlow and PyTorch Models

• Deploy TF-IDF and Text Classification Models for Twitter Sentiment Analysis

• Deploy Models using TensorFlow.js and JavaScript

MLOps

• Track Model Training Experiments and Deployments using MLflow

• Run MLflow on Google Colab and Databricks

Generative AI and LLM Fundamentals

• OpenAI and the Evolution of GPT Models

• Create an OpenAI Account and Invoke Text-to-Speech Models using Python

• Invoke OpenAI Chat, Text Generation, and Image Generation Models from Python

• Build a Chatbot using the OpenAI API and ChatGPT with Python

• Introduction to Large Language Models (LLMs) and Prompt Engineering

AI-Assisted Development with GitHub Copilot

• Agent Mode Model Development with GitHub Copilot

• Vibe Coding: Build ML Models with a Single Prompt

• Build a REST API for an ML Model using GitHub Copilot

• Build Interactive Machine Learning Web Applications with Copilot Agent Mode

• Build a Serverless Machine Learning API using AWS S3, Lambda, and API Gateway

Building AI Agents with OpenAI SDK

• What is an AI Agent?

• Build Your First AI Agent using the OpenAI SDK

• Build a Tool Calling AI Agent

• Deploy an AI Agent as a REST API using FastAPI

• Build an AI Agent with Web Search

• Build an AI Agent with Memory

• Build a Multi-turn AI Chatbot

• Assignment: Build a Multi-Tool AI Agent with OpenAI SDK

AI Engineering Trends & Emerging Topics

This continuously updated section explores the latest developments in AI Engineering through conceptual lectures and industry insights. Topics include Agentic AI, MCP (Model Context Protocol), Retrieval-Augmented Generation (RAG), AI Coding Assistants, Vibe Coding, AI career trends, AI infrastructure, and other emerging technologies that every AI Engineer should understand.

This course is designed for beginners with no prior experience in Machine Learning or Deep Learning. A basic understanding of Python programming is recommended.

By the end of this course, you will be able to build, deploy, and serve Machine Learning models, Deep Learning models, LLM-powered applications, and AI Agents using Python, FastAPI, TensorFlow, PyTorch, MLflow, OpenAI SDK, GitHub Copilot, and modern cloud deployment techniques.

As the AI landscape evolves, new lectures and emerging topics will continue to be added, ensuring that this course remains an up-to-date resource for AI Engineering, Model Deployment, MLOps, LLM Applications, and Agentic AI.