Practical AI Fundamentals and Deployment

Practical AI Fundamentals and Deployment

Prerequisites:

Basic understanding of programming (preferably in Python), Basic knowledge of machine learning concepts, Familiarity with command line interfaces, Basic Linux knowledge

Course Description:

This course provides a comprehensive introduction to AI fundamentals and hands-on experience in deploying AI products from scratch. Participants will learn AI terminology, set up an operating system, install GPU hardware, and deploy a machine learning model. The course emphasizes practical skills, culminating in the development and deployment of a custom AI solution for a specific company. It's ideal for running your local AI on-premises.

Learning Outcomes:

- Understand key AI concepts and terminology

- Install and configure an operating system for AI applications

- Set up and optimize GPU hardware for AI workloads

- Develop, train, and deploy machine learning models

- Manage data preparation and model training processes

- Implement AI solutions tailored to specific business needs

Ideal For:

- Aspiring data scientists and AI engineers

- IT professionals looking to transition into AI roles

- Tech enthusiasts interested in practical AI deployment

- Business analysts seeking to understand AI applications in business

Course Outline:

- Introduction to AI and Machine Learning

- Overview of AI and ML

- Key terminology and concepts

- Applications of AI in various industries

- Setting Up the Environment

- Installing and configuring the operating system

- Installing and setting up GPU hardware

- Overview of software and tools required

- Data Preparation and Management

- Collecting and preparing data

- Data preprocessing techniques

- Managing datasets for training and validation

- Developing AI Models

- Introduction to model architectures

- Choosing the right model for your problem

- Implementing models using popular frameworks 

- Training and Evaluating Models

- Training models on GPU hardware

- Monitoring training progress

- Evaluating model performance

- Deploying AI Models

- Setting up deployment environments

- Deploying models to production

- Monitoring and maintaining deployed models

- Case Study: Developing a Custom AI Solution

- Defining the business problem

- Developing and deploying a tailored AI solution

- Best practices and lessons learned