Supercomputers for training and inference

The Superintelligence
Cloud

Skill Path

AI Model Training & Deployment

Learn the fundamentals of AI model training and deployment, from building GPT models to deploying them at scale.

Includes Neural Networks, Transformer Architecture, Model Optimization, and more.

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2,458 learners enrolled

This skill path includes

AI assistance for guided coding help
Projects to apply new skills
Quizzes to test your knowledge
A certificate of completion

About this skill path

AI model training has become increasingly powerful through the years. In this skill path, you'll learn how to build, train, and deploy transformer models like GPT-2. You'll work through fundamental concepts including tokenization, embeddings, attention mechanisms, and gain the skills necessary to develop and optimize large language models.

Skills you'll gain

Transformer architecture fundamentals
Model training and optimization
Tokenization and embeddings
GPU acceleration and scaling

Syllabus

8 units · 24 lessons · 8 projects · 16 quizzes

1

Introduction to Transformer Models

Welcome to the AI Training & Deployment Skill Path!

2

Tokenization and Embeddings

Learn about token embeddings, positional encodings, and vocabulary.

3

Building GPT Architecture

Implement self-attention, multi-head attention, and transformer blocks.

4

Training Strategies

Learn about optimization techniques, learning rates, and training loops.

5

Model Scaling and Parallelism

Understand GPU utilization, distributed training, and model parallelism.

6

Fine-tuning and Transfer Learning

Learn how to adapt pre-trained models for specific tasks.

7

Model Deployment

Deploy models to production with inference optimization.

8

Advanced Topics

Explore RLHF, prompt engineering, and model evaluation.

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