PyTorch Certified Associate (PTCA)

PyTorch Certified Associate (PTCA) 

The PyTorch Certified Associate (PTCA) is designed for early-stage practitioners who are beginning to work with PyTorch and want to validate their foundational knowledge of modern AI and machine learning workflows. The certification demonstrates an understanding of how PyTorch is used to design, train, test and work with machine learning models. It can help candidates build credibility for AI and machine learning roles and demonstrate practical knowledge of one of the most widely used frameworks in modern AI development.

Who should take the PTCA Certification?

PTCA is particularly suitable for:

Students & Recent Graduates

• Aspiring AI/ML Professionals
• Python Developers
• Early-Stage ML Practitioners

• Engineers & Researchers

Skills Required

The PTCA is designed for beginner-level, early-stage practitioners. You do not need advanced PyTorch expertise before taking the exam, and the certification has no formal prerequisites. However, having some foundational skills will make your preparation much easier.

Python Programming

You should have a basic understanding of Python programming, including:

• Variables and data types
• Functions
• Conditional statements and loops
• Basic data structures
• Working with Python libraries
• Reading and understanding basic Python code

Machine Learning Fundamentals

A basic understanding of machine learning is recommended, including:

• What machine learning models are
• Training and testing concepts
• Basic model evaluation
• Understanding datasets and features
• General understanding of neural networks

Basic PyTorch Exposure

You should ideally have some familiarity with PyTorch or be beginning to work with it, including:

• PyTorch tensors
• Basic model workflows
• Training concepts
• Working with datasets
• Basic understanding of devices such as CPU and GPU
• Problem-Solving Skills

Knowledge Gained

By preparing for the PTCA exam, candidates can develop a strong foundation in the core concepts and workflows used when working with PyTorch.

PyTorch Fundamentals

You will develop knowledge of:

• PyTorch tensors
• Training, testing and using models
• Device basics
• CPU, CUDA and MPS environments
• Core PyTorch workflows

Model Development

You will understand:

• PyTorch neural network building blocks
• How models are structured
• Key components involved in developing machine learning models
• The fundamentals of working with PyTorch-based models

Performance & Optimization

You will gain knowledge of:

• Precision and execution optimization
• Measuring model and workflow performance
• Performance considerations when working with PyTorch
• Fundamentals of distributed training

Data Handling

You will understand:

• Datasets
• DataLoaders
• Transforms
• Training data
• How data is prepared and used within PyTorch workflows

PTCA Exam Domains

The PTCA exam covers four major competency areas:

PyTorch Fundamentals (38%)

• Core Concepts
• Tensors
• Training, Testing, and Using Models
• Device Basics (CPU, CUDA, MPS, etc)

Model Development (20%)

• PyTorch Neural Network (NN) Building Blocks

Performance & Optimization (26%)

• Precision and Execution Optimization
• Performance Measurement
• Distributed Training

Data Handling (16%)

• Datasets
• DataLoaders
• Transforms
• Training Data