Visão Geral

O curso Mojo para Engenharia de IA/ML foi desenvolvido para capacitar engenheiros de Inteligência Artificial e Machine Learning a construir pipelines, algoritmos e sistemas de IA altamente performáticos, utilizando a linguagem Mojo.

O curso aborda o uso do Mojo como substituto e complemento ao Python em cenários críticos de performance, explorando computação numérica intensiva, paralelismo, SIMD, controle de memória e integração com frameworks de IA, permitindo extrair o máximo do hardware moderno para workloads de Machine Learning, Deep Learning e Data Science em larga escala.

Objetivo

Após realizar este curso Mojo para Engenharia de IA/ML, você será capaz de:

• Utilizar Mojo para acelerar workloads de IA e Machine Learning
• Implementar código numérico altamente otimizado para treinamento e inferência
• Aplicar paralelismo e SIMD em pipelines de IA
• Controlar memória de forma eficiente em aplicações de ML
• Integrar Mojo com projetos Python e frameworks de IA
• Desenvolver sistemas de IA escaláveis, seguros e de alto desempenho

Publico Alvo

•  
• Engenheiros de IA (Artificial Intelligence Engineers)
• Engenheiros de Machine Learning
• Cientistas de Dados
• Desenvolvedores Python focados em IA
• Engenheiros de Software atuando com IA/ML
• Profissionais que trabalham com modelos de alto desempenho
•  

Pre-Requisitos

•  
• Conhecimentos sólidos em Python
• Fundamentos de Machine Learning
• Noções de álgebra linear e estatística
• Experiência básica com bibliotecas de IA (NumPy, Pandas, PyTorch ou similares)
•  

Materiais

Conteúdo Programatico

Module 1 – Mojo for AI/ML Engineers – Foundations

1. Introduction to Mojo for AI workloads
2. Why Mojo for Machine Learning performance
3. Mojo vs Python in AI pipelines
4. Overview of Mojo execution model
5. Use cases in ML and Deep Learning

Module 2 – Numeric Computing with Mojo

1. Numeric types and precision
2. Vector and matrix representations
3. Efficient array operations
4. Memory layout for numerical data
5. Performance considerations

Module 3 – Data Processing Pipelines

1. High-performance data ingestion
2. Batch processing optimization
3. Feature engineering with Mojo
4. Data transformation pipelines
5. Zero-copy data handling

Module 4 – Memory Management for ML

1. Stack vs heap in AI workloads
2. Buffer reuse strategies
3. Memory ownership for tensors
4. Avoiding unnecessary allocations
5. Memory optimization patterns

Module 5 – Parallel Programming for ML

1. Data parallelism strategies
2. Task parallelism in ML pipelines
3. Parallel preprocessing
4. Scaling across CPU cores
5. Safe parallel execution

Module 6 – SIMD for Machine Learning

1. Vectorized numerical operations
2. SIMD acceleration for tensors
3. Loop vectorization techniques
4. Reducing branch divergence
5. SIMD performance tuning

Module 7 – Mojo for Model Training

1. Accelerating training loops
2. Optimizing loss calculations
3. Gradient computation optimization
4. Batch and mini-batch processing
5. Training performance benchmarks

Module 8 – Mojo for Model Inference

1. Low-latency inference pipelines
2. Batch vs real-time inference
3. Memory-efficient inference
4. Throughput optimization
5. CPU-based inference acceleration

Module 9 – Integration with Python and ML Frameworks

1. Calling Mojo from Python
2. Hybrid Python + Mojo architectures
3. Using Mojo inside existing ML projects
4. Gradual migration strategies
5. Best practices for interoperability

Module 10 – Performance Benchmarking

1. Measuring training performance
2. Measuring inference latency
3. Python vs Mojo benchmarks
4. Profiling CPU and memory usage
5. Interpreting performance results

Module 11 – Real-World AI Use Cases

1. Large-scale data processing
2. Classical ML optimization
3. Deep Learning preprocessing
4. AI pipelines at scale
5. Industry case studies

Module 12 – Final Project

1. End-to-end ML pipeline in Python
2. Identification of performance bottlenecks
3. Partial rewrite using Mojo
4. Parallel and SIMD optimization
5. Final benchmark and technical report