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Expert6 tutorials

Production-Scale ML

For senior ML engineers. Master distributed training, MLOps, custom CUDA kernels, and cutting-edge research implementations.

Prerequisites

  • Completed advanced tutorials
  • Production ML experience (2+ years)
  • Strong software engineering skills
  • Experience with distributed systems
  • GPU programming fundamentals
  • MLOps tool familiarity
  • Research paper reading experience
  • Access to multi-GPU setup
Review Advanced Tutorials

Distributed Training at Scale

Train large models across multiple GPUs and nodes. Data parallelism, model parallelism, and pipeline parallelism strategies.

Data ParallelismModel ParallelismPipeline Parallelism+12 more
150 min15 lessons

MLOps & Infrastructure

Build production ML pipelines with CI/CD, feature stores, model registries, and automated retraining.

ML PipelinesFeature StoresModel Registry+9 more
120 min12 lessons

Custom CUDA Kernels

Write high-performance GPU code for ML. Optimize matrix operations, attention mechanisms, and custom layers.

CUDA BasicsMemory HierarchyThread Synchronization+15 more
180 min18 lessons

Implementing Research Papers

Read, understand, and implement cutting-edge ML research. From paper to production-ready code.

Reading PapersUnderstanding NotationAblation Studies+17 more
200 min20 lessons

Large Language Model Training

Train LLMs from scratch. Tokenization, pre-training, instruction tuning, and RLHF alignment.

TokenizationBPE TrainingPre-training Data+21 more
240 min24 lessons

ML System Design

Design scalable ML systems. Handle billions of predictions, real-time inference, and multi-model architectures.

System RequirementsLatency vs ThroughputBatch vs Real-time+13 more
160 min16 lessons

1.ML Expert Certification

Complete all expert tutorials and pass the assessment to earn your 1.ML Expert certification.

Learn About Certification
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