PyTorch
Train and fine-tune models — the ML foundation behind MLOps and many LLM workflows.
Maturity level: L2 — Can Build
Six perspectives on PyTorch
Roadmap
Learn after Python — before MLflow and model serving make sense.
Architecture
Training layer that feeds experiment tracking and deployment pipelines.
Company
Expected for ML engineer and MLOps roles; useful for LLMOps fine-tuning work.
Projects
Train at least one model end-to-end before automating the pipeline.
Interview
Training fundamentals, not just tool names.
Career
Credibility with ML teams — you understand what the pipeline is shipping.
What & Why
What: Deep learning framework for building, training, and exporting neural network models.
Why: MLOps engineers need to understand training loops, checkpoints, and exports — not just pipelines around models others built.
Build this
Train a classifier, track metrics, export to ONNX, and serve via an API.
Production reality
- ! OOM during training
- ! Non-determinism
- ! Checkpoint corruption
- ! Train/serve skew
Interview preparation
- Explain training vs inference graph
- How do you debug NaN losses?
- Fine-tuning vs training from scratch
Connected skills
Explore PyTorch in the interactive universe or train with live cohorts.
Want this PyTorch learning path turned into your own plan?
A roadmap shows the destination. In 1:1 mentorship I turn it into a week-by-week learning path for your background, review your projects, and prepare you for interviews until you land the role. Online, in your time zone.
Start with one 60-minute 1:1 call: .
Prefer a structured live course? Join the MLOps · AIOps · LLMOps · Agentic AI masterclass →