Okay real talk — if you've googled "how to become an AI/ML engineer" you've drowned in roadmap images that are just... a wall of logos. TensorFlow logo, AWS logo, Docker logo, arrows everywhere, zero actual explanation of what to do with any of it.
So here's an actual phase-by-phase breakdown, with checkpoints, so you know when to move on instead of doom-scrolling more "10 skills you need" listicles.
Not a whole degree. Just:
Checkpoint: Build linear regression from scratch in NumPy. No sklearn. Yes it's annoying. Do it anyway.
Pandas + SQL + feature engineering. This is 80% of the actual job and 5% of what tutorials cover. SQL specifically will show up in interviews and catch people off guard constantly.
Checkpoint: Full EDA project on a real (messy) dataset, pushed to GitHub with a real README.
Regression → trees → random forest → gradient boosting (XGBoost, everyone uses this) → SVM → clustering.
Metrics matter more than the algorithm honestly. Accuracy is basically a trap metric for imbalanced data and a shocking number of people don't know that.
Checkpoint: 2-3 full projects w/ a writeup of why you picked the metrics you did.
Neural net basics → pick PyTorch or TF (I'd say PyTorch) → CNNs for vision → Transformers for basically everything text-related now, including the RAG/LLM stuff that's all anyone's hiring for currently.
Checkpoint: Fine-tune a pretrained model, wrap it in a FastAPI endpoint.
Pick ONE:
Learn IAM, storage, Docker, basic Kubernetes, managed training, model registries, endpoints. These concepts transfer no matter which cloud you pick.
Checkpoint: Deploy a model you already built. Full loop: train → register → serve.
This is the "actual engineer" phase. Experiment tracking (MLflow), pipelines (Airflow), CI/CD (GitHub Actions), monitoring for drift.
Checkpoint: One full pipeline start to finish, automated, monitored.
Stop building 20 tutorial projects that all look the same. Build 3-5 real ones and take them all the way to actually deployed + monitored + documented. That's the difference between a portfolio that gets ignored and one that gets you an interview.
Drop a comment — what phase are you stuck on?