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AI / ML Engineer in 2026, please learn:
One ML stack deeply:- PyTorch or JAX, not just .fit(), but GPU memory, kernels, mixed precision, profiling, and why your model OOMs at 3am.
Data:- Where it comes from, how it lies, how it drifts, how labels break, how leakage sneaks in, and why 80% of model failures are upstream.
Statistics:- Bias vs variance, confidence intervals, calibration, distribution shift, and why โ95% accuracyโ is often meaningless.
Loss functions:- What you are actually optimizing, how it shapes behavior, and how bad losses silently create bad products.
Evaluation:- Real-world metrics, not Kaggle ones. Offline vs online. Regression tests for models. When numbers lie.
Training:- Distributed GPUs, gradient accumulation, checkpointing, reproducibility, and how to not lose a 3-day run to one crash.
LLMs: Tokenization, attention, context limits, KV cache, LoRA vs fine-tuning vs RAG, and where hallucinations are born.
Inference:- Batching, quantization, vLLM, streaming, cold starts, GPU vs. CPU, and why serving is harder than training.
Retrieval:- Embeddings, chunking, hybrid search, reranking, grounding, and why most RAG systems fail quietly.
Pipelines:- Feature stores, offline vs. online data, backfills, late events, schema evolution, and broken joins.
Monitoring:- Drift, outliers, token spend, latency, hallucination rate, and silent quality decay.
Optimization:- Distillation, pruning, caching, prompt compression, and how to make models affordable.
Agents:- Tool calling, memory, retries, failure modes, and why autonomous systems are chaos engines.
Security:- Prompt injection, data exfiltration, training data leaks, and tool misuse.
Deployment:- Model versioning, shadow runs, canaries, rollbacks, and killing bad models fast.
Distributed systems:- Queues, retries, idempotency, backpressure, and partial failures. ML is just distributed systems with gradients.
Documentation:- Model cards, data contracts, eval reports, and written tradeoffs.
Pick one stack. Build real systems. Break them. Fix them.
If I missed something, Add in the comment section.
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