@suraj_sharma14: If you want to become an AI/ML Engineer, here's what you actually need to learn: - Math & theory foundations : Linear a…

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Summary

A detailed roadmap of topics to learn for becoming an AI/ML engineer, covering math fundamentals, deep learning architectures, training techniques, data pipelines, evaluation, inference, MLOps, and responsible AI.

If you want to become an AI/ML Engineer, here's what you actually need to learn: - Math & theory foundations : Linear algebra, probability & statistics, optimization, information theory - Deep learning architectures : Transformers, diffusion models, CNNs/ViTs, RNNs/LSTMs/GRUs, mixture-of-experts - Training & scaling : Pre-training, fine-tuning, PEFT (LoRA/QLoRA), RLHF/DPO/PPO, distributed training (DDP, FSDP, DeepSpeed, Megatron) - Data at scale : Data pipelines, cleaning & deduplication, tokenization strategies, synthetic data, multimodal datasets - Evaluation & benchmarking : Automatic metrics (BLEU/ROUGE/BERTScore/MMLU), human eval, adversarial testing, bias & fairness checks, LLM-as-a-judge - Inference & serving : vLLM, TensorRT-LLM, quantization (AWQ/GPTQ/bitsandbytes), speculative decoding, continuous batching, model sharding - MLOps : Experiment tracking (MLflow, W&B), model registry, CI/CD for models, drift monitoring, feature stores - Responsible AI & safety : Alignment techniques, red-teaming, jailbreak defenses, constitutional AI, interpretability basics (attention viz, SAEs) This isn't a weekend list. It's a multi-year stack. Pick one section and go deep before moving to the next. (Bookmark & Repost)
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Cached at: 06/26/26, 04:05 AM

If you want to become an AI/ML Engineer, here’s what you actually need to learn:

  • Math & theory foundations : Linear algebra, probability & statistics, optimization, information theory

  • Deep learning architectures : Transformers, diffusion models, CNNs/ViTs, RNNs/LSTMs/GRUs, mixture-of-experts

  • Training & scaling : Pre-training, fine-tuning, PEFT (LoRA/QLoRA), RLHF/DPO/PPO, distributed training (DDP, FSDP, DeepSpeed, Megatron)

  • Data at scale : Data pipelines, cleaning & deduplication, tokenization strategies, synthetic data, multimodal datasets

  • Evaluation & benchmarking : Automatic metrics (BLEU/ROUGE/BERTScore/MMLU), human eval, adversarial testing, bias & fairness checks, LLM-as-a-judge

  • Inference & serving : vLLM, TensorRT-LLM, quantization (AWQ/GPTQ/bitsandbytes), speculative decoding, continuous batching, model sharding

  • MLOps : Experiment tracking (MLflow, W&B), model registry, CI/CD for models, drift monitoring, feature stores

  • Responsible AI & safety : Alignment techniques, red-teaming, jailbreak defenses, constitutional AI, interpretability basics (attention viz, SAEs)

This isn’t a weekend list. It’s a multi-year stack. Pick one section and go deep before moving to the next.

(Bookmark & Repost)

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