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Deep Learning Systems: Technical Brief for Engineers & Agents

Representation learning, discriminative vs generative paths, metrics, and buy/build routing — AWS concepts rewritten for production teams

  • Balinder Walia
  • 1 min read
  • Updated
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Technical digest: deep learning as multi-layer neural optimisation, discriminative vs generative paths, and how to route work to classic ML, trained DL, or foundation-model APIs. Full equations, figures, and agent checklist in the long article.

Deep learning systems technical brief — train, optimise, serve

Definition. Deep learning is gradient-based training of deep neural nets so f_θ(x) learns hierarchical features from high-dimensional x. It underpins modern vision/ASR/NLP and is the substrate of generative AI (transformers / diffusion). Cost drivers: clean data volume, accelerator time, and inference OpEx — not slideware.
Agent digest. DL ⊂ ML. Discriminative = p(y|x). Generative = sample x or x|c. Prefer GBM for strong tabular features; train DL for perception; use Bedrock/vLLM for language agents. GitOps serving; gate money/auth/prod.

Adapted from AWS — What is Deep Learning? — for TenthMatrix engineering teams.

Capability split

ML vs deep learning vs generative AI stack

  • Classic ML — engineered features; trees/linear models; best default for dense tabular SLAs.
  • Deep learning — end-to-end nets on images/text/audio; backprop + GPUs; serve via Triton/KServe/SageMaker.
  • Generative AI — sample new content; LLMs/diffusion + RAG/tools; Bedrock API or open-weight inference.

Task families (metrics matter)

Family Typical objective / metric
Visiondetect/classify — mAP, IoU, p99 latency
SpeechASR/TTS — WER, real-time factor
NLPclassify / generate — F1, exact-match, groundedness
Recommendrank — NDCG, CTR/CVR
Gen AI agentstask success + safety + $/request

Constraints

  • Data — label noise and shift dominate; version datasets.
  • Compute — training and LLM decode are accelerator-bound.
  • Ops — eval harness, drift, cost, GitOps rollback before wide promote.

Train vs buy vs hybrid decision flow

TenthMatrix recommendation

Classify discriminative vs generative first. Prefer managed foundation models for language agents unless residency/cost forces open weights. Keep classic ML for tabular. Details, layer diagram, and checklist: long article. Published by TenthMatrix.

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