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ML Projects for Experienced Professionals

Production-grade project ideas with architecture, datasets, evaluation, and business impact — chosen to stretch senior ML engineers.

How to use this page
These aren't toy notebooks. Each project is scoped like a real engineering initiative with an end-to-end deliverable. Click any card to see the architecture diagram, step-by-step plan, key risks, and the common problems engineers hit (with fixes).

What "experienced-level" means here

  • End-to-end ownership: data → training → serving → monitoring.
  • Production constraints: latency, cost, drift, reproducibility.
  • Business framing: pick a metric the business actually cares about.
  • Failure modes: explicit error analysis, not just leaderboard scores.

Project catalogue

#01MLOpsExpert
Real-time Fraud Detection Pipeline
Problem

Detect fraudulent credit-card transactions in <50ms with extreme class imbalance (~0.17% positive) and concept drift over time.

Stack
KafkaFeastLightGBMFastAPIMLflowEvidentlyDockerKubernetes
Business impact
Each 1% recall improvement at fixed false-positive rate often saves $1M+/year for a mid-size issuer.
Open deep dive — architecture, plan, risks, problems & solutions
#02GenAIExpert
Domain-Specific RAG Assistant for Internal Docs
Problem

Build a Retrieval-Augmented Generation system that answers questions over a private corpus (10k+ PDFs) with citations and zero hallucination tolerance.

Stack
LangGraphQdrant / pgvectorBGE embeddingsvLLMRagasLangSmith
Business impact
Replaces tier-1 internal support; typical deflection ~40% of repetitive queries.
Open deep dive — architecture, plan, risks, problems & solutions
#03Time SeriesAdvanced
Demand Forecasting at SKU x Store Granularity
Problem

Forecast 14-day demand for 50k SKU-store pairs with promotions, holidays, and intermittent demand.

Stack
LightGBMstatsforecastmlforecastOptunaPrefectDuckDB
Business impact
1-3% forecast accuracy gain typically reduces inventory holding costs by 5-10%.
Open deep dive — architecture, plan, risks, problems & solutions
#04Computer VisionAdvanced
Multi-Modal Visual Search for E-commerce
Problem

Given a user-uploaded photo, return visually + semantically similar products from a catalog of 5M items.

Stack
PyTorchOpenCLIPFAISS / QdrantONNX RuntimeTriton
Business impact
Visual search converts 2-4x higher than text search on fashion verticals.
Open deep dive — architecture, plan, risks, problems & solutions
#05RecommenderExpert
Two-Tower Recommender with Cold-Start Handling
Problem

Recommend items to 10M+ users with millions of items, including handling new users/items appearing every minute.

Stack
TensorFlow Recommenders / PyTorchScaNNBigQueryVertex AIAirflow
Business impact
Top-of-funnel rec quality drives 10-30% of platform GMV on most marketplaces.
Open deep dive — architecture, plan, risks, problems & solutions
#06MLOpsExpert
End-to-End MLOps Platform on Kubernetes
Problem

Build a self-serve platform where data scientists can train, register, deploy, and monitor models with full reproducibility and CI/CD.

Stack
KubernetesArgoCDMLflowKubeflowKServePrometheusTerraform
Business impact
Reduces ML cycle time from weeks to hours; the highest-leverage investment for a data org > 5 people.
Open deep dive — architecture, plan, risks, problems & solutions
#07NLPAdvanced
Causal Uplift Modeling for Marketing Campaigns
Problem

Identify which customers should receive a promotion to maximize *incremental* revenue, not just predicted purchase.

Stack
EconMLDoWhyscikit-learnCausalMLPandas
Business impact
Switching from look-alike to uplift targeting commonly improves marketing ROI by 20-50%.
Open deep dive — architecture, plan, risks, problems & solutions

Portfolio packaging tips

  • One repo per project with a clean README: problem, results, architecture diagram, how to reproduce.
  • Show the metric curve, not just the final number — and a baseline you beat.
  • Write a 1-page case study in business language; engineers read code, hiring managers read prose.
  • Deploy something live — a Streamlit / Gradio demo or a small HTTP API beats a static notebook.
  • Talk about trade-offs: what you tried, what failed, what you'd do with more time/data.

Suggested 90-day plan

  • Weeks 1–2: scope, data acquisition, baseline model, eval harness.
  • Weeks 3–6: iterate on modeling, ablations, error analysis.
  • Weeks 7–9: serving, monitoring, drift, CI/CD.
  • Weeks 10–12: write-up, demo video, blog post, polish.