Applied AI by Domain
Every domain uses the same tools. Every domain has different constraints.
A fraud detection model at a bank must explain every decision to a regulator. A medical imaging model must hit 99.9% recall even at the cost of precision. A retail demand forecast must account for promotions, seasonality, and competitor actions simultaneously. The underlying ML techniques are often identical - the stakes, data conditions, and operating constraints are completely different.
This track is for engineers who have learned the fundamentals and want to understand how those fundamentals get applied under real-world domain constraints. Not toy examples - actual production architectures shaped by regulatory requirements, data scarcity, latency budgets, and industry-specific failure modes.
Why Domain Knowledge Matters
General ML knowledge gets you through the interview. Domain knowledge gets you promoted.
The engineers who have the most impact at companies like JPMorgan, Google Health, and Amazon are not the ones with the most theoretical depth - they are the ones who understand what the domain actually requires. They know which model failure modes matter (false positives in fraud vs. false negatives in cancer screening), which data quality problems are endemic to the industry, and which regulatory constraints are non-negotiable.
Domain knowledge also gives you sharper instincts. When you know that financial data has survivorship bias, you stop making a class of error before you make it. When you know that clinical NLP has annotation disagreement rates of 15-30%, you design your training pipeline differently from the start.
Five Domains, Five Modules
| Module | Domain | Core Problems |
|---|---|---|
| 1 | Healthcare | Medical imaging, clinical NLP, drug discovery, patient outcomes |
| 2 | Legal | Contract analysis, research automation, compliance monitoring |
| 3 | Retail | Demand forecasting, personalization, pricing, supply chain |
| 4 | Manufacturing | Predictive maintenance, quality control, digital twins |
| 5 | EdTech | Adaptive learning, assessment, knowledge tracing |
What Each Module Covers
Every module follows the same structure:
- The domain problem landscape - which ML problems actually get funded and deployed
- Data constraints - what the data looks like, label availability, quality issues, privacy
- Architecture patterns - the system designs that work at production scale
- Regulatory and ethical constraints - what you cannot do, and why
- Case studies with numbers - real systems, real metrics, real outcomes
- Failure modes - the mistakes that are specific to this domain
Prerequisites
This track assumes you are comfortable with ML fundamentals. If not, start here first:
- Machine Learning Track - supervised learning, neural networks, evaluation
- LLMs Track - transformers, fine-tuning, RAG, agents
- AI Engineering Track - production AI patterns
Start Here
Pick the domain most relevant to your current or target role. Each module is independent - you do not need to complete them in order.
For healthcare: start with Medical Imaging AI
For legal: start with Contract Analysis and NLP