flowchart LR A[Learn Foundations] --> B[Build Models] B --> C[Evaluate Models] C --> D[Deploy Model] D --> E[Operate System] E --> F[Improve System] F --> A
ML Systems Role Path: From Models to Real Systems
This chapter extends the career system into machine learning and systems.
Machine learning is not only about building models.
It is about building systems that work in real environments.
This path connects:
- data → models → deployment → operation
What This Path Represents
This is a structured path from:
- learning → modeling → evaluation → deployment → system operation → improvement
Focused on the ML Engineer / ML Systems role.
The Full Path
Stage 1: Learn Foundations
Data Foundations
- data cleaning
- feature understanding
- data splitting
Modeling Foundations
- regression
- classification
- model evaluation
Programming Foundations
- Python
- libraries (scikit-learn, pandas, numpy)
Stage 2: Build Models
Create simple models:
- predict outcomes
- classify categories
- test different approaches
Focus on:
- correct setup
- reproducible workflow
- clear structure
Stage 3: Evaluate Models
Go beyond accuracy.
Evaluate using:
- appropriate metrics
- validation strategies
- comparison of models
Understand:
- what the model is doing
- where it fails
- how reliable it is
Stage 4: Deploy Model
Move from notebook to usable system.
This includes:
- building an API
- creating a simple interface
- packaging the model
Deployment makes the model usable.
Stage 5: Operate System
Once deployed, the system must run reliably.
This includes:
- monitoring performance
- handling errors
- maintaining stability
This is where DevOps thinking begins.
Stage 6: Improve System
Systems evolve.
You:
- update models
- refine features
- improve performance
- incorporate feedback
This closes the loop.
What Makes This Path Different
Unlike analysis roles, this path includes:
- deployment
- operation
- continuous improvement
It is not a one-time process.
It is a system.
Readiness Within This Path
You are approaching readiness when:
- you can build and evaluate a model
- you can deploy it in a simple way
- you understand system behavior
- you can explain model decisions
- your workflow is reproducible
Common Pitfalls
- focusing only on models, not systems
- ignoring deployment
- overfitting without understanding
- building without evaluation
- skipping monitoring
CDI Perspective
At Complex Data Insights, machine learning is part of a larger system.
Models alone do not create value.
Systems do.
This path reflects:
- model → deployment → operation → improvement
What Comes Next
This completes the role-based paths.
In the final chapter, we summarize how to move forward and apply this system to your own career.