How Machine Learning Systems Are Built: An End-to-End Overview with MLOps in Mind

[Lomanu4]

As I begin my journey into Machine Learning Engineering, I want to understand not just how models work — but, how entire ML systems are designed, deployed, and maintained in the real world.

In this post, I’ll break down the end-to-end lifecycle of a Machine Learning project, including where MLOps fits in.

? The Machine Learning Lifecycle


Here’s a high-level look at the typical stages in a production-ready ML workflow:

1. Problem Definition

• What's the goal? Predict churn? Classify images? Detect fraud?
• ML might not even be the right solution — business context matters.

2. Data Collection

• Raw data from logs, APIs, sensors, databases.
• Often messy, incomplete, or biased.

3. Data Cleaning & Preprocessing

• Handle missing values, outliers, encoding, normalization, etc.
• Feature engineering — the art of extracting signal from noise.

4. Model Training

• Choose an algorithm: linear regression, decision tree, neural net?
• Use frameworks like Scikit-learn, TensorFlow, or PyTorch.
• Split data into training/validation/test.

5. Model Evaluation

• Accuracy isn’t enough. Think about precision, recall, F1, AUC.
• Use confusion matrices and cross-validation to evaluate.

6. Model Deployment

• Turn the model into a service (API or batch job).
• Use tools like Flask, FastAPI, or platforms like SageMaker, Vertex AI.

7. Monitoring & Maintenance

• Is the model still performing well?
• Detect drift, monitor latency, trigger retraining when needed.

? Where Does MLOps Fit?


MLOps (Machine Learning Operations) is the discipline of treating ML systems with the same rigor as traditional software:

MLOps Concern	Why It Matters
Reproducibility	Can we re-run the training and get the same result?
Versioning	Track data, code, model versions
Automation	Use CI/CD for model training & deployment
Monitoring	Detect model degradation, data drift, anomalies
Collaboration	Devs, data scientists, and ops all need to work together

Common tools:

• MLflow, DVC: experiment tracking
• Airflow, Prefect: pipelines
• Kubeflow, TFX: scalable ML workflows
• Docker, Kubernetes: containerization and orchestration

? What’s Next for Me?


Next, I’ll start building a simple ML pipeline — from data to deployment — probably using Scikit-learn + FastAPI + Docker. I’ll blog each step as I go.

This post is my anchor — a reference point I’ll keep returning to.

Let me know what you’d like to see more of — or if I missed anything major.

Let’s build some real ML.

— Sri

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