MLOps Pipelines
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- Author
- Ram Simran G
- twitter @rgarimella0124
As a DevOps engineer with years of experience bridging the gap between development, operations, and now data science teams, I’ve seen firsthand how machine learning (ML) projects can spiral into chaos without proper structure. Traditional software pipelines are linear and predictable, but ML introduces complexities like data drift, model versioning, and the need for reproducible experiments. This is where MLOps comes in—applying DevOps principles to ML workflows to ensure scalability, reliability, and efficiency. In 2025, with AI adoption at an all-time high, mastering MLOps isn’t optional; it’s essential for deploying production-ready models that deliver real business value.
Recently, I came across a series of diagrams on social media that beautifully illustrate key MLOps components: feature pipelines, training pipelines, and inference pipelines. These visuals, often shared in tech communities, highlight the journey from raw data sources to actionable predictions, like price prediction or fraud scoring. In this lengthy blog post, I’ll dissect these diagrams from a DevOps lens, incorporating all the terms mentioned, comparing the visuals, outlining a complete end-to-end pipeline, and adding critical elements not shown—such as CI/CD integration, monitoring, security, and scaling. I’ll also include Markdown-based diagrams for better visualization, drawing on best practices from the evolving MLOps landscape.
Understanding the Core Components from the Diagrams
The diagrams focus on three interconnected pipelines: feature engineering, model training, and inference. They use a fraud detection scenario (e.g., scoring transactions for fraudulent vs. non-fraudulent activity) leading to outcomes like price prediction or SMS alerting. Key terms like raw data source, feature pipeline, feature store, ML features, experiment tracker, training pipeline, model registry, inference pipeline, training data (features, labels), metadata (test metrics, logs, charts), ML model, payment gateway, transaction ingestion service, Kafka, fraud scoring service, model artifact, fraud scores, transaction updater service, SMS alerting service, data warehouse, feature engineering service, contextual features ingestor, labels (fraudulent vs non-fraudulent), feature group 1, feature group 2, labels group, feature view, and model training service are all integral.
From a DevOps perspective, these components emphasize automation, versioning, and observability—hallmarks of reliable systems. Let’s break them down.
The Feature Pipeline: From Raw Data to Reusable Features
The first diagram depicts the feature pipeline as the foundation: starting from a raw data source (e.g., databases or streams), data flows through a feature pipeline that transforms it into ML features stored in a feature store. This store acts as a centralized repository for reusable features, ensuring consistency across training and inference.
In practice, as a DevOps engineer, I’d orchestrate this with tools like Apache Airflow or Kubeflow for scheduling, ensuring data ingestion is idempotent and fault-tolerant. The feature store (e.g., Feast or Tecton) allows for feature groups—like feature group 1 for basic transaction details and feature group 2 for aggregated behaviors—plus labels groups for categorizing fraudulent vs non-fraudulent data. A feature view provides a queryable interface for downstream pipelines.
The diagram also shows raw data branching directly to the feature store, highlighting offline processing, while real-time paths might use Kafka for streaming.
Markdown Diagram for Feature Pipeline:
+-------------------+ +-------------------+ +-------------------+
| Raw Data Source | -> | Feature Pipeline | -> | Feature Store |
| (e.g., Data | | (Transforms raw | | (Stores ML |
| Warehouse) | | data to features)| | Features, Groups)|
+-------------------+ +-------------------+ +-------------------+
|
v
+-------------------+
| Feature View |
| (Queryable |
| Interface) |
+-------------------+The Training Pipeline: Building Models with Metadata and Experimentation
The second and fourth diagrams zoom into the training pipeline. Here, training data (comprising features and labels from the feature store) feeds into a training pipeline, producing an ML model stored in a model registry. An experiment tracker logs metadata like test metrics, logs, and charts for reproducibility.
From my DevOps viewpoint, this is where CI/CD shines: automate hyperparameter tuning with tools like MLflow or Weights & Biases (the experiment tracker). The model registry (e.g., MLflow Model Registry or Hugging Face) versions model artifacts, ensuring traceability. Labels (fraudulent vs non-fraudulent) are crucial for supervised learning, and the pipeline integrates with a data warehouse for historical data.
The diagrams show bidirectional flow with the experiment tracker, emphasizing iteration—DevOps engineers would set up automated retraining triggers based on data drift.
Markdown Diagram for Training Pipeline:
+-------------------+ +-------------------+ +-------------------+
| Feature Store | -> | Training Data | -> | Training Pipeline |
| (Features, Labels)| | (Features + | | (Builds ML Model) |
| | | Labels) | +-------------------+
+-------------------+ +-------------------+ |
| v
+-------------------+ +-------------------+
| Experiment Tracker| | Model Registry |
| (Metadata: Test | | (Stores ML Model |
| Metrics, Logs, | | Artifact) |
| Charts) | +-------------------+
+-------------------+The Inference Pipeline: Real-Time Predictions and Scoring
The third, fifth, and sixth diagrams illustrate the inference pipeline for production use. In a fraud detection example, data from a payment gateway enters via a transaction ingestion service, streams through Kafka (transactions topic), and reaches a fraud scoring service using a model artifact from the model registry. Outputs like fraud scores go to Kafka (fraud scores topic), then to a transaction updater service or SMS alerting service.
The feature store plays a dual role: providing feature views for real-time enrichment via feature engineering service and contextual features ingestor. This ensures fresh features for predictions, like price prediction.
As a DevOps engineer, I’d focus on latency: deploy the inference pipeline on Kubernetes with auto-scaling, using Kafka for decoupling services to handle spikes in transaction volume.
Markdown Diagram for Inference Pipeline:
+-------------------+ +-------------------+ +-------------------+
| Payment Gateway | -> | Transaction | -> | Kafka |
| | | Ingestion Service | | (Transactions |
| | | | | Topic) |
+-------------------+ +-------------------+ +-------------------+
|
v
+-------------------+ +-------------------+ +-------------------+
| Feature Store | <- | Fraud Scoring | -> | Kafka (Fraud |
| (Feature Groups, | | Service (Model | | Scores Topic) |
| Labels Group, | | Artifact) | +-------------------+
| Feature View) | +-------------------+ |
+-------------------+ ^ v
| | +-------------------+
| | | Transaction |
v | | Updater Service / |
+-------------------+ | | SMS Alerting |
| Data Warehouse | | | Service |
| (Contextual | | +-------------------+
| Features |
| Ingestor, |
| Feature Eng. |
| Service) |
+-------------------+Comparing the Diagrams: Similarities, Differences, and Insights
Comparing these visuals reveals a cohesive narrative but with nuanced focuses:
Similarities: All emphasize the feature store as a central hub, decoupling data preparation from modeling. The model registry is a common endpoint for training and starting point for inference. Experiment trackers appear in training-focused diagrams, underscoring the need for metadata in iterative development. Raw data sources and pipelines lead to practical outcomes like price prediction.
Differences: The first and fifth diagrams are high-level, abstracting to price prediction, while the third and sixth dive into fraud-specific flows with Kafka, payment gateways, and alerting—highlighting real-time vs. batch processing. Training diagrams (second, fourth) include labels and metadata explicitly, whereas inference ones focus on fresh features and scoring services. The fraud examples add complexity with feature groups and views, showing scalability for production.
From a DevOps angle, these differences highlight pipeline modularity: feature pipelines are reusable across training/inference, reducing redundancy. However, the diagrams overlook integration points, like how CI/CD triggers retraining on code changes.
The Complete End-to-End MLOps Pipeline
Combining these, a full MLOps pipeline as a DevOps engineer would design it flows like this:
Data Ingestion: From raw data sources (e.g., data warehouse or payment gateway) via transaction ingestion service to Kafka.
Feature Engineering: Feature pipeline processes data into ML features, stored in feature store with groups (feature group 1/2, labels group) and views.
Training: Pull training data (features, labels—fraudulent vs non-fraudulent) into training pipeline, log to experiment tracker (metadata: test metrics, logs, charts), produce ML model artifact in model registry.
Inference: Fraud scoring service fetches model artifact and fresh features from feature store (via contextual features ingestor/feature engineering service), computes fraud scores, updates via transaction updater service or triggers SMS alerting.
Output: Predictions like price prediction or alerts.
This loop closes with monitoring: if drift detected, retrain automatically.
Markdown Diagram for Complete Pipeline:
Data Ingestion
+-------------------+ +-------------------+
| Raw Data Source | -> | Transaction |
| (Payment Gateway, | | Ingestion Service |
| Data Warehouse) | +-------------------+
+-------------------+ |
v
+-------------------+ +-------------------+
| Kafka | -> | Feature Pipeline |
| (Transactions | | (Engineering |
| Topic) | | Service, |
+-------------------+ | Contextual |
| Ingestor) |
+-------------------+
|
v
Feature Management
+-------------------+ +-------------------+
| Feature Store | <- | Feature Groups |
| (Feature View) | | (Group 1/2, |
+-------------------+ | Labels Group - |
| Fraudulent vs |
| Non-Fraudulent) |
+-------------------+
|
Training | Inference
| | |
v | v
+-------------------+ | +-------------------+
| Training Pipeline | | | Inference Pipeline|
| (Training Data: | | | (Fraud Scoring |
| Features + | | | Service, Model |
| Labels) | | | Artifact) |
+-------------------+ | +-------------------+
| | |
v | v
+-------------------+ +-------------------+ +-------------------+
| Experiment Tracker| | Model Registry | | Kafka (Fraud |
| (Metadata: Test | | (ML Model | | Scores Topic) |
| Metrics, Logs, | | Artifact) | +-------------------+
| Charts) | +-------------------+ |
+-------------------+ v
+-------------------+
| Outputs: Price |
| Prediction, |
| Transaction |
| Updater, SMS |
| Alerting Service |
+-------------------+Additional Components Not Mentioned: Enhancing the Pipeline for Production Readiness
While the diagrams cover core flows, real-world MLOps requires more for robustness. As a DevOps engineer, I’d add:
CI/CD Integration: Use GitHub Actions or Jenkins to automate pipeline triggers. For example, code changes in feature engineering service deploy via Helm to Kubernetes, with automated tests for data validation.
Monitoring and Observability: Tools like Prometheus and Grafana track model performance, data drift, and latency. Integrate with the experiment tracker for alerts on degrading test metrics.
Versioning and Lineage: Beyond model registry, use DVC or lakeFS for data versioning. Track lineage from raw data source to fraud scores for audits.
Security and Compliance: Implement RBAC in the feature store, encrypt Kafka topics, and scan model artifacts for vulnerabilities. For fraud systems, ensure GDPR compliance in labels handling.
Scaling and Orchestration: Deploy on cloud-native platforms like AWS SageMaker or Google Vertex AI for auto-scaling inference. Use Kubeflow for end-to-end orchestration.
Retraining Loops: Automate model training service triggers based on drift detection, closing the loop from inference back to training.
Cost Optimization: Monitor resource usage in training pipelines; use spot instances for non-critical jobs.
These additions prevent common pitfalls like siloed teams or unmonitored deployments, aligning with 2025 best practices emphasizing automation and collaboration.
Conclusion: Embracing MLOps as a DevOps Evolution
In summary, these diagrams provide a solid blueprint for MLOps pipelines, from raw data transformation to real-time fraud scoring and price prediction. By comparing them, we see how feature stores and model registries enable modular, reusable workflows. As DevOps engineers, our role is to operationalize these—infusing CI/CD, monitoring, and security to build resilient systems. In 2025, with AI regulations tightening and models growing complex, adopting these practices ensures your ML initiatives scale without breaking. Start small: prototype a feature pipeline, then expand. The future of DevOps is MLOps—let’s build it together!
Cheers,
Sim