DE & MLOps Infrastructure
🏗️ Data Engineering & MLOps Infrastructure
In modern data stacks, the boundary between Data Engineering (DE) and Machine Learning Operations (MLOps) is increasingly blurred.
🟢 Level 1: Foundations
1. Data Ingestion for ML
Feeding models requires reliable, idempotent pipelines.
- Batch: S3 -> Spark -> Parquet.
- Streaming: Kafka -> Flink -> Feature Store.
2. The Model Training Pipeline
Standardizing the transition from raw data to a trained artifact.
🟡 Level 2: Feature Stores
A Feature Store (like Feast) is the single source of truth for both training and serving.
- Offline Store: Large-scale historical data for training.
- Online Store: Low-latency latest data for real-time inference.
🔴 Level 3: Experiment Tracking
Integrating tools like MLflow or Weights & Biases into your DE pipelines to track which data versions produced which models.