Data Engineering & ⚙️ MLOps
1. What is Data Engineering?
Data Engineering focuses on building the systems and pipelines that move, store, prepare, and transform data so it can be used for analytics, machine learning, and business intelligence.
Key Responsibilities
Build ETL/ELT pipelines
Design data warehouses and data lakes
Ensure data quality, data governance, and data reliability
Optimize data workflows for scalability and performance
Integrate data from diverse sources (databases, APIs, logs, IoT, etc.)
Typical Tools
Big Data: Hadoop, Spark, Flink
Orchestration: Airflow, Dagster, Prefect
Data Warehouses: Snowflake, BigQuery, Redshift
Streaming: Kafka, Kinesis, Pulsar
Databases: Postgres, MySQL, MongoDB, Cassandra
Cloud Platforms: AWS/GCP/Azure (S3, Glue, Dataflow, Databricks)
2. What is MLOps?
MLOps (Machine Learning Operations) is a discipline that applies DevOps principles to the ML lifecycle.
It ensures ML models are reliably trained, deployed, monitored, and maintained in production.
Key Responsibilities
Automate model training, testing, and deployment
Manage model versioning and experiment tracking
Monitor model drift, data drift, and performance
Handle CI/CD for ML
Scale ML systems (batch or real-time inference)
Enable reproducible ML workflows
Typical Tools
Model training/orchestration: MLflow, Kubeflow, Airflow, Vertex AI
Model serving: TensorFlow Serving, TorchServe, FastAPI, BentoML
CI/CD: GitHub Actions, GitLab CI, Jenkins
Monitoring: Prometheus, Grafana, Evidently AI
Containers/Infra: Docker, Kubernetes, Terraform
3. How Data Engineering and MLOps Are Related
Data Engineering and MLOps are tightly connected because ML models are only as good as the data pipelines behind them.
Shared Areas
Data pipelines
Feature engineering
Streaming systems
Cloud infrastructure
Automation
Data Engineering provides:
➡️ Clean, reliable, production-ready data
➡️ Feature stores
➡️ Scalable data infrastructure
MLOps provides:
➡️ Automated model lifecycle
➡️ Deployment & monitoring
➡️ ML-specific infrastructure
Together, they enable end-to-end AI systems.
4. How They Differ
Aspect Data Engineering MLOps
Goal Manage & deliver clean data Manage & operationalize ML models
Focus Pipelines, storage, transformation Training, deployment, monitoring
Outputs Datasets, features, schemas Models, predictions, metrics
Tech Spark, Kafka, Airflow MLflow, TensorFlow, Kubernetes
Challenges Scalability, latency, data quality Drift, reproducibility, model decay
5. Example Workflow Combining Both
Data Engineering builds a pipeline to ingest raw data → clean → feature store
MLOps triggers training pipelines → stores model versions
MLOps deploys model to production
Monitoring systems track:
Data quality (DE)
Model performance (MLOps)
Retraining pipeline automatically triggers on drift
6. Career Paths
Data Engineering Roles
Data Engineer
Big Data Engineer
Data Platform Engineer
ETL Developer
MLOps Roles
MLOps Engineer
ML Platform Engineer
ML Infrastructure Engineer
AI Systems Engineer
Summary
Data Engineering builds the foundation: scalable, reliable data pipelines.
MLOps operationalizes machine learning: automated training, deployment, and monitoring.
Together, they form the backbone of production-grade AI systems.
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