A step-by-step breakdown of the MLOps pipeline along with best practices and tools.
Description: Collect raw data from various sources like databases, APIs, logs, IoT devices, etc.
Best Practices: Ensure structured data collection, automate ingestion pipelines.
Tools: Apache Kafka, Apache Nifi, Airflow, AWS Glue, Google Cloud Dataflow
Description: Check data quality, integrity, and consistency.
Best Practices: Use schema validation, automate data validation pipelines.
Tools: Great Expectations, Deequ, TensorFlow Data Validation (TFDV)
Description: Handle missing values, remove duplicates, resolve inconsistencies.
Best Practices: Define clear rules, automate cleaning processes.
Tools: Pandas, PySpark, OpenRefine
Description: Convert data into a uniform format for easier processing.
Best Practices: Normalize numerical values, standardize categorical data.
Tools: Scikit-learn, Pandas, NumPy
Description: Organize and prepare data for feature engineering.
Best Practices: Maintain metadata, align curated datasets with business objectives.
Tools: Delta Lake, DVC, Snowflake
Description: Identify and derive useful features from raw data.
Best Practices: Use feature extraction techniques like PCA, embeddings.
Tools: Featuretools, Scikit-learn, TensorFlow Feature Columns
Description: Choose the most relevant features for modeling.
Best Practices: Use selection techniques like mutual information, SHAP values.
Tools: Scikit-learn, SHAP, BorutaPy
Description: Experiment with different ML models.
Best Practices: Start with baseline models before tuning complex ones.
Tools: TensorFlow, PyTorch, Scikit-learn, H2O.ai, Google AutoML
Description: Implement ML models, pipelines, and training logic.
Best Practices: Follow modular programming, use version control.
Tools: Git, Jupyter Notebooks, VS Code, PyCharm
Description: Fit models using the prepared dataset.
Best Practices: Use GPU/TPU acceleration, implement early stopping.
Tools: TensorFlow, PyTorch, XGBoost
Description: Assess model performance on validation data.
Best Practices: Use cross-validation, compare models with benchmarks.
Tools: Scikit-learn, TensorBoard, MLflow
Description: Release model into production and track performance.
Best Practices: Use A/B testing, automate monitoring alerts.
Tools: AWS SageMaker, Kubernetes, Prometheus, Grafana
Description: Update or remove models based on performance.
Best Practices: Automate scheduled retraining with fresh data.
Tools: Kubeflow, Airflow, DVC