Full-Stack Data Science Pipeline

Connect to raw data sources (databases, data lakes, APIs) and pull the dataset. Perform schema validation, handle missing values, and split into train/validation/test sets. This step ensures data quality before any modeling begins.

Why LanceDB: LanceDB can store and query embeddings from data validation outputs, and manage multimodal data from cloud storage, aligning with the ingestion and validation needs.

Transform raw features into model-ready inputs: encode categoricals, scale numerics, create derived features, and handle temporal patterns. Wrap all transformations into a reproducible pipeline (e.g., using scikit-learn Pipeline or custom classes) to avoid data leakage.

Why scikit-learn: scikit-learn directly provides classification, regression, and clustering tools essential for feature engineering and pipeline assembly.

Train candidate models using the feature pipeline, then perform hyperparameter optimization (e.g., grid search, Bayesian optimization) with cross-validation. Select the best model based on validation metrics, and register it in a model registry (e.g., MLflow, SageMaker) with full metadata (parameters, metrics, lineage).

Why scikit-learn: scikit-learn provides classification, regression, and clustering algorithms directly needed for model training and tuning.

Containerize the model and its feature pipeline (e.g., Docker) and deploy it behind a REST API (e.g., FastAPI, Flask). Set up a lightweight inference server (e.g., TorchServe, BentoML) that handles request batching, input validation, and returns predictions with confidence scores.

Why Huddle01 Cloud: Huddle01 Cloud can deploy virtual machines and managed Kubernetes clusters, directly supporting Docker, Kubernetes, and API exposure needs.

Instrument the API to log every prediction request/response, input features, and model version. Set up dashboards (e.g., Grafana, Prometheus) to track latency, throughput, error rates, and data drift metrics (e.g., PSI, KS test). Configure alerts for anomalies (e.g., accuracy drop, drift threshold breach).

Why Evidently AI: Evidently AI directly provides data drift detection and production model monitoring, core to observability and monitoring setup.

Set up a scheduled pipeline (e.g., Airflow, cron) that re-runs the entire workflow on new data, compares new model performance against the current production model, and promotes the new version if it meets a performance threshold. This ensures the model stays accurate as data evolves.

Why Flyte: Flyte orchestrates ML pipelines and large-scale batch processing, directly supporting automated retraining and model refresh workflows.

Generate periodic reports (PDF, dashboard snapshots) summarizing model performance, data drift, and business impact. Maintain an audit trail of model versions, training data, and decisions for compliance. This step closes the loop with business stakeholders.

Why Tableau AI: Tableau AI offers data analysis and visualization, directly supporting stakeholder reporting and governance needs.