# Data Science and Machine Learning - Process Backlog (Phase 2)

## Overview

This backlog contains concrete processes identified from the Data Science and Machine Learning specialization that can be orchestrated using the Babysitter SDK. Each process represents a repeatable workflow that can be automated, quality-gated, and iteratively refined.

## Summary Statistics

- **Total Processes**: 18
- **Categories**: 4 (Planning, Execution, Validation, Monitoring)
- **Planning Processes**: 5
- **Execution Processes**: 7
- **Validation Processes**: 4
- **Monitoring Processes**: 2

## Planning Processes

### [x] ML Project Scoping and Requirements Analysis

**Description**: Define business objectives, success metrics, constraints, and technical requirements for a machine learning project. Includes feasibility assessment, data availability checks, and stakeholder alignment.

**Category**: Planning

**References**:
- [CRISP-DM Methodology](https://www.datascience-pm.com/crisp-dm-2/)
- [Team Data Science Process (TDSP)](https://learn.microsoft.com/en-us/azure/architecture/data-science-process/overview)
- [MLOps Principles (Google Cloud)](https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning)

---

### [x] Exploratory Data Analysis (EDA) Pipeline

**Description**: Automated exploration of datasets including statistical summaries, distribution analysis, correlation studies, missing value assessment, outlier detection, and visualization generation with quality gates for data quality.

**Category**: Planning

**References**:
- [CRISP-DM Methodology](https://www.datascience-pm.com/crisp-dm-2/)
- [Pandas Data Analysis](https://pandas.pydata.org/)
- [Great Expectations Data Validation](https://greatexpectations.io/)

---

### [x] Feature Engineering Design and Implementation

**Description**: Design and implement feature transformations, derived features, encoding strategies, and feature selection with validation against training-serving skew and data leakage prevention.

**Category**: Planning

**References**:
- [Feature Engineering for Machine Learning (O'Reilly)](https://www.oreilly.com/library/view/feature-engineering-for/9781491953235/)
- [Rules of Machine Learning - Google](https://developers.google.com/machine-learning/guides/rules-of-ml)
- [Feast Feature Store](https://feast.dev/)

---

### [x] ML Architecture Design and Model Selection

**Description**: Design system architecture for ML pipelines, select candidate model architectures, define evaluation criteria, and establish baseline performance benchmarks with iterative refinement.

**Category**: Planning

**References**:
- [Designing Machine Learning Systems by Chip Huyen](https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/)
- [Production ML Systems - Google](https://developers.google.com/machine-learning/crash-course/production-ml-systems)
- [ML Test Score: Production Readiness Rubric](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/aad9f93b86b7addfea4c419b9100c6cdd26cacea.pdf)

---

### [x] Experiment Planning and Hypothesis Testing

**Description**: Design ML experiments with clear hypotheses, establish statistical test criteria, plan A/B test configurations, and define success metrics with iterative learning loops.

**Category**: Planning

**References**:
- [Rules of Machine Learning - Google](https://developers.google.com/machine-learning/guides/rules-of-ml)
- [Experimentation Best Practices](https://developers.google.com/machine-learning/guides/rules-of-ml)

---

## Execution Processes

### [x] Data Collection and Validation Pipeline

**Description**: Orchestrate data ingestion from multiple sources, validate schema compliance, check data quality metrics, handle missing values, and version datasets with automated quality gates.

**Category**: Execution

**References**:
- [Great Expectations](https://greatexpectations.io/)
- [DVC (Data Version Control)](https://dvc.org/)
- [MLOps Principles (Google Cloud)](https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning)

---

### [x] Model Training Pipeline with Experiment Tracking

**Description**: Execute model training with hyperparameter tuning, track experiments with metrics and artifacts, compare model variants, and select best performers with automated quality gates and convergence criteria.

**Category**: Execution

**References**:
- [MLflow Experiment Tracking](https://mlflow.org/)
- [Weights & Biases](https://wandb.ai/)
- [Kubeflow Pipelines](https://www.kubeflow.org/)
- [TensorFlow](https://www.tensorflow.org/)
- [PyTorch](https://pytorch.org/)

---

### [x] Feature Store Implementation and Management

**Description**: Build and maintain feature store infrastructure, implement online and offline feature serving, ensure consistency between training and serving, and manage feature versioning.

**Category**: Execution

**References**:
- [Feast Feature Store](https://feast.dev/)
- [Tecton Feature Platform](https://www.tecton.ai/)
- [Rules of Machine Learning - Avoiding Training-Serving Skew](https://developers.google.com/machine-learning/guides/rules-of-ml)

---

### [x] Model Deployment Pipeline with Canary Release

**Description**: Package models for production, deploy with gradual traffic rollout, implement shadow mode testing, monitor health metrics, and enable automated rollback on degradation.

**Category**: Execution

**References**:
- [MLOps Principles - Continuous Delivery](https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning)
- [TensorFlow Serving](https://www.tensorflow.org/tfx/guide/serving)
- [Seldon Core ML Deployment](https://www.seldon.io/tech/products/core/)
- [BentoML Model Serving](https://www.bentoml.com/)

---

### [x] AutoML Pipeline Orchestration

**Description**: Orchestrate automated machine learning workflows including algorithm selection, hyperparameter optimization, ensemble creation, and model selection with human-in-the-loop validation gates.

**Category**: Execution

**References**:
- [Auto-sklearn](https://automl.github.io/auto-sklearn/)
- [H2O AutoML](https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html)
- [MLflow Experiment Tracking](https://mlflow.org/)

---

### [x] Distributed Training Orchestration

**Description**: Coordinate distributed model training across multiple nodes/GPUs, manage resource allocation, handle failures and retries, aggregate results, and optimize training efficiency.

**Category**: Execution

**References**:
- [Kubeflow Distributed Training](https://www.kubeflow.org/)
- [Ray Distributed Computing](https://docs.ray.io/)
- [Apache Spark MLlib](https://spark.apache.org/)
- [Horovod Distributed Deep Learning](https://github.com/horovod/horovod)

---

### [x] ML Model Retraining Pipeline

**Description**: Detect model staleness triggers, automatically retrain models on updated data, validate improved performance, deploy updated models, and maintain model version lineage.

**Category**: Execution

**References**:
- [MLOps Continuous Training](https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning)
- [Apache Airflow Workflow Orchestration](https://airflow.apache.org/)
- [Prefect Modern Workflow](https://www.prefect.io/)

---

## Validation Processes

### [x] Model Evaluation and Validation Framework

**Description**: Execute comprehensive model evaluation on held-out test sets, calculate multiple performance metrics, analyze error distributions, validate business metric impact, and ensure fairness across demographic groups.

**Category**: Validation

**References**:
- [ML Test Score: Production Readiness](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/aad9f93b86b7addfea4c419b9100c6cdd26cacea.pdf)
- [Fairlearn Fairness Assessment](https://fairlearn.org/)
- [scikit-learn Model Evaluation](https://scikit-learn.org/)

---

### [x] A/B Testing Framework for ML Models

**Description**: Design and execute A/B tests comparing model variants, collect statistical evidence, analyze business impact metrics, and make data-driven deployment decisions with iterative refinement.

**Category**: Validation

**References**:
- [Rules of Machine Learning - Testing](https://developers.google.com/machine-learning/guides/rules-of-ml)
- [Production ML Systems - Google](https://developers.google.com/machine-learning/crash-course/production-ml-systems)

---

### [x] Model Interpretability and Explainability Analysis

**Description**: Generate model explanations using SHAP/LIME, analyze feature importance, create model cards documenting behavior and limitations, and validate interpretability requirements for compliance.

**Category**: Validation

**References**:
- [SHAP (SHapley Additive exPlanations)](https://github.com/slundberg/shap)
- [LIME Explanations](https://github.com/marcotcr/lime)
- [InterpretML Microsoft](https://interpret.ml/)
- [Interpretable Machine Learning Book](https://christophm.github.io/interpretable-ml-book/)

---

### [x] ML System Integration Testing

**Description**: Test end-to-end ML pipelines including data ingestion, feature computation, model inference, and downstream integration with comprehensive test coverage and performance validation.

**Category**: Validation

**References**:
- [ML Test Score Rubric](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/aad9f93b86b7addfea4c419b9100c6cdd26cacea.pdf)
- [Great Expectations Testing](https://greatexpectations.io/)
- [Hidden Technical Debt in ML Systems](https://papers.nips.cc/paper/2015/hash/86df7dcfd896fcaf2674f757a2463eba-Abstract.html)

---

## Monitoring Processes

### [x] Model Performance Monitoring and Drift Detection

**Description**: Continuously monitor prediction accuracy, detect data drift and concept drift, track feature distributions, alert on performance degradation, and trigger retraining workflows.

**Category**: Monitoring

**References**:
- [Evidently AI Model Monitoring](https://www.evidentlyai.com/)
- [Arize AI ML Observability](https://arize.com/)
- [WhyLabs AI Observability](https://whylabs.ai/)
- [MLOps Continuous Monitoring](https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning)

---

### [x] ML System Observability and Incident Response

**Description**: Implement comprehensive logging and monitoring for ML systems, establish alerting on anomalies, create incident response runbooks, track SLOs/SLIs, and coordinate remediation with quality gates.

**Category**: Monitoring

**References**:
- [ML Test Score: Monitoring](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/aad9f93b86b7addfea4c419b9100c6cdd26cacea.pdf)
- [Production ML Systems - Google](https://developers.google.com/machine-learning/crash-course/production-ml-systems)
- [Hidden Technical Debt in ML Systems](https://papers.nips.cc/paper/2015/hash/86df7dcfd896fcaf2674f757a2463eba-Abstract.html)

---

## Notes

- All processes should implement quality gates appropriate to their category
- Iterative refinement loops should be built into planning and validation processes
- Integration with existing methodologies (CRISP-DM, TDSP, Agile) is recommended
- Processes should be composable and reusable across different ML projects
- Human-in-the-loop validation gates are critical for production deployments
- Compliance and governance checkpoints should be integrated throughout
