Implements Change Data Capture patterns for real-time data integration
Skills in this repository
a5c-ai/babysitter - Page 8
SkillsMP has collected 2,079 skills from a5c-ai/babysitter. Open a skill to review its source and details.
a5c-ai/babysitterShowing 40 of 2,079 collected skills.
Analyzes and optimizes costs for cloud data platforms
Enriches data catalog entries with automated metadata
Extracts and maps data lineage from various sources including SQL, dbt, Airflow, and Spark, generating comprehensive lineage graphs for impact analysis.
Profiles data assets to assess quality dimensions, detect anomalies, and generate comprehensive data quality reports with actionable recommendations.
Analyzes dbt projects for best practices, performance, maintainability, and generates actionable recommendations for improvement.
Validates dimensional models against Kimball methodology best practices
Optimizes feature engineering pipelines and feature store configurations
Generates Great Expectations suites from data profiles and business rules
Selects and configures optimal incremental model strategies
Designs and optimizes Apache Kafka topics and configurations
Designs and optimizes One Big Table (OBT) patterns
Generates Slowly Changing Dimension implementations across platforms
Manages schema evolution and compatibility across data systems
Analyzes and optimizes SQL queries across different data warehouse platforms (Snowflake, BigQuery, Redshift, Databricks) with platform-specific recommendations.
Designs optimal windowing strategies for stream processing
Alibi explainability skill for counterfactual explanations, anchors, and trust scores.
Arize AI skill for production ML monitoring, embedding drift, and performance analysis.
BentoML skill for model packaging, serving, and containerization.
Dataset versioning skill using DVC for tracking data changes, managing data pipelines, and ensuring reproducibility.
Evidently AI skill for data drift detection, model performance monitoring, target drift analysis, and automated reporting for ML systems in production.
Fairness assessment skill using Fairlearn for bias detection, mitigation, and compliance reporting.
Feature store management skill for online/offline feature serving, feature registration, and training-serving consistency.
Data quality validation skill using Great Expectations for schema validation, expectation suites, data documentation, and automated data quality checks in ML pipelines.
Jupyter notebook execution skill for running notebooks programmatically and extracting outputs.
Kubeflow Pipelines skill for ML workflow orchestration, component management, and Kubernetes-native ML.
LIME-based local explanation skill for individual predictions across tabular, text, and image data.
MLflow integration skill for experiment tracking, model registry, and artifact management. Enables LLMs to log experiments, compare runs, manage model lifecycle, and retrieve artifacts through the MLflow API.
Model documentation skill for generating model cards following Google's model card framework.
Optuna integration skill for automated hyperparameter optimization with advanced search strategies, pruning, multi-objective optimization, and visualization capabilities.
Automated DataFrame analysis skill for statistical summaries, missing value detection, data type inference, and memory optimization recommendations.
PyTorch model training skill with custom training loops, gradient management, and GPU optimization.
Distributed computing skill using Ray for parallel training, hyperparameter search, and resource management.
Seldon Core deployment skill for model serving, A/B testing, and canary deployments on Kubernetes.
SHAP-based model explainability skill for feature attribution, summary plots, and interaction analysis.
Scikit-learn model training skill with cross-validation, hyperparameter tuning, pipeline construction, and model serialization. Enables automated ML model development using scikit-learn's comprehensive toolkit.
TensorFlow/Keras model training skill with callbacks, distributed strategies, and TensorBoard integration.
Weights & Biases integration skill for experiment tracking, hyperparameter sweeps, and artifact versioning.
WhyLabs integration skill for ML observability, profile logging, and anomaly detection.
Run accessibility audits with axe-core and screen reader testing for desktop applications