ml-pipeline-automation
Build end-to-end ML pipelines with automated data processing, training, validation, and deployment using Airflow, Kubeflow, and Jenkins
发现 tools data engineering 相关 Skills,比较来源仓库和用途,查看适合你工作流的技能说明。
Build end-to-end ML pipelines with automated data processing, training, validation, and deployment using Airflow, Kubeflow, and Jenkins
Debug database performance issues through query analysis, index optimization, and execution plan review. Identify and fix slow queries.
Design database schemas with normalization, relationships, and constraints. Use when creating new database schemas, designing tables, or planning data models for PostgreSQL and MySQL.
Plan and execute cloud migrations with assessment, database migration, application refactoring, and cutover strategies across AWS, Azure, and GCP.
Monitor database performance and health. Use when setting up monitoring, analyzing metrics, or troubleshooting database issues.
Create safe, reversible database migration scripts with rollback capabilities, data validation, and zero-downtime deployments. Use when changing database schemas, migrating data between systems, or performing l...
Manage database migrations and schema versioning. Use when planning migrations, version control, rollback strategies, or data transformations in PostgreSQL and MySQL.
Improve database query performance through indexing, query optimization, and execution plan analysis. Reduce response times and database load.
Manages schema lifecycle including scaffolding, safe field deprecation, and data migrations. Use when modifying existing schemas.
Expert assistance for writing, optimizing, and debugging GROQ queries. Use when writing queries or debugging performance.
Use when Python data modeling with dataclasses, attrs, and Pydantic. Use when creating data structures and models.
Use when validating and casting data with Ecto changesets including field validation, constraints, nested changesets, and data transformation. Use for ensuring data integrity before database operations.
Create efficient data pipelines with tf.data
Use when optimizing GraphQL API performance with query complexity analysis, batching, caching strategies, depth limiting, monitoring, and database optimization.
Use when Java Streams API for functional-style data processing. Use when processing collections with streams.
Use when scala collections including immutable/mutable variants, List, Vector, Set, Map operations, collection transformations, lazy evaluation with views, parallel collections, and custom collection builders f...
Automatically discover database skills when working with SQL, PostgreSQL, MongoDB, Redis, database schema design, query optimization, migrations, connection pooling, ORMs, or database selection. Activates for d...
Automatically discover data pipeline and ETL skills when working with ETL. Activates for data development tasks.
Database optimization, query tuning, migrations, and administration. Use for database performance issues, schema design, or operational tasks.
Build ETL pipelines, data warehouses, and streaming architectures. Use for data pipeline design or analytics infrastructure.
Write SQL queries, optimize execution plans, and design schemas. Use for query optimization, complex joins, or database design.
Data analysis, SQL queries, BigQuery operations, and data insights. Use for data analysis tasks and queries.
Convert spatial data (GeoJSON, Shapefile, etc.) to optimized GeoParquet using the gpio CLI. Analyzes files, recommends settings, and publishes to cloud storage.
Prisma ORM patterns including Prisma Client usage, queries, mutations, relations, transactions, and schema management. Use when working with Prisma database operations or schema definitions.
默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库,同一仓库的多个技能可能共享相同数值;它反映仓库关注度,不代表单个技能的安装量或质量评分。