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2 repository-uri

Awesome GitHub RepositoriesFeature Views

Grouping related time-series features from a data source into a logical collection for consistent offline training and online serving.

Distinguishing note: No candidate in the shortlist covers the concept of grouping features into a logical view for ML pipelines.

Explore 2 awesome GitHub repositories matching data & databases · Feature Views. Refine with filters or upvote what's useful.

Awesome Feature Views GitHub Repositories

Găsește cele mai bune repo-uri cu AI.Vom căuta cele mai potrivite repository-uri folosind AI.
  • feast-dev/feastAvatar feast-dev

    feast-dev/feast

    6,727Vezi pe GitHub↗

    Feast is an open-source feature store for machine learning that provides a central platform for defining, storing, and serving features across both training and inference workflows. It operates as a declarative system where feature definitions are written as code in Python files, synchronized to a central registry, and made available for low-latency online retrieval or point-in-time correct historical joins for training datasets. The project abstracts storage behind a pluggable architecture, allowing offline and online backends to be swapped without changing retrieval logic, and coordinates ma

    Groups related time-series features from a data source into a logical collection for training and serving.

    Pythonbig-datadata-engineeringdata-quality
    Vezi pe GitHub↗6,727
  • cyb3rward0g/threathunter-playbookAvatar Cyb3rWard0g

    Cyb3rWard0g/ThreatHunter-Playbook

    4,594Vezi pe GitHub↗

    ThreatHunter-Playbook este un framework structurat pentru gestionarea playbook-urilor de threat hunting, a fluxurilor de lucru de inginerie a detecției și a modelării tradecraft-ului adversarului. Oferă un sistem pentru organizarea tiparelor comportamentale și a regulilor de detecție în grupuri tactice pentru a dezvolta ipoteze de monitorizare a securității. Proiectul dispune de un mediu de notebook de securitate interactiv care combină analizele și interogările de validare pentru a testa ipotezele de amenințare împotriva seturilor de date de telemetrie. Include un instrument de mapare pentru organizarea acestor tipare bazat pe framework-ul de securitate MITRE ATT&CK. Framework-ul acoperă întregul ciclu de viață al vânătorii de amenințări, inclusiv ciclurile formalizate de planificare și raportare. Permite dezvoltarea ingineriei de detecție prin potrivirea log-urilor de sistem și a datelor de evenimente așteptate cu telemetria reală a mediului pentru a valida ipotezele de securitate.

    Formalizes security operations through a repetitive sequence of planning, executing, and reporting phases.

    Python
    Vezi pe GitHub↗4,594
  1. Home
  2. Data & Databases
  3. Feature Views

Explorează sub-etichetele

  • CompositeJoins features from multiple sources or views into a single composite feature view using entity ID joins. **Distinct from Feature Views:** Distinct from Feature Views: focuses on combining multiple feature views into one, not defining a single view.
  • Execution Plans1 sub-tagResolving feature view definitions into execution plans that run transformations, aggregations, joins, and filters. **Distinct from Feature Views:** Distinct from Feature Views: focuses on the execution planning and resolution of feature operations, not just grouping features into views.
  • Multi-Feature View Batch ReadsIssuing a single pipeline for all feature views in a request to reduce round trips and latency. **Distinct from Feature Views:** Distinct from Feature Views: focuses on batching reads across multiple feature views, not just grouping features into a view.