Awesome AI in Finance
Section: Time Series Data · ML powered analytics engine for outlier/anomaly detection and root cause analysis..
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ML powered analytics engine for outlier/anomaly detection and root cause analysis..
Section: Time Series Data · ML powered analytics engine for outlier/anomaly detection and root cause analysis..
Section: Miscellaneous Tools · ML powered analytics engine for outlier/anomaly detection and root cause analysis
Section: Tools · ML powered analytics engine for outlier/anomaly detection and root cause analysis.
Section: Time Series · ML powered analytics engine for outlier/anomaly detection and root cause analysis
Section: Anomaly Detection Software · Python
Transpile trained ML models into other languages. sklearn-porter - Transpile trained scikit-learn estimators to C, Java, JavaScript and others. mlflow - Manage the machine learning lifecycle, including experimentation, reproducibility and deployment. skll - Command-line utilities to make it easier…
Comet's open-source AI observability and evaluation platform: deep tracing of LLM calls, conversation logging, and agent activity, plus built-in eval metrics, prompt versioning, guardrails, and the Opik Agent Optimizer. Worth including because it unifies observability, verification, and…
"Use airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it…
A fast and simple framework for building and running distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library. ray.io
Build and share delightful machine learning apps, all in Python. The de facto standard for creating interactive ML demos with automatic UI generation from function signatures. Powers thousands of Hugging Face Spaces.
Workflow management system that makes it easy to take your data pipelines and add semantics like retries, logging, dynamic mapping, caching, failure notifications, and more.