Reverse Dependencies of mlflow-skinny

The following projects have a declared dependency on mlflow-skinny:

• acryl-datahub — A CLI to work with DataHub metadata
• adapta — Logging, data connectors, monitoring, secret handling and general lifehacks to make data people lives easier.
• atr-dan — Teklia DAN
• azure-ai-generative — Microsoft Azure Machine Learning Client Library for Python
• azure-ai-resources — Microsoft Azure Azure AI control plane SDK Client Library for Python
• azureml-contrib-automl-dnn-forecasting — Azure Automated Machine Learning DNN package for timeseries forecasting.
• azureml-evaluate-mlflow — Contains the integration code of AzureML Evaluate with Mlflow.
• azureml-mlflow — Contains the integration code of AzureML with Mlflow.
• azureml-train-automl — Used for automatically finding the best machine learning model and its parameters.
• codeflare-torchx — TorchX SDK and Components
• composable-logs — no summary
• composable-logs-snapshot — no summary
• cortex-container-tools — Nearly Human Cortex Container Tools dependency for creating training / deployment containers.
• databricks-feature-engineering — Databricks Feature Engineering Client
• databricks-rag-studio — Databricks RAG Studio Library
• databricks-vectorsearch — Databricks Vector Search Client
• databricks-vectorsearch-preview — Databricks Vector Search Client
• dbx — DataBricks CLI eXtensions aka dbx
• didas — Python Commons for Didas
• flowcept — FlowCept is a runtime data integration system that empowers any data processing system to capture and query workflow provenance data using data observability, requiring minimal or no changes in the target system code. It seamlessly integrates data from multiple workflows, enabling users to comprehend complex, heterogeneous, and large-scale data from various sources in federated environments.
• giskard — The testing framework dedicated to ML models, from tabular to LLMs
• h2o-experiment-tracking — Python client for H2O.ai Experiment Tracking.
• iap-token — no summary
• ibm-aigov-facts-client — FactSheetService ML facts collection utility
• idg-metadata-client — Ingestion Framework for OpenMetadata
• imgori — no summary
• irisml-tasks-training — IrisML tasks for pytorch training
• kozai-mlflow — MLflow for integrated kozai environments
• layer — Layer AI SDK
• magnus — A Compute agnostic pipelining software
• magnus-extensions — Extensions to Magnus core
• mantik — mantik for mlflow
• mlflow-databricks-artifacts — Plugin to create and access MLflow-managed artifacts on Databricks
• mlflow-iap-token — no summary
• mlflow-mlserver-docker — Package mlflow models as mlserver docker images
• mlrpc — Deploy FastAPI applications on MLFlow
• mlsriracha — A project to abstract the boilerplate required to deploy jobs in MLOps systems
• numalogic — Collection of operational Machine Learning models and tools.
• omegaml — An open source DataOps, MLOps platform for humans
• openmetadata-ingestion — Ingestion Framework for OpenMetadata
• orquestra-sdk — Compose Orquestra workflows using a Python DSL
• radops — ops tooling for Striveworks's R&D team
• rl8 — A high throughput, end-to-end RL library for infinite horizon tasks.
• rlstack — A minimal RL library for infinite horizon tasks.
• runnable — A Compute agnostic pipelining software
• sfu-ml-lib — Libraries that support the development of machine learning models in TensorFlow.
• sfu-tf-lib — Libraries that support the development of machine learning models in TensorFlow.
• sfu-torch-lib — Libraries that support the development of machine learning models in PyTorch.
• sparkpipelineframework — Framework for simpler Spark Pipelines
• torchx — TorchX SDK and Components
• torchx-applovin — TorchX SDK and Components
• torchx-nightly — TorchX SDK and Components
• whylogs — Profile and monitor your ML data pipeline end-to-end
• yomikata — Japanese kanji disambiguation