[38;5;12m                                  [39m[38;2;255;187;0m[1m[4mAwesome MLOps [0m[38;5;14m[1m[4m![0m[38;2;255;187;0m[1m[4mAwesome[0m[38;5;14m[1m[4m (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)[0m[38;2;255;187;0m[1m[4m (https://github.com/sindresorhus/awesome)[0m

[38;5;12mA curated list of awesome MLOps tools.[39m

[38;5;12mInspired by [39m[38;5;14m[1mawesome-python[0m[38;5;12m (https://github.com/vinta/awesome-python).[39m

[38;5;12m- [39m[38;5;14m[1mAwesome MLOps[0m[38;5;12m (#awesome-mlops)[39m
[48;5;235m[38;5;249m- **AutoML** (#automl)[49m[39m[48;5;235m[38;5;249m                                              [49m[39m
[48;5;235m[38;5;249m- **CI/CD for Machine Learning** (#cicd-for-machine-learning)[49m[39m[48;5;235m[38;5;249m       [49m[39m
[48;5;235m[38;5;249m- **Cron Job Monitoring** (#cron-job-monitoring)[49m[39m[48;5;235m[38;5;249m                    [49m[39m
[48;5;235m[38;5;249m- **Data Catalog** (#data-catalog)[49m[39m[48;5;235m[38;5;249m                                  [49m[39m
[48;5;235m[38;5;249m- **Data Enrichment** (#data-enrichment)[49m[39m[48;5;235m[38;5;249m                            [49m[39m
[48;5;235m[38;5;249m- **Data Exploration** (#data-exploration)[49m[39m[48;5;235m[38;5;249m                          [49m[39m
[48;5;235m[38;5;249m- **Data Management** (#data-management)[49m[39m[48;5;235m[38;5;249m                            [49m[39m
[48;5;235m[38;5;249m- **Data Processing** (#data-processing)[49m[39m[48;5;235m[38;5;249m                            [49m[39m
[48;5;235m[38;5;249m- **Data Validation** (#data-validation)[49m[39m[48;5;235m[38;5;249m                            [49m[39m
[48;5;235m[38;5;249m- **Data Visualization** (#data-visualization)[49m[39m[48;5;235m[38;5;249m                      [49m[39m
[48;5;235m[38;5;249m- **Drift Detection** (#drift-detection)[49m[39m[48;5;235m[38;5;249m                            [49m[39m
[48;5;235m[38;5;249m- **Feature Engineering** (#feature-engineering)[49m[39m[48;5;235m[38;5;249m                    [49m[39m
[48;5;235m[38;5;249m- **Feature Store** (#feature-store)[49m[39m[48;5;235m[38;5;249m                                [49m[39m
[48;5;235m[38;5;249m- **Hyperparameter Tuning** (#hyperparameter-tuning)[49m[39m[48;5;235m[38;5;249m                [49m[39m
[48;5;235m[38;5;249m- **Knowledge Sharing** (#knowledge-sharing)[49m[39m[48;5;235m[38;5;249m                        [49m[39m
[48;5;235m[38;5;249m- **Machine Learning Platform** (#machine-learning-platform)[49m[39m[48;5;235m[38;5;249m        [49m[39m
[48;5;235m[38;5;249m- **Model Fairness and Privacy** (#model-fairness-and-privacy)[49m[39m[48;5;235m[38;5;249m      [49m[39m
[48;5;235m[38;5;249m- **Model Interpretability** (#model-interpretability)[49m[39m[48;5;235m[38;5;249m              [49m[39m
[48;5;235m[38;5;249m- **Model Lifecycle** (#model-lifecycle)[49m[39m[48;5;235m[38;5;249m                            [49m[39m
[48;5;235m[38;5;249m- **Model Serving** (#model-serving)[49m[39m[48;5;235m[38;5;249m                                [49m[39m
[48;5;235m[38;5;249m- **Model Testing & Validation** (#model-testing--validation)[49m[39m[48;5;235m[38;5;249m       [49m[39m
[48;5;235m[38;5;249m- **Optimization Tools** (#optimization-tools)[49m[39m[48;5;235m[38;5;249m                      [49m[39m
[48;5;235m[38;5;249m- **Simplification Tools** (#simplification-tools)[49m[39m[48;5;235m[38;5;249m                  [49m[39m
[48;5;235m[38;5;249m- **Visual Analysis and Debugging** (#visual-analysis-and-debugging)[49m[39m
[48;5;235m[38;5;249m- **Workflow Tools** (#workflow-tools)[49m[39m[48;5;235m[38;5;249m                              [49m[39m
[38;5;12m- [39m[38;5;14m[1mResources[0m[38;5;12m (#resources)[39m
[48;5;235m[38;5;249m- **Articles** (#articles)[49m[39m[48;5;235m[38;5;249m      [49m[39m
[48;5;235m[38;5;249m- **Books** (#books)[49m[39m[48;5;235m[38;5;249m            [49m[39m
[48;5;235m[38;5;249m- **Events** (#events)[49m[39m[48;5;235m[38;5;249m          [49m[39m
[48;5;235m[38;5;249m- **Other Lists** (#other-lists)[49m[39m
[48;5;235m[38;5;249m- **Podcasts** (#podcasts)[49m[39m[48;5;235m[38;5;249m      [49m[39m
[48;5;235m[38;5;249m- **Slack** (#slack)[49m[39m[48;5;235m[38;5;249m            [49m[39m
[48;5;235m[38;5;249m- **Websites** (#websites)[49m[39m[48;5;235m[38;5;249m      [49m[39m
[38;5;12m- [39m[38;5;14m[1mContributing[0m[38;5;12m (#contributing)[39m

[38;5;238m――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――[39m

[38;2;255;187;0m[4mAutoML[0m

[48;2;30;30;40m[38;5;13m[3mTools for performing AutoML.[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAutoGluon[0m[38;5;12m (https://github.com/awslabs/autogluon) - Automated machine learning for image, text, tabular, time-series, and multi-modal data.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAutoKeras[0m[38;5;12m (https://github.com/keras-team/autokeras) - AutoKeras goal is to make machine learning accessible for everyone.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAutoPyTorch[0m[38;5;12m (https://github.com/automl/Auto-PyTorch) - Automatic architecture search and hyperparameter optimization for PyTorch.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAutoSKLearn[0m[38;5;12m (https://github.com/automl/auto-sklearn) - Automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mEvalML[0m[38;5;12m (https://github.com/alteryx/evalml) - A library that builds, optimizes, and evaluates ML pipelines using domain-specific functions.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFLAML[0m[38;5;12m (https://github.com/microsoft/FLAML) - Finds accurate ML models automatically, efficiently and economically.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mH2O AutoML[0m[38;5;12m (https://h2o.ai/platform/h2o-automl) - Automates ML workflow, which includes automatic training and tuning of models.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMindsDB[0m[38;5;12m (https://github.com/mindsdb/mindsdb) - AI layer for databases that allows you to effortlessly develop, train and deploy ML models.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMLBox[0m[38;5;12m (https://github.com/AxeldeRomblay/MLBox) - MLBox is a powerful Automated Machine Learning python library.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mModel Search[0m[38;5;12m (https://github.com/google/model_search) - Framework that implements AutoML algorithms for model architecture search at scale.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNNI[0m[38;5;12m (https://github.com/microsoft/nni) - An open source AutoML toolkit for automate machine learning lifecycle.[39m

[38;2;255;187;0m[4mCI/CD for Machine Learning[0m

[48;2;30;30;40m[38;5;13m[3mTools for performing CI/CD for Machine Learning.[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mClearML[0m[38;5;12m (https://github.com/allegroai/clearml) - Auto-Magical CI/CD to streamline your ML workflow.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCML[0m[38;5;12m (https://github.com/iterative/cml) - Open-source library for implementing CI/CD in machine learning projects.[39m

[38;2;255;187;0m[4mCron Job Monitoring[0m

[48;2;30;30;40m[38;5;13m[3mTools for monitoring cron jobs (recurring jobs).[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCronitor[0m[38;5;12m (https://cronitor.io/cron-job-monitoring) - Monitor any cron job or scheduled task.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHealthchecksIO[0m[38;5;12m (https://healthchecks.io/) - Simple and effective cron job monitoring.[39m

[38;2;255;187;0m[4mData Catalog[0m

[48;2;30;30;40m[38;5;13m[3mTools for data cataloging.[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAmundsen[0m[38;5;12m (https://www.amundsen.io/) - Data discovery and metadata engine for improving the productivity when interacting with data.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mApache Atlas[0m[38;5;12m (https://atlas.apache.org) - Provides open metadata management and governance capabilities to build a data catalog.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCKAN[0m[38;5;12m (https://github.com/ckan/ckan) - Open-source DMS (data management system) for powering data hubs and data portals.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDataHub[0m[38;5;12m (https://github.com/linkedin/datahub) - LinkedIn's generalized metadata search & discovery tool.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMagda[0m[38;5;12m (https://github.com/magda-io/magda) - A federated, open-source data catalog for all your big data and small data.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMetacat[0m[38;5;12m (https://github.com/Netflix/metacat) - Unified metadata exploration API service for Hive, RDS, Teradata, Redshift, S3 and Cassandra.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mOpenMetadata[0m[38;5;12m (https://open-metadata.org/) - A Single place to discover, collaborate and get your data right.[39m

[38;2;255;187;0m[4mData Enrichment[0m

[48;2;30;30;40m[38;5;13m[3mTools and libraries for data enrichment.[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSnorkel[0m[38;5;12m (https://github.com/snorkel-team/snorkel) - A system for quickly generating training data with weak supervision.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mUpgini[0m[38;5;12m (https://github.com/upgini/upgini) - Enriches training datasets with features from public and community shared data sources.[39m

[38;2;255;187;0m[4mData Exploration[0m

[48;2;30;30;40m[38;5;13m[3mTools for performing data exploration.[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mApache Zeppelin[0m[38;5;12m (https://zeppelin.apache.org/) - Enables data-driven, interactive data analytics and collaborative documents.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBambooLib[0m[38;5;12m (https://github.com/tkrabel/bamboolib) -  An intuitive GUI for Pandas DataFrames.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDataPrep[0m[38;5;12m (https://github.com/sfu-db/dataprep) - Collect, clean and visualize your data in Python.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGoogle Colab[0m[38;5;12m (https://colab.research.google.com) - Hosted Jupyter notebook service that requires no setup to use.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mJupyter Notebook[0m[38;5;12m (https://jupyter.org/) - Web-based notebook environment for interactive computing.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mJupyterLab[0m[38;5;12m (https://jupyterlab.readthedocs.io) - The next-generation user interface for Project Jupyter.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mJupytext[0m[38;5;12m (https://github.com/mwouts/jupytext) - Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPandas Profiling[0m[38;5;12m (https://github.com/ydataai/pandas-profiling) - Create HTML profiling reports from pandas DataFrame objects.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPolynote[0m[38;5;12m (https://polynote.org/) - The polyglot notebook with first-class Scala support.[39m

[38;2;255;187;0m[4mData Management[0m

[48;2;30;30;40m[38;5;13m[3mTools for performing data management.[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mArrikto[0m[38;5;12m (https://www.arrikto.com/) - Dead simple, ultra fast storage for the hybrid Kubernetes world.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBlazingSQL[0m[38;5;12m (https://github.com/BlazingDB/blazingsql) - A lightweight, GPU accelerated, SQL engine for Python. Built on RAPIDS cuDF.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDelta Lake[0m[38;5;12m (https://github.com/delta-io/delta) - Storage layer that brings scalable, ACID transactions to Apache Spark and other engines.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDolt[0m[38;5;12m (https://github.com/dolthub/dolt) - SQL database that you can fork, clone, branch, merge, push and pull just like a git repository.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDud[0m[38;5;12m (https://github.com/kevin-hanselman/dud) - A lightweight CLI tool for versioning data alongside source code and building data pipelines.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDVC[0m[38;5;12m (https://dvc.org/) - Management and versioning of datasets and machine learning models.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGit LFS[0m[38;5;12m (https://git-lfs.github.com) - An open source Git extension for versioning large files.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHub[0m[38;5;12m (https://github.com/activeloopai/Hub) - A dataset format for creating, storing, and collaborating on AI datasets of any size.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mIntake[0m[38;5;12m (https://github.com/intake/intake) - A lightweight set of tools for loading and sharing data in data science projects.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mlakeFS[0m[38;5;12m (https://github.com/treeverse/lakeFS) - Repeatable, atomic and versioned data lake on top of object storage.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMarquez[0m[38;5;12m (https://github.com/MarquezProject/marquez) - Collect, aggregate, and visualize a data ecosystem's metadata.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMilvus[0m[38;5;12m (https://github.com/milvus-io/milvus/) - An open source embedding vector similarity search engine powered by Faiss, NMSLIB and Annoy.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPinecone[0m[38;5;12m (https://www.pinecone.io) - Managed and distributed vector similarity search used with a lightweight SDK.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mQdrant[0m[38;5;12m (https://github.com/qdrant/qdrant) - An open source vector similarity search engine with extended filtering support.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mQuilt[0m[38;5;12m (https://github.com/quiltdata/quilt) - A self-organizing data hub with S3 support.[39m

[38;2;255;187;0m[4mData Processing[0m

[48;2;30;30;40m[38;5;13m[3mTools related to data processing and data pipelines.[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAirflow[0m[38;5;12m (https://airflow.apache.org/) - Platform to programmatically author, schedule, and monitor workflows.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAzkaban[0m[38;5;12m (https://github.com/azkaban/azkaban) - Batch workflow job scheduler created at LinkedIn to run Hadoop jobs.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDagster[0m[38;5;12m (https://github.com/dagster-io/dagster) - A data orchestrator for machine learning, analytics, and ETL.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHadoop[0m[38;5;12m (https://hadoop.apache.org/) - Framework that allows for the distributed processing of large data sets across clusters.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mOpenRefine[0m[38;5;12m (https://github.com/OpenRefine/OpenRefine) - Power tool for working with messy data and improving it.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSpark[0m[38;5;12m (https://spark.apache.org/) - Unified analytics engine for large-scale data processing.[39m

[38;2;255;187;0m[4mData Validation[0m

[48;2;30;30;40m[38;5;13m[3mTools related to data validation.[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCerberus[0m[38;5;12m (https://github.com/pyeve/cerberus) - Lightweight, extensible data validation library for Python.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCleanlab[0m[38;5;12m (https://github.com/cleanlab/cleanlab) - Python library for data-centric AI and machine learning with messy, real-world data and labels.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGreat Expectations[0m[38;5;12m (https://greatexpectations.io) - A Python data validation framework that allows to test your data against datasets.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mJSON Schema[0m[38;5;12m (https://json-schema.org/) - A vocabulary that allows you to annotate and validate JSON documents.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTFDV[0m[38;5;12m (https://github.com/tensorflow/data-validation) - An library for exploring and validating machine learning data.[39m

[38;2;255;187;0m[4mData Visualization[0m

[48;2;30;30;40m[38;5;13m[3mTools for data visualization, reports and dashboards.[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCount[0m[38;5;12m (https://count.co) - SQL/drag-and-drop querying and visualisation tool based on notebooks.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDash[0m[38;5;12m (https://github.com/plotly/dash) - Analytical Web Apps for Python, R, Julia, and Jupyter.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mData Studio[0m[38;5;12m (https://datastudio.google.com) - Reporting solution for power users who want to go beyond the data and dashboards of GA.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFacets[0m[38;5;12m (https://github.com/PAIR-code/facets) - Visualizations for understanding and analyzing machine learning datasets.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGrafana[0m[38;5;12m (https://grafana.com/grafana/) -  Multi-platform open source analytics and interactive visualization web application.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLux[0m[38;5;12m (https://github.com/lux-org/lux) - Fast and easy data exploration by automating the visualization and data analysis process.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMetabase[0m[38;5;12m (https://www.metabase.com/) - The simplest, fastest way to get business intelligence and analytics to everyone.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mRedash[0m[38;5;12m (https://redash.io/) - Connect to any data source, easily visualize, dashboard and share your data.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSolidUI[0m[38;5;12m (https://github.com/CloudOrc/SolidUI) - AI-generated visualization prototyping and editing platform, support 2D and 3D models.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSuperset[0m[38;5;12m (https://superset.incubator.apache.org/) - Modern, enterprise-ready business intelligence web application.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTableau[0m[38;5;12m (https://www.tableau.com) - Powerful and fastest growing data visualization tool used in the business intelligence industry.[39m

[38;2;255;187;0m[4mDrift Detection[0m

[48;2;30;30;40m[38;5;13m[3mTools and libraries related to drift detection.[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAlibi Detect[0m[38;5;12m (https://github.com/SeldonIO/alibi-detect) - An open source Python library focused on outlier, adversarial and drift detection.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFrouros[0m[38;5;12m (https://github.com/IFCA/frouros) - An open source Python library for drift detection in machine learning systems.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTorchDrift[0m[38;5;12m (https://github.com/torchdrift/torchdrift/) - A data and concept drift library for PyTorch.[39m

[38;2;255;187;0m[4mFeature Engineering[0m

[48;2;30;30;40m[38;5;13m[3mTools and libraries related to feature engineering.[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFeature Engine[0m[38;5;12m (https://github.com/feature-engine/feature_engine) - Feature engineering package with SKlearn like functionality.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFeaturetools[0m[38;5;12m (https://github.com/alteryx/featuretools) - Python library for automated feature engineering.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTSFresh[0m[38;5;12m (https://github.com/blue-yonder/tsfresh) - Python library for automatic extraction of relevant features from time series.[39m

[38;2;255;187;0m[4mFeature Store[0m

[48;2;30;30;40m[38;5;13m[3mFeature store tools for data serving.[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mButterfree[0m[38;5;12m (https://github.com/quintoandar/butterfree) - A tool for building feature stores. Transform your raw data into beautiful features.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mByteHub[0m[38;5;12m (https://github.com/bytehub-ai/bytehub) - An easy-to-use feature store. Optimized for time-series data.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFeast[0m[38;5;12m (https://feast.dev/) - End-to-end open source feature store for machine learning.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFeathr[0m[38;5;12m (https://github.com/linkedin/feathr) - An enterprise-grade, high performance feature store.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFeatureform[0m[38;5;12m (https://github.com/featureform/featureform) - A Virtual Feature Store. Turn your existing data infrastructure into a feature store.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTecton[0m[38;5;12m (https://www.tecton.ai/) - A fully-managed feature platform built to orchestrate the complete lifecycle of features.[39m

[38;2;255;187;0m[4mHyperparameter Tuning[0m

[48;2;30;30;40m[38;5;13m[3mTools and libraries to perform hyperparameter tuning.[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAdvisor[0m[38;5;12m (https://github.com/tobegit3hub/advisor) - Open-source implementation of Google Vizier for hyper parameters tuning.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHyperas[0m[38;5;12m (https://github.com/maxpumperla/hyperas) - A very simple wrapper for convenient hyperparameter optimization.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHyperopt[0m[38;5;12m (https://github.com/hyperopt/hyperopt) - Distributed Asynchronous Hyperparameter Optimization in Python.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKatib[0m[38;5;12m (https://github.com/kubeflow/katib) - Kubernetes-based system for hyperparameter tuning and neural architecture search.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKerasTuner[0m[38;5;12m (https://github.com/keras-team/keras-tuner) - Easy-to-use, scalable hyperparameter optimization framework.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mOptuna[0m[38;5;12m (https://optuna.org/) - Open source hyperparameter optimization framework to automate hyperparameter search.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mScikit Optimize[0m[38;5;12m (https://github.com/scikit-optimize/scikit-optimize) - Simple and efficient library to minimize expensive and noisy black-box functions.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTalos[0m[38;5;12m (https://github.com/autonomio/talos) - Hyperparameter Optimization for TensorFlow, Keras and PyTorch.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTune[0m[38;5;12m (https://docs.ray.io/en/latest/tune.html) - Python library for experiment execution and hyperparameter tuning at any scale.[39m

[38;2;255;187;0m[4mKnowledge Sharing[0m

[48;2;30;30;40m[38;5;13m[3mTools for sharing knowledge to the entire team/company.[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKnowledge Repo[0m[38;5;12m (https://github.com/airbnb/knowledge-repo) - Knowledge sharing platform for data scientists and other technical professions.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKyso[0m[38;5;12m (https://kyso.io/) - One place for data insights so your entire team can learn from your data.[39m

[38;2;255;187;0m[4mMachine Learning Platform[0m

[48;2;30;30;40m[38;5;13m[3mComplete machine learning platform solutions.[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1maiWARE[0m[38;5;12m (https://www.veritone.com/aiware/aiware-os/) - aiWARE helps MLOps teams evaluate, deploy, integrate, scale & monitor ML models.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAlgorithmia[0m[38;5;12m (https://algorithmia.com/) - Securely govern your machine learning operations with a healthy ML lifecycle.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAllegro AI[0m[38;5;12m (https://allegro.ai/) - Transform ML/DL research into products. Faster.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBodywork[0m[38;5;12m (https://bodywork.readthedocs.io/en/latest/) - Deploys machine learning projects developed in Python, to Kubernetes.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCNVRG[0m[38;5;12m (https://cnvrg.io/) - An end-to-end machine learning platform to build and deploy AI models at scale.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDAGsHub[0m[38;5;12m (https://dagshub.com/) - A platform built on open source tools for data, model and pipeline management.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDataiku[0m[38;5;12m (https://www.dataiku.com/) - Platform democratizing access to data and enabling enterprises to build their own path to AI.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDataRobot[0m[38;5;12m (https://www.datarobot.com/) - AI platform that democratizes data science and automates the end-to-end ML at scale.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDomino[0m[38;5;12m (https://www.dominodatalab.com/) - One place for your data science tools, apps, results, models, and knowledge.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mEdge Impulse[0m[38;5;12m (https://edgeimpulse.com/) - Platform for creating, optimizing, and deploying AI/ML algorithms for edge devices.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1menvd[0m[38;5;12m (https://github.com/tensorchord/envd) - Machine learning development environment for data science and AI/ML engineering teams.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFedML[0m[38;5;12m (https://fedml.ai/) - Simplifies the workflow of federated learning anywhere at any scale.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGradient[0m[38;5;12m (https://gradient.paperspace.com/) - Multicloud CI/CD and MLOps platform for machine learning teams.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mH2O[0m[38;5;12m (https://www.h2o.ai/) - Open source leader in AI with a mission to democratize AI for everyone.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHopsworks[0m[38;5;12m (https://www.hopsworks.ai/) - Open-source platform for developing and operating machine learning models at scale.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mIguazio[0m[38;5;12m (https://www.iguazio.com/) - Data science platform that automates MLOps with end-to-end machine learning pipelines.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKatonic[0m[38;5;12m (https://katonic.ai/) - Automate your cycle of intelligence with Katonic MLOps Platform.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKnime[0m[38;5;12m (https://www.knime.com/) - Create and productionize data science using one easy and intuitive environment.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKubeflow[0m[38;5;12m (https://www.kubeflow.org/) - Making deployments of ML workflows on Kubernetes simple, portable and scalable.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLynxKite[0m[38;5;12m (https://lynxkite.com/) - A complete graph data science platform for very large graphs and other datasets.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mML Workspace[0m[38;5;12m (https://github.com/ml-tooling/ml-workspace) - All-in-one web-based IDE specialized for machine learning and data science.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMLReef[0m[38;5;12m (https://github.com/MLReef/mlreef) - Open source MLOps platform that helps you collaborate, reproduce and share your ML work.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mModzy[0m[38;5;12m (https://www.modzy.com/) - Deploy, connect, run, and monitor machine learning (ML) models in the enterprise and at the edge.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNeu.ro[0m[38;5;12m (https://neu.ro) - MLOps platform that integrates open-source and proprietary tools into client-oriented systems.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mOmnimizer[0m[38;5;12m (https://www.omniml.ai) - Simplifies and accelerates MLOps by bridging the gap between ML models and edge hardware.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPachyderm[0m[38;5;12m (https://www.pachyderm.com/) - Combines data lineage with end-to-end pipelines on Kubernetes, engineered for the enterprise.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPolyaxon[0m[38;5;12m (https://www.github.com/polyaxon/polyaxon/) - A platform for reproducible and scalable machine learning and deep learning on kubernetes.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSagemaker[0m[38;5;12m (https://aws.amazon.com/sagemaker/) - Fully managed service that provides the ability to build, train, and deploy ML models quickly.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSAS Viya[0m[38;5;12m (https://www.sas.com/en_us/software/viya.html) - Cloud native AI, analytic and data management platform that supports the analytics life cycle.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSematic[0m[38;5;12m (https://sematic.dev) - An open-source end-to-end pipelining tool to go from laptop prototype to cloud in no time.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSigOpt[0m[38;5;12m (https://sigopt.com/) - A platform that makes it easy to track runs, visualize training, and scale hyperparameter tuning.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTrueFoundry[0m[38;5;12m (https://www.truefoundry.com) - A Cloud-native MLOps Platform over Kubernetes to simplify training and serving of ML Models.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mValohai[0m[38;5;12m (https://valohai.com/) - Takes you from POC to production while managing the whole model lifecycle.[39m

[38;2;255;187;0m[4mModel Fairness and Privacy[0m

[48;2;30;30;40m[38;5;13m[3mTools for performing model fairness and privacy in production.[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAIF360[0m[38;5;12m (https://github.com/Trusted-AI/AIF360) - A comprehensive set of fairness metrics for datasets and machine learning models.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFairlearn[0m[38;5;12m (https://github.com/fairlearn/fairlearn) - A Python package to assess and improve fairness of machine learning models.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mOpacus[0m[38;5;12m (https://github.com/pytorch/opacus) - A library that enables training PyTorch models with differential privacy.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTensorFlow Privacy[0m[38;5;12m (https://github.com/tensorflow/privacy) - Library for training machine learning models with privacy for training data.[39m

[38;2;255;187;0m[4mModel Interpretability[0m

[48;2;30;30;40m[38;5;13m[3mTools for performing model interpretability/explainability.[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAlibi[0m[38;5;12m (https://github.com/SeldonIO/alibi) - Open-source Python library enabling ML model inspection and interpretation.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCaptum[0m[38;5;12m (https://github.com/pytorch/captum) - Model interpretability and understanding library for PyTorch.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mELI5[0m[38;5;12m (https://github.com/eli5-org/eli5) - Python package which helps to debug machine learning classifiers and explain their predictions.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mInterpretML[0m[38;5;12m (https://github.com/interpretml/interpret) - A toolkit to help understand models and enable responsible machine learning.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLIME[0m[38;5;12m (https://github.com/marcotcr/lime) - Explaining the predictions of any machine learning classifier.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLucid[0m[38;5;12m (https://github.com/tensorflow/lucid) - Collection of infrastructure and tools for research in neural network interpretability.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSAGE[0m[38;5;12m (https://github.com/iancovert/sage) - For calculating global feature importance using Shapley values.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSHAP[0m[38;5;12m (https://github.com/slundberg/shap) - A game theoretic approach to explain the output of any machine learning model.[39m

[38;2;255;187;0m[4mModel Lifecycle[0m

[48;2;30;30;40m[38;5;13m[3mTools for managing model lifecycle (tracking experiments, parameters and metrics).[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAim[0m[38;5;12m (https://github.com/aimhubio/aim) - A super-easy way to record, search and compare 1000s of ML training runs.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCascade[0m[38;5;12m (https://github.com/Oxid15/cascade) - Library of ML-Engineering tools for rapid prototyping and experiment management.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComet[0m[38;5;12m (https://github.com/comet-ml) - Track your datasets, code changes, experimentation history, and models.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGuild AI[0m[38;5;12m (https://guild.ai/) - Open source experiment tracking, pipeline automation, and hyperparameter tuning.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKeepsake[0m[38;5;12m (https://github.com/replicate/keepsake) - Version control for machine learning with support to Amazon S3 and Google Cloud Storage.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLosswise[0m[38;5;12m (https://losswise.com) - Makes it easy to track the progress of a machine learning project.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMlflow[0m[38;5;12m (https://mlflow.org/) - Open source platform for the machine learning lifecycle.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mModelDB[0m[38;5;12m (https://github.com/VertaAI/modeldb/) - Open source ML model versioning, metadata, and experiment management.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNeptune AI[0m[38;5;12m (https://neptune.ai/) - The most lightweight experiment management tool that fits any workflow.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSacred[0m[38;5;12m (https://github.com/IDSIA/sacred) - A tool to help you configure, organize, log and reproduce experiments.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mWeights and Biases[0m[38;5;12m (https://github.com/wandb/client) - A tool for visualizing and tracking your machine learning experiments.[39m

[38;2;255;187;0m[4mModel Serving[0m

[48;2;30;30;40m[38;5;13m[3mTools for serving models in production.[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBanana[0m[38;5;12m (https://banana.dev) - Host your ML inference code on serverless GPUs and integrate it into your app with one line of code.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBeam[0m[38;5;12m (https://beam.cloud) - Develop on serverless GPUs, deploy highly performant APIs, and rapidly prototype ML models.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBentoML[0m[38;5;12m (https://github.com/bentoml/BentoML) - Open-source platform for high-performance ML model serving.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBudgetML[0m[38;5;12m (https://github.com/ebhy/budgetml) - Deploy a ML inference service on a budget in less than 10 lines of code.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCog[0m[38;5;12m (https://github.com/replicate/cog) - Open-source tool that lets you package ML models in a standard, production-ready container.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCortex[0m[38;5;12m (https://www.cortex.dev/) - Machine learning model serving infrastructure.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGeniusrise[0m[38;5;12m (https://docs.geniusrise.ai) - Host inference APIs, bulk inference and fine tune text, vision, audio and multi-modal models.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGradio[0m[38;5;12m (https://github.com/gradio-app/gradio) - Create customizable UI components around your models.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGraphPipe[0m[38;5;12m (https://oracle.github.io/graphpipe) - Machine learning model deployment made simple.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHydrosphere[0m[38;5;12m (https://github.com/Hydrospheredata/hydro-serving) - Platform for deploying your Machine Learning to production.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKFServing[0m[38;5;12m (https://github.com/kubeflow/kfserving) - Kubernetes custom resource definition for serving ML models on arbitrary frameworks.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLocalAI[0m[38;5;12m (https://github.com/mudler/LocalAI) - Drop-in replacement REST API that’s compatible with OpenAI API specifications for inferencing.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMerlin[0m[38;5;12m (https://github.com/gojek/merlin) - A platform for deploying and serving machine learning models.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMLEM[0m[38;5;12m (https://github.com/iterative/mlem) - Version and deploy your ML models following GitOps principles.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mOpyrator[0m[38;5;12m (https://github.com/ml-tooling/opyrator) - Turns your ML code into microservices with web API, interactive GUI, and more.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPredictionIO[0m[38;5;12m (https://github.com/apache/predictionio) - Event collection, deployment of algorithms, evaluation, querying predictive results via APIs.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mQuix[0m[38;5;12m (https://quix.io) - Serverless platform for processing data streams in real-time with machine learning models.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mRune[0m[38;5;12m (https://github.com/hotg-ai/rune) - Provides containers to encapsulate and deploy EdgeML pipelines and applications.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSeldon[0m[38;5;12m (https://www.seldon.io/) - Take your ML projects from POC to production with maximum efficiency and minimal risk.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mStreamlit[0m[38;5;12m (https://github.com/streamlit/streamlit) - Lets you create apps for your ML projects with deceptively simple Python scripts.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTensorFlow Serving[0m[38;5;12m (https://www.tensorflow.org/tfx/guide/serving) - Flexible, high-performance serving system for ML models, designed for production.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTorchServe[0m[38;5;12m (https://github.com/pytorch/serve) - A flexible and easy to use tool for serving PyTorch models.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTriton Inference Server[0m[38;5;12m (https://github.com/triton-inference-server/server) - Provides an optimized cloud and edge inferencing solution.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mVespa[0m[38;5;12m (https://github.com/vespa-engine/vespa) - Store, search, organize and make machine-learned inferences over big data at serving time.[39m

[38;2;255;187;0m[4mModel Testing & Validation[0m

[48;2;30;30;40m[38;5;13m[3mTools for testing and validating models.[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDeepchecks[0m[38;5;12m (https://github.com/deepchecks/deepchecks) - Open-source package for validating ML models & data, with various checks and suites.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mStarwhale[0m[38;5;12m (https://github.com/star-whale/starwhale) - An MLOps/LLMOps platform for model building, evaluation, and fine-tuning.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTrubrics[0m[38;5;12m (https://github.com/trubrics/trubrics-sdk) - Validate machine learning with data science and domain expert feedback.[39m

[38;2;255;187;0m[4mOptimization Tools[0m

[48;2;30;30;40m[38;5;13m[3mOptimization tools related to model scalability in production.[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAccelerate[0m[38;5;12m (https://github.com/huggingface/accelerate) - A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDask[0m[38;5;12m (https://dask.org/) - Provides advanced parallelism for analytics, enabling performance at scale for the tools you love.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDeepSpeed[0m[38;5;12m (https://github.com/microsoft/DeepSpeed) - Deep learning optimization library that makes distributed training easy, efficient, and effective.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFiber[0m[38;5;12m (https://uber.github.io/fiber/) - Python distributed computing library for modern computer clusters.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHorovod[0m[38;5;12m (https://github.com/horovod/horovod) - Distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMahout[0m[38;5;12m (https://mahout.apache.org/) - Distributed linear algebra framework and mathematically expressive Scala DSL.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMLlib[0m[38;5;12m (https://spark.apache.org/mllib/) - Apache Spark's scalable machine learning library.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mModin[0m[38;5;12m (https://github.com/modin-project/modin) - Speed up your Pandas workflows by changing a single line of code.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNebullvm[0m[38;5;12m (https://github.com/nebuly-ai/nebullvm) - Easy-to-use library to boost AI inference.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNos[0m[38;5;12m (https://github.com/nebuly-ai/nos) - Open-source module for running AI workloads on Kubernetes in an optimized way.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPetastorm[0m[38;5;12m (https://github.com/uber/petastorm) - Enables single machine or distributed training and evaluation of deep learning models.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mRapids[0m[38;5;12m (https://rapids.ai/index.html) - Gives the ability to execute end-to-end data science and analytics pipelines entirely on GPUs.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mRay[0m[38;5;12m (https://github.com/ray-project/ray) - Fast and simple framework for building and running distributed applications.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSinga[0m[38;5;12m (http://singa.apache.org/en/index.html) - Apache top level project, focusing on distributed training of DL and ML models.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTpot[0m[38;5;12m (https://github.com/EpistasisLab/tpot) - Automated ML tool that optimizes machine learning pipelines using genetic programming.[39m

[38;2;255;187;0m[4mSimplification Tools[0m

[48;2;30;30;40m[38;5;13m[3mTools related to machine learning simplification and standardization.[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mChassis[0m[38;5;12m (https://chassisml.io) - Turns models into ML-friendly containers that run just about anywhere.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHermione[0m[38;5;12m (https://github.com/a3data/hermione) - Help Data Scientists on setting up more organized codes, in a quicker and simpler way.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHydra[0m[38;5;12m (https://github.com/facebookresearch/hydra) - A framework for elegantly configuring complex applications.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKoalas[0m[38;5;12m (https://github.com/databricks/koalas) - Pandas API on Apache Spark. Makes data scientists more productive when interacting with big data.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLudwig[0m[38;5;12m (https://github.com/uber/ludwig) - Allows users to train and test deep learning models without the need to write code.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMLNotify[0m[38;5;12m (https://github.com/aporia-ai/mlnotify) - No need to keep checking your training, just one import line and you'll know the second it's done.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPyCaret[0m[38;5;12m (https://pycaret.org/) - Open source, low-code machine learning library in Python.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSagify[0m[38;5;12m (https://github.com/Kenza-AI/sagify) - A CLI utility to train and deploy ML/DL models on AWS SageMaker.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSoopervisor[0m[38;5;12m (https://github.com/ploomber/soopervisor) - Export ML projects to Kubernetes (Argo workflows), Airflow, AWS Batch, and SLURM.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSoorgeon[0m[38;5;12m (https://github.com/ploomber/soorgeon) - Convert monolithic Jupyter notebooks into maintainable pipelines.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTrainGenerator[0m[38;5;12m (https://github.com/jrieke/traingenerator) - A web app to generate template code for machine learning.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTuri Create[0m[38;5;12m (https://github.com/apple/turicreate) - Simplifies the development of custom machine learning models.[39m

[38;2;255;187;0m[4mVisual Analysis and Debugging[0m

[48;2;30;30;40m[38;5;13m[3mTools for performing visual analysis and debugging of ML/DL models.[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAporia[0m[38;5;12m (https://www.aporia.com/) - Observability with customized monitoring and explainability for ML models.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mArize[0m[38;5;12m (https://www.arize.com/) - A free end-to-end ML observability and model monitoring platform.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCometLLM[0m[38;5;12m (https://github.com/comet-ml/comet-llm) - Track, visualize, and evaluate your LLM prompts and chains in one easy-to-use UI.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mEvidently[0m[38;5;12m (https://github.com/evidentlyai/evidently) - Interactive reports to analyze ML models during validation or production monitoring.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFiddler[0m[38;5;12m (https://www.fiddler.ai/) - Monitor, explain, and analyze your AI in production.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mManifold[0m[38;5;12m (https://github.com/uber/manifold) - A model-agnostic visual debugging tool for machine learning.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNannyML[0m[38;5;12m (https://github.com/NannyML/nannyml) - Algorithm capable of fully capturing the impact of data drift on performance.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNetron[0m[38;5;12m (https://github.com/lutzroeder/netron) - Visualizer for neural network, deep learning, and machine learning models.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPhoenix[0m[38;5;12m (https://phoenix.arize.com) - MLOps in a Notebook for troubleshooting and fine-tuning generative LLM, CV, and tabular models.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSuperwise[0m[38;5;12m (https://www.superwise.ai) - Fully automated, enterprise-grade model observability in a self-service SaaS platform.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mWhylogs[0m[38;5;12m (https://github.com/whylabs/whylogs) - The open source standard for data logging. Enables ML monitoring and observability.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mYellowbrick[0m[38;5;12m (https://github.com/DistrictDataLabs/yellowbrick) - Visual analysis and diagnostic tools to facilitate machine learning model selection.[39m

[38;2;255;187;0m[4mWorkflow Tools[0m

[48;2;30;30;40m[38;5;13m[3mTools and frameworks to create workflows or pipelines in the machine learning context.[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mArgo[0m[38;5;12m (https://github.com/argoproj/argo) - Open source container-native workflow engine for orchestrating parallel jobs on Kubernetes.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAutomate Studio[0m[38;5;12m (https://www.veritone.com/applications/automate-studio/) - Rapidly build & deploy AI-powered workflows.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCouler[0m[38;5;12m (https://github.com/couler-proj/couler) - Unified interface for constructing and managing workflows on different workflow engines.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mdstack[0m[38;5;12m (https://github.com/dstackai/dstack) - An open-core tool to automate data and training workflows.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFlyte[0m[38;5;12m (https://flyte.org/) - Easy to create concurrent, scalable, and maintainable workflows for machine learning.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHamilton[0m[38;5;12m (https://github.com/dagworks-inc/hamilton) - A scalable general purpose micro-framework for defining dataflows.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKale[0m[38;5;12m (https://github.com/kubeflow-kale/kale) - Aims at simplifying the Data Science experience of deploying Kubeflow Pipelines workflows.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKedro[0m[38;5;12m (https://github.com/quantumblacklabs/kedro) - Library that implements software engineering best-practice for data and ML pipelines.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLuigi[0m[38;5;12m (https://github.com/spotify/luigi) - Python module that helps you build complex pipelines of batch jobs.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMetaflow[0m[38;5;12m (https://metaflow.org/) - Human-friendly lib that helps scientists and engineers build and manage data science projects.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMLRun[0m[38;5;12m (https://github.com/mlrun/mlrun) - Generic mechanism for data scientists to build, run, and monitor ML tasks and pipelines.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mOrchest[0m[38;5;12m (https://github.com/orchest/orchest/) - Visual pipeline editor and workflow orchestrator with an easy to use UI and based on Kubernetes.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPloomber[0m[38;5;12m (https://github.com/ploomber/ploomber) - Write maintainable, production-ready pipelines. Develop locally, deploy to the cloud.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPrefect[0m[38;5;12m (https://docs.prefect.io/) - A workflow management system, designed for modern infrastructure.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mVDP[0m[38;5;12m (https://github.com/instill-ai/vdp) - An open-source tool to seamlessly integrate AI for unstructured data into the modern data stack.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mZenML[0m[38;5;12m (https://github.com/maiot-io/zenml) - An extensible open-source MLOps framework to create reproducible pipelines.[39m

[38;5;238m――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――[39m

[38;5;12m                                                                                                                 [39m[38;2;255;187;0m[1m[4mResources[0m

[38;5;12mWhere to discover new tools and discuss about existing ones.[39m

[38;2;255;187;0m[4mArticles[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mA Tour of End-to-End Machine Learning Platforms[0m[38;5;12m (https://databaseline.tech/a-tour-of-end-to-end-ml-platforms/) (Databaseline)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mContinuous Delivery for Machine Learning[0m[38;5;12m (https://martinfowler.com/articles/cd4ml.html) (Martin Fowler)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDelivering on the Vision of MLOps: A maturity-based approach[0m[38;5;12m (https://azure.microsoft.com/mediahandler/files/resourcefiles/gigaom-Delivering-on-the-Vision-of-MLOps/Delivering%20on%20the%20Vision%20of%20MLOps.pdf) (GigaOm)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMachine Learning Operations (MLOps): Overview, Definition, and Architecture[0m[38;5;12m (https://arxiv.org/abs/2205.02302) (arXiv)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMLOps: Continuous delivery and automation pipelines in machine learning[0m[38;5;12m (https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning) (Google)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMLOps: Machine Learning as an Engineering Discipline[0m[38;5;12m (https://towardsdatascience.com/ml-ops-machine-learning-as-an-engineering-discipline-b86ca4874a3f) (Medium)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mRules of Machine Learning: Best Practices for ML Engineering[0m[38;5;12m (https://developers.google.com/machine-learning/guides/rules-of-ml) (Google)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mThe ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction[0m[38;5;12m (https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/aad9f93b86b7addfea4c419b9100c6cdd26cacea.pdf) (Google)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mWhat Is MLOps?[0m[38;5;12m (https://blogs.nvidia.com/blog/2020/09/03/what-is-mlops/) (NVIDIA)[39m

[38;2;255;187;0m[4mBooks[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBeginning MLOps with MLFlow[0m[38;5;12m (https://www.amazon.com/Beginning-MLOps-MLFlow-SageMaker-Microsoft/dp/1484265483) (Apress)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBuilding Machine Learning Pipelines[0m[38;5;12m (https://www.oreilly.com/library/view/building-machine-learning/9781492053187) (O'Reilly)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBuilding Machine Learning Powered Applications[0m[38;5;12m (https://www.oreilly.com/library/view/building-machine-learning/9781492045106) (O'Reilly)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDeep Learning in Production[0m[38;5;12m (https://www.amazon.com/gp/product/6180033773) (AI Summer)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDesigning Machine Learning Systems[0m[38;5;12m (https://www.oreilly.com/library/view/designing-machine-learning/9781098107956) (O'Reilly)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mEngineering MLOps[0m[38;5;12m (https://www.packtpub.com/product/engineering-mlops/9781800562882) (Packt)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mImplementing MLOps in the Enterprise[0m[38;5;12m (https://www.oreilly.com/library/view/implementing-mlops-in/9781098136574) (O'Reilly)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mIntroducing MLOps[0m[38;5;12m (https://www.oreilly.com/library/view/introducing-mlops/9781492083283) (O'Reilly)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKubeflow for Machine Learning[0m[38;5;12m (https://www.oreilly.com/library/view/kubeflow-for-machine/9781492050117) (O'Reilly)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKubeflow Operations Guide[0m[38;5;12m (https://www.oreilly.com/library/view/kubeflow-operations-guide/9781492053262) (O'Reilly)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMachine Learning Design Patterns[0m[38;5;12m (https://www.oreilly.com/library/view/machine-learning-design/9781098115777) (O'Reilly)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMachine Learning Engineering in Action[0m[38;5;12m (https://www.manning.com/books/machine-learning-engineering-in-action) (Manning)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mML Ops: Operationalizing Data Science[0m[38;5;12m (https://www.oreilly.com/library/view/ml-ops-operationalizing/9781492074663) (O'Reilly)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMLOps Engineering at Scale[0m[38;5;12m (https://www.manning.com/books/mlops-engineering-at-scale) (Manning)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMLOps Lifecycle Toolkit[0m[38;5;12m (https://link.springer.com/book/10.1007/978-1-4842-9642-4) (Apress)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPractical Deep Learning at Scale with MLflow[0m[38;5;12m (https://www.packtpub.com/product/practical-deep-learning-at-scale-with-mlflow/9781803241333) (Packt)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPractical MLOps[0m[38;5;12m (https://www.oreilly.com/library/view/practical-mlops/9781098103002) (O'Reilly)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mProduction-Ready Applied Deep Learning[0m[38;5;12m (https://www.packtpub.com/product/production-ready-applied-deep-learning/9781803243665) (Packt)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mReliable Machine Learning[0m[38;5;12m (https://www.oreilly.com/library/view/reliable-machine-learning/9781098106218) (O'Reilly)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mThe Machine Learning Solutions Architect Handbook[0m[38;5;12m (https://www.packtpub.com/product/the-machine-learning-solutions-architect-handbook/9781801072168) (Packt)[39m

[38;2;255;187;0m[4mEvents[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mapply() - The ML data engineering conference[0m[38;5;12m (https://www.applyconf.com/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMLOps Conference - Keynotes and Panels[0m[38;5;12m (https://www.youtube.com/playlist?list=PLH8M0UOY0uy6d_n3vEQe6J_gRBUrISF9m)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMLOps World: Machine Learning in Production Conference[0m[38;5;12m (https://mlopsworld.com/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNormConf - The Normcore Tech Conference[0m[38;5;12m (https://normconf.com/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mStanford MLSys Seminar Series[0m[38;5;12m (https://mlsys.stanford.edu/)[39m

[38;2;255;187;0m[4mOther Lists[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mApplied ML[0m[38;5;12m (https://github.com/eugeneyan/applied-ml)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAwesome AutoML Papers[0m[38;5;12m (https://github.com/hibayesian/awesome-automl-papers)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAwesome AutoML[0m[38;5;12m (https://github.com/windmaple/awesome-AutoML)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAwesome Data Science[0m[38;5;12m (https://github.com/academic/awesome-datascience)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAwesome DataOps[0m[38;5;12m (https://github.com/kelvins/awesome-dataops)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAwesome Deep Learning[0m[38;5;12m (https://github.com/ChristosChristofidis/awesome-deep-learning)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAwesome Game Datasets[0m[38;5;12m (https://github.com/leomaurodesenv/game-datasets) (includes AI content)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAwesome Machine Learning[0m[38;5;12m (https://github.com/josephmisiti/awesome-machine-learning)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAwesome MLOps[0m[38;5;12m (https://github.com/visenger/awesome-mlops)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAwesome Production Machine Learning[0m[38;5;12m (https://github.com/EthicalML/awesome-production-machine-learning)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAwesome Python[0m[38;5;12m (https://github.com/vinta/awesome-python)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDeep Learning in Production[0m[38;5;12m (https://github.com/ahkarami/Deep-Learning-in-Production)[39m

[38;2;255;187;0m[4mPodcasts[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHow AI Built This[0m[38;5;12m (https://how-ai-built-this.captivate.fm/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKubernetes Podcast from Google[0m[38;5;12m (https://kubernetespodcast.com/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMachine Learning – Software Engineering Daily[0m[38;5;12m (https://podcasts.google.com/?feed=aHR0cHM6Ly9zb2Z0d2FyZWVuZ2luZWVyaW5nZGFpbHkuY29tL2NhdGVnb3J5L21hY2hpbmUtbGVhcm5pbmcvZmVlZC8)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMLOps.community[0m[38;5;12m (https://podcasts.google.com/?feed=aHR0cHM6Ly9hbmNob3IuZm0vcy8xNzRjYjFiOC9wb2RjYXN0L3Jzcw)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPipeline Conversation[0m[38;5;12m (https://podcast.zenml.io/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPractical AI: Machine Learning, Data Science[0m[38;5;12m (https://changelog.com/practicalai)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mThis Week in Machine Learning & AI[0m[38;5;12m (https://twimlai.com/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTrue ML Talks[0m[38;5;12m (https://www.youtube.com/playlist?list=PL4-eEhdXDO5F9Myvh41EeUh7oCgzqFRGk)[39m

[38;2;255;187;0m[4mSlack[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKubeflow Workspace[0m[38;5;12m (https://kubeflow.slack.com/#/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMLOps Community Wokspace[0m[38;5;12m (https://mlops-community.slack.com)[39m

[38;2;255;187;0m[4mWebsites[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFeature Stores for ML[0m[38;5;12m (http://featurestore.org/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMade with ML[0m[38;5;12m (https://github.com/GokuMohandas/Made-With-ML)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mML-Ops[0m[38;5;12m (https://ml-ops.org/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMLOps Community[0m[38;5;12m (https://mlops.community/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMLOps Guide[0m[38;5;12m (https://mlops-guide.github.io/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMLOps Now[0m[38;5;12m (https://mlopsnow.com)[39m

[38;5;12m                                                                                                                [39m[38;2;255;187;0m[1m[4mContributing[0m

[38;5;12mAll contributions are welcome! Please take a look at the [39m[38;5;14m[1mcontribution guidelines[0m[38;5;12m (https://github.com/kelvins/awesome-mlops/blob/main/CONTRIBUTING.md) first.[39m
