MLops Tools

MLOps tools link 

• CI/CD For Machine learning: ClearML, CML, Gitlab
• CronJob Monitoring: Cronitor, HealthchecksIO 
• Data Exploration: Apache Zeppelin, BambooLib, Google Colab, Jupyter Notebook, JupyterLab
• Data Management: DVC, Arrikto, BlazingSQL, Delta Lake, Dolt, DVC, Git LFS
• Data Processing: AirFlow, Hadoop
• Data Validation: Cerberus, Great Expectations
• Data Visualization: SuperSet, Tableau, Facet, Dash
• Feature Engineering: Featuretools, TSFresh
• Feature Store: Butterfree, ByteHub, Feast, Tecton
• Hyperparameter Tuning: Hyperas, Hyperopt, Kabit, KerasTuner, Optuna, Scikit Optimize, Optuna
• Machine Learning Platform: SageMaker, Kubeflow, H2O, MLReef, algorithmia, DataRobot, DAGsHub
• Model FairNess: AI 360, FairLearn, Opacus
• Model Interpretability: Alibi, Captum, ELI5, InterpretML, LIME, Lucid, SAGE, SHAP, Skater
• Model LifeCycle: MLflow, NeptuneAI, Comet, Keepsake, ModelDB, Weights and Biases
• Model Serving: BentoML, Tensorflow Serving, KFServing, SeldonCore, Streamlit, TorchServce, Gradio, Graphpipe, Hydrosphereout
• Model Testing and Validation: DeepChecks
• Optimization Tools: Dask, DeepSpeed, Horovod, Tpot, Ray Rapids
• Simplification Tools for ML: Pycaret, Hermione, Hydra, Koalas, TuriCreate(apple), TrainGenerator
• Visual Analysis and Debugging: Aporia, Evidently, Yellowricks, Netron, Fiddler, Manifold
• Workflow Tools: MLRun,Flyte, Metaflow, Ploomber, ZenML, Kedro

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Big Picture - Different phases of Model Development

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Overall Landscape - Monitor, Manage, Retrain, Tools Stack

MLOps vs Data Engineering

I always had a mixed opinion of different tasks in ML vs Data Engineering overlapping. This article I align to the views

MLOps is Mostly Data Engineering.

Data in different forms and the reporting aspects

• Transaction Data
• BI Reports
• ML Features
• ML Dashboards
• Everything operates on same data. 

Key Questions from article

• How different is the observability of model quality metrics like drift different to any product-related monitoring? 
• In product we keep monitoring the performance of our features, do people engage with them in the way we expect?

Cycle of Activities

Keep Exploring!!!