The Best NannyML Alternatives in 2026

Evidently evaluates, tests, and monitors machine learning systems and data pipelines.

Deepchecks tests and validates machine learning models and data, covering workflows from research through production.

Eurybia tracks model drift and validates data during machine learning model deployment.

Pachyderm provides data-focused pipelines and version control for datasets and related workflows.

igel lets users train, test, and use machine learning models without writing code.

Adaptive Reliability Layer decides when to retrain a model based on drift signals and delayed labels.

Edge Impulse provides MLOps workflows for developing, deploying, and monitoring edge and physical AI models.

OpenLLMetry provides OpenTelemetry-based observability for generative AI and large language model applications.

Kedro helps create reproducible, maintainable, and modular data science and data engineering pipelines.

PySyft supports data science on data that remains on another person’s server.

Open-source version control system for Data Science and Machine Learning projects. Git-like experience to organize your data, models, and experiments.

Radicalbit AI Monitoring provides a solution for monitoring machine learning models in production.

TPOT uses genetic programming to optimize machine learning pipelines in Python.

The ChefBoost Python framework supports decision trees such as ID3, C4.5, CART, CHAID, regression trees, and ensemble methods.

FLAML provides a fast Python library for automated machine learning and tuning.

FlexML offers an easy to use and flexible automated machine learning library for Python.

Maze provides an applied reinforcement learning framework for developing projects in Python.

Butterfree provides tools for building feature stores and transforming raw data.

Feathr provides enterprises with a platform for data engineering and AI workflows.

Featureform turns existing data infrastructure into a virtual feature store.

PyTorch Lightning supports pretraining and fine tuning AI models across GPU setups.

Ludwig provides a low code framework for building custom LLMs, neural networks, and other AI models.

Kubeflow Pipelines orchestrates and manages machine learning workflows on Kubeflow.

Ray provides an AI compute engine with a distributed runtime and libraries for machine learning workloads.