Machine Learning with CrateDB¶
Machine learning applications and frameworks which can be used together with CrateDB.
Learn how to integrate CrateDB with machine learning frameworks and tools, for MLOps and Vector database operations.
MLflow¶
MLflow is an open source platform to manage the whole ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry.
The MLflow adapter for CrateDB, available through the mlflow-cratedb package, provides support to use CrateDB as a storage database for the MLflow Tracking subsystem, which is about recording and querying experiments, across code, data, config, and results.
PyCaret¶
PyCaret is an open-source, low-code machine learning library for Python that automates machine learning workflows.
It is a high-level interface and AutoML wrapper on top of your loved machine learning libraries like scikit-learn, xgboost, ray, lightgbm, and many more. PyCaret provides a universal interface to utilize these libraries without needing to know the details of the underlying model architectures and parameters.
scikit-learn¶
scikit-learn
Machine Learning in Python.
Simple and efficient tools for predictive data analysis
Accessible to everybody, and reusable in various contexts
Built on NumPy, SciPy, and matplotlib
pandas
The open source data analysis and manipulation tool.
Pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series.
Project Jupyter
Interactive computing across all programming languages.
JupyterLab is the latest web-based interactive development environment for notebooks, code, and data. Its flexible interface allows users to configure and arrange workflows in data science, scientific computing, computational journalism, and machine learning. A modular design invites extensions to expand and enrich functionality.