Pycaret

PyCaret 3.3.2 on Ubuntu 24.04 with Free Maintenance Support by kCloud

Accelerate your machine learning workflow with PyCaret on Ubuntu 24.04, a low-code, fully open-source machine learning library designed for rapid prototyping and production deployment. Available through the kCloud Marketplace, PyCaret enables data scientists, analysts, and developers to experiment, train, and deploy models quickly without writing hundreds of lines of repetitive code.

With kCloudHubs maintenance support, your PyCaret environment delivers enterprise-grade reliability, including regular updates, expert troubleshooting, and smooth production integration on kCloud.

Low-Code Machine Learning for Rapid Prototyping

• Unified API for All ML Tasks: Work easily with classification, regression, clustering, anomaly detection, NLP, and time-series forecasting using a single, consistent API.
• Minimal Setup: Runs directly on Ubuntu 24.04, reducing configuration time and enabling quick deployment to cloud or on-premise environments.
• Low-Code Workflow: Replace hundreds of lines of traditional ML code with simple function calls for data preparation, model training, evaluation, and deployment.

End-to-End ML Lifecycle Management

• Setup to Deployment: Manages the entire machine learning lifecycle, from data import and cleaning to model selection, tuning, and production deployment.

Built on Powerful ML Libraries

• Leverages scikit-learn, XGBoost, LightGBM, CatBoost, and other high-performance machine learning libraries.
• Ensures fast, reliable, and production-ready model training and evaluation.
• Provides access to advanced ML algorithms without complex or verbose coding.

Enterprise-Grade Reliability with kCloudHubs

• Optional kCloudHubs maintenance support provides regular updates, expert troubleshooting, and operational assistance.
• Ideal for production deployments where uptime, performance, and reliability are critical.

Benefits of PyCaret on kCloud

• Rapid Prototyping: Experiment and iterate faster than traditional, code-heavy machine learning pipelines.
• Simplified Deployment: Deploy models easily from Ubuntu 24.04 LTS to production environments on kCloud.
• Low-Code Efficiency: Focus more on insights and analysis rather than repetitive coding tasks.
• All-in-One ML Framework: Use a single framework for classification, regression, clustering, NLP, anomaly detection, and time-series analysis.
• Cloud-Ready: Integrates smoothly with kCloud infrastructure to support scalable, secure, and distributed machine learning workflows.