DS2.ai

Automate ML Pipelines and Prescriptive Analytics with MLOps

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Description

DS2.ai provides a comprehensive environment for data scientists and machine learning professionals to manage the entire AI lifecycle. The platform facilitates data upload, offers both manual and automated annotation tools for various data types including tabular, text, and images, and supports training machine learning and deep learning models using popular frameworks like PyTorch, TensorFlow, and XGBoost. Users can leverage AutoML for accelerated or high-accuracy training, opt for manual configuration, or generate code for greater control.

Beyond training, DS2.ai enables the deployment and monitoring of both internally trained and external models, integrating them into an active learning loop for continuous improvement. It features capabilities for prescriptive analytics powered by explainable AI (XAI) and allows interaction via a user interface, a robust Python SDK, or API calls. The platform also simplifies the use of state-of-the-art pre-trained models for tasks like OCR, translation, and text generation, providing a versatile solution for developing and operationalizing AI.

Key Features

MLOps Automation: Streamlines the entire machine learning lifecycle through automated pipelines.
Active Learning Process: Continuously improves model accuracy through a Learn-Analyze-Deploy-Collect cycle.
Data Annotation Tools: Supports manual and auto-labeling for tabular, text, image, and recommender system data.
Flexible Model Training: Offers AutoML, manual configuration (PyTorch, TensorFlow, XGBoost), and code generation options.
SOTA Model Integration: Easy access to pre-trained models for OCR, GPT, STT, TTS, Text-to-Image, Translations, etc.
Model Deployment & Monitoring: Deploy internal or external models with endpoint management and monitoring.
Prescriptive Analytics: Generates insights using explainable AI (XAI).
API & Python SDK: Provides programmatic access and control over platform features.
Model Verification: Includes tools to verify AI model performance.
Jupyter Integration: Manage Jupyter environments directly within the platform.

Use Cases

Developing custom machine learning models.
Automating ML workflows (MLOps).
Annotating data for AI training.
Deploying and monitoring AI models in production.
Performing prescriptive analytics.
Utilizing state-of-the-art AI models for specific tasks (e.g., OCR, translation).
Implementing active learning strategies.