Baseten Wiki, Company Profile, Founder, Net Worth, Logo, Establishment, Gallery and more
Baseten is an ML application builder for data science and machine learning teams. Baseten makes going from machine learning models to production-grade applications fast and easy. With Baseten, data science and machine learning teams can ship full-stack ML applications without backend, frontend, or MLOps knowledge, enabling faster, smarter decision-making in their organizations.
Baseten company has its headquarters in San Fransisco, CA. The company builds delightful products for data science and machine learning teams to deliver their best work. The company’s mission is to make machine learning accessible to every organization. The company is backed by world-class investors.
|Date of Establishment||2019|
|Establishment Place||San Francisco, CA|
|Registered Address||San Francisco, CA USA|
|Net Worth||Data not available|
|Telephone no.||Data not available|
|Data not available|
Baseten is the ML Application builder for data scientists. Baseten makes it simple to serve your machine learning models, integrate with custom business logic, and design powerful web apps for business users. Baseten is currently free to use. Its specialties are machine learning, developer tools, software engineering and artificial intelligence.
The data labeling app showcases how you can collect several, detailed labels for a given image while streamlining the workflow for your labeling team. Labels are saved in Baseten Postgres database so they can be used in model training.
Image Generation combines multiple machine learning processes to turn a text prompt into an original image. It is able to position objects in space, match styles, approach photorealism, and image generation models like OpenAI’s Dall-E 2 and Google’s Imagen represent a cutting-edge frontier in AI research.
Deploy XGBoost Model
You can serve an XGBoost model behind a REST API endpoint. The app also has Deploy Hugging face model and Deploy scikit-learn model, Deploy Keras model, Deploy fast.ai model.
Human in the loop workflows
The Baseten app uses a zero-shot classification model to flag non-sports-related messages in a sports-specific Slack workspace. It uses cases for content moderation.
The application uses a CLIP model to assess the temperament of different cat images as a proxy for potentially problematic content.
The app uses a sentiment analysis model to assess the general vibe of each provided support ticket. The app also provides a separate dashboard to track overall trends in sentiment over time to monitor for changes.
With the app, restore old photos using the photo restoration application, powered by the GFP-GAN model. Baseten is working hard to create and maintain comprehensive, readable, and occasionally, even personable references to assist you in building full-stack applications powered by ML models.
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