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Boltzbit Platform

The First Neural Generative Learning Platform

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One model. Unlimited Tasks.

One model for one predictive task is expensive and not scalable. Neural Generative Learning builds a versatile model for unlimited tasks.

  • Conventional AutoML platforms are task oriented. One model is trained to serve a single predictive task, which is specified by inputs and outputs.
  • On Boltzbit platform, users can leverage Neural Generative Learning to build a single model for unlimited predictive or generative tasks.

Built-in Training Data Synthesis

Boltzbit platform integrates DataOps and MLOps. No need to generate synthetic data deliberately.

  • Data processing and augmentation are usually not supported at MLOps platforms. Users need to switch between data platforms and MLOps platforms in the development lifecycle.
  • Boltzbit Platform is an all-in-one platform to cover both DataOps and MLOps. By virtue of Neural Generative Learning, training data can be easily augmented and synthesised without extra cost and effort.

Developer Inclusive

ML should not be exclusive to Data Scientists. We democratise AI to whoever passionate about innovation.

  • Elementary AutoML platforms are designed for data scientists, which arguably limits the innovative collaboration across teams.
  • Besides the coverage of standard data science use cases, Boltzbit platform also provides developer-friendly gears to support software engineers to integrate ML work through their workflow. Of course, we do open source.

Support Cutting-edge AI Research

AI research without right tool is dull with tedious coding and experimenting. Boltzbit platform simplifies AI research with code generation and experiment management.

  • Significant time of ML research is spent on building experiment infrastructure. Researchers are often distracted from working out innovative ideas.
  • Boltzibt platform enables researcher to focus on the most critical part by eliminating all coding and experimenting burden. Level up teams' R&D capacity with simple clicks and drags.

Full Deployment Support

Shipping ML outcomes in production is often under valued on unseasoned AutoML platforms. Boltzbit platform offers wide options for online and offline ML deployment.

  • ML model deployment is usually restricted to limited options, e.g. tensorflow, scikit-learn (python). This deficiency hinders ML prodcutionization when more flexibility is needed.
  • Boltzbit platform provides full deployment support in all major programming languages. One can flexibly customize online serving configs or download tailored artifacts for offline usages.