Effortless development of personalized AI
Train on your data and host in your cloud
Scale large models, fine-tune on your data, deliver blazing fast inferences, optimize infrastructure costs
Streamlined Development
Leverage our development tool chain to build your first AI with your own data with three lines of code.
API for non-AI experts
No need to be lost in complex development library such as PyTorch or HuggingFace. Our open-source library requires minimal inputs from your side to build your own model.
Rapid fine-tuning
Concerned about the time and money required to train huge AI models? We use cutting-edge techniques to reduce the fine-tuning costs by up to 90% in a fraction of the time.
Automatic Acceleration
Get over 10x speedup by leveraging cutting-edge optimization of large deep learning models using software and hardware acceleration techniques
Model scaling and optimization
Fastest and most accurate models can be trained and deployed efficiently
Optimal compliers and runtimes
Best of the compilers and runtimes targeting user-selected hardware and optimization objective
Comprehensive Benchmarking
Select the best model for your use-case by running benchmarks on different configurations, downstream tasks, evaluation metrics and hardware
Collaboration
Create and manage teams to limit access to different resources from the admin dashboard
Share assets
Easy sharing of models, datasets, benchmarking results and optimization jobs from the dashboard
Auto-Scaling Deployments
Deploy auto-scaling optimized models in the cloud with complete transparency of the underlying hardware
Auto-tuning distributed computing
Distributed computing infrastructure is scaled automatically, resulting in the optimal pipeline
Monitor efficiency, not just accuracy
Real-time logging and monitoring of resource utilization and cloud costs of deployed models
Unlock new capabilities
Better and faster models
Deploy more accurate and 10x faster models in seconds, with a click or a line of code to unlock new ML capabilities
Reduce time to market
Reduce the time it takes to optimize the model and serving stack to production-grade performance from months to hours
Minimize cloud costs
Minimize cloud costs, compute resources and carbon-footprint used for serving models by more than 80%
Use Cases
Text classification
Classify sequences of text according to a number of classes. Train a model to automatically rank your customer reviews.
Text generation
Create a coherent portion of text that is a continuation from the given context.Generate marketing content from product descriptions.
Translation
Translate from one language to another. Translate blog posts or documentation into multiple languages to maximize reach.
Image Classification
Classify images according to a given number of classes. Automatically detect defective parts in your production chain.
Summarization
Summarize a document into a shorter text. Summarize earnings calls, research papers and articles to save time.
Token Classification
Classify tokens according to a class. Detect Personal Identifiable Information (PII) before using your data.
Try Stochastic in your own cloud or on-premise
Get more control, cost savings and compliance with our application hosted in your private infrastructure.
Compatible with your existing pipeline
No solution is a good solution if it adds extra work. Start using Stochastic acceleration platform through web dashboard or CLI or Python SDK.
Making the best AI accessible to everyone
Leveling the playing field of AI through easy-access to optimized AI computing