Determined AI
Category : Artificial Intelligence, Developer Tools, Machine Learning
Domain : Machine Learning
Founders : Ameet Talwalkar, Evan Sparks, Neil Conway
Established : 2017
Operating Status : Active
Funding status : Not Disclosed
Website : determined.ai
Country : San Francisco, California, United States
Latest in News: Company nabs $11M Series A to democratize AI development
Determined AI is an early stage machine learning technology company that revolutionizes the way deep models are trained and deployed and reduces time-to-market by increasing developer productivity, improving resource (GPU) utilization, and reducing risk.
The company will interact with their customers daily to learn more about their data sets, modeling problems, and infrastructure, to help them with our product, and to improve our product offering.
The company takes a pragmatic, results-driven approach to deep learning, with a goal of dramatically improving the productivity of deep learning developers. They believe in best ideas can come from anyone and anywhere, and we have to be humble enough to listen for them and they believe in open communication and transparency in process and priorities.
Auto ML platform simplifies the entire deep learning workflow from data management to model training and deployment and manage heterogeneous hardware and optimize your GPU resource utilization.
AutoML at scale – Deliver quality models in less time like, Automatic data-parallel distributed training, Data access accelerator, TensorFlow, Keras, or PyTorch support, State-of-the-art hyperparameter and neural architecture search and Push-button transfer learning and retraining.
The Seamless infrastructure – Optimize resource utilization and ensure experiment reliability like, Efficient scheduling and execution of experiments, Share GPU resources on premises, in the cloud, or both, Run on bare metal, and in Kubernetes and Fault-tolerant training.
Experiment tracking – Enables reproducibility and collaboration like, Reproducible, containerized training, Label and share experiments, Integrated metrics capture and model version management, Multi-tenancy in a shared infrastructure and Real-time experiment metrics visualization.
Model deployment optimization – Ensures model deployability and reduce time-to-market like, Automated architecture search for the edge, cloud, and mobile, Simulate inference speed on deployment hardware, Containerized deployment in a single click and Explore models with deployment constraints.
Artificial Intelligence development in Deep learning involves a highly iterative process where data scientists build models and test them on GPU-powered systems until they get something they can work with. The company heads with a new startup that changes by making the process faster, cheaper and more efficient and announced an earlier $2.6 million seed round from 2017, for a total $13.6 million raised to date.
