Discover how the Aktiver MLOps platform can turn your AI projects into tangible business results. By automating the creation, deployment, and management of your AI applications, we accelerate and scale up your machine learning (ML) and deep learning pipelines.
Reshape your AI business journey today for IoT/Edge & Cloud
Unlock Complex AI Systems & Boost Your AI Go-to-Market Strategy
Aktiver's Neural Composite Graph learns model training strategies, MLOps Design patterns, and their use cases
Aktiver is used to design the infrastructure needed to support complicated deep learning use cases in the following:
It puts the science back in “Data Science”, by enabling teams to conduct parallel scientific experimentations on the data.
No Vendor Lock - Freedom at Your Fingertips: Migrate Your Data Pipelines Between Clouds with One Click, all on Kubernetes.
Save 66-90% on your current GPU cluster costs, large models in complex decision making pipelines don't need to be expensive!
With the Aktiver ML Kubernetes Accelerator, we have modified Kubernetes into a superstructure for throughput computing. Making all-GPU RAM across the cluster available as a resource to train complex GNNs, Computer Vision, and RL models in a fraction of the time, saving millions upfront.
ML Model Security: Run adversarial robustness & adversarial defense experiments in parallel using Aktiver. Data scientists can “bake their own attacks” and measure attack severity and performance based on adversary knowledge and adversary capabilities.
Aktiver supports confident learning that identify label errors, characterize label noise, and learn with noisy labels. Aktiver also features the ability to use diverse and multiple data sets with any augmentation techniques in its advanced machine learning environment.
Synthetic Interventions on Scenes for Robustness Evaluation to test and evaluate with over two hundred different evaluation settings. This open source solution is designed to be extremely simple to add additional models and datasets, while converting label types for any model architecture.
The training cluster contains all the APIs needed to connect into the powerful infrastructure launched from the notebook, which is automatically launched from the training cluster.
Pipeline data quality testing employs multiple checks throughout the model training process to spot errors and assess label quality, allowing for optimized learning of predictive features.