Aktiver converts your AI/ML projects into tangible business outcomes through the automation of a customizable end-to-end data science process, accelerating your AI go-to-market strategy. With Aktiver, a groundbreaking tool, create ML systems that yield ROI previously exclusive to major players (FAANG) in AI and ML.
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's Pipeline Index™ is a pioneering tool aiding a range of training strategies and facilitating efficient deep learning models. It significantly reduces data science project failures by 80%, providing substantial savings and flexibility in AI development and infrastructure. The Pipeline Index accelerates the creation and deployment of advanced end-to-end ML systems, saving time and making complex MLOps design patterns accessible to everyone.
Aktiver is used to design the infrastructure needed to support complicated deep learning use cases in:
Aktiver puts the science back in “Data Science” by enabling teams to conduct parallel scientific experiments 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!
The Aktiver ML Kubernetes Accelerator transforms Kubernetes into a throughput computing superstructure, leveraging all-GPU RAM across the cluster to train complex GNNs, Computer Vision, and RL models swiftly, saving millions upfront.
ML Model Security: With Aktiver, run concurrent adversarial robustness and defense experiments. Data scientists can create and measure attacks based on adversary knowledge and capabilities.
Aktiver enables confident learning to detect label errors, characterize noise, and learn from noisy labels. It also allows usage of diverse data sets with any augmentation techniques in its advanced ML environment.
Synthetic Interventions on Scenes for Robustness Evaluation tests over 200 evaluation settings. This open-source solution easily accommodates additional models and datasets, and converts 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 uses various checks during the model training phase to detect errors and evaluate model & label quality to enhance learning levels of a task combined with accuracy in production.
E-mail: jay.weinberg@aktiver.io