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


—NEW TECHNOLOGY—

Neural Composite Graph AutoML

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 

 

 

What is a Pipeline Index™?

Gain a competitive edge in AI against competitor data science teams.

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 a PaaS to design experiments

Enjoy no-limits capabilities with Aktiver's automated integration APIs

On-the-fly design of the infrastructure needed for experimentation of advanced model training strategies. 

Incorporate any open source tool set with ease using the Pipeline Index™.

With Aktiver, you're getting a platform that understands the delicate balance between development and operations in the AI realm. Our solution respects the unique needs of both data scientists and operations teams.

Aktiver is used to design the infrastructure needed to support complicated deep learning use cases in:

  • Graph Neural Networks
  • Computer Vision
  • Bring your own GPT model to keep your data safe!
  • Reinforcement Learning with Human Feedback
  • Generative AI
  • Physics Informed Machine Learning
  • Knowledge Graph-backed AI data mesh to develop AI data products
     

How would you like to perform like the big companies in AI/ML? 

Aktiver puts the science back in “Data Science” by enabling teams to conduct parallel scientific experiments on the data.

Auto Migrate Data Pipelines

No Vendor Lock - Freedom at Your Fingertips: Migrate Your Data Pipelines Between Clouds with One Click, all on Kubernetes.

Reduced GPU Cluster Costs

Save 66-90% on your current GPU cluster costs. Large models in complex decision-making pipelines don't need to be expensive!

Design and deploy multiple AI/ML experiments against the advanced scientific infrastructure for LLMs/GPT, Generative AI, GNNs, Reinforcement Learning feedback loops, Computer Vision, and Physics Informed ML models, on the cloud or IoT/Edge.

Model Computational Performance

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.

Adversarial
Robustness

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.

Superior Dataset Analysis

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.

Test & Evaluate Model Robustness

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.

DL & Physics Informed Model Performance

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.

DL & Physics Informed Model Analysis

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.

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Contact us

E-mail: jay.weinberg@aktiver.io

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