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
—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
Aktiver is MLOps 2.0
Enjoy no limits capabilities with Aktiver's automated integration APIs
It is a MLOps infrastructure platform that allows the data scientist to incorporate any open source toolset 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 developers and operations teams.
Aktiver is used to design the infrastructure needed to support complicated deep learning use cases in the following:
- Graph Neural Networks
- Computer Vision
- Bring your own GPT model
- 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?
It puts the science back in “Data Science”, by enabling teams to conduct parallel scientific experimentations 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!
Advanced Scientific MLOps Infrastructure for LLMs/GPT, Generative AI, GNNs, Reinforcement Learning feedback loops, Computer Vision & Physics Informed ML models
Model Computational Performance
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.
Adversarial Robustness
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.
Superior Dataset Analysis
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.
Test & Evaluate Model Robustness
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.
CV, GNN, NLP/LLM & Physics Informed Model Performace
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.
CV, GNN, NLP/LLM & Physics Informed Model Analysis
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.
Contact us
E-mail: jay.weinberg@aktiver.io
