• Company rebrands from T-Dab.ai to OctaiPipe – the name of its Federated Learning Operations (FL-Ops) platform for AIoT (AI Internet of Things)
• Platform empowers Critical Infrastructure OEMs, operators and device OEMs to easily build and orchestrate networks of intelligent Edge devices securely
• Version 2.0 of OctaiPipe introduces a new web front-end interface, enhanced security and AWS Platform-as-a-Service (PaaS) availability

OctaiPipe, the Federated Learning Operations (FL-Ops) company, today announced general availability of version 2.0 of its platform. Targeted at Critical Infrastructure OEMs and device manufacturers, the Octaipipe platform allows data scientists to build and orchestrate networks of intelligent Edge devices, streamlining the entire lifecycle from network setup and training to deployment, AI model fine-tuning and continuous learning.

Critical Infrastructure companies across energy, telecoms, civil engineering and security are aware of the many benefits AI and connected IoT devices can offer – from improving operations efficiency and sustainability, to monitoring asset performance and ensuring network resilience. Critical Infrastructure environments are typically data-rich and highly secure. As such, intensive cloud data requirements, security and network dependency concerns have limited the sector’s utilisation of Cloud AI and connected devices.

Federated Learning Operations (FL-Ops) enables the deployment of AI to the edge and the management of distributed learning across a network of intelligent devices. Rather than move data from Edge devices to the cloud to train AI algorithms – with all the data storage costs and security concerns that entails – Federated Learning (FL) instead trains algorithms on-device at the Edge with data shared between devices in a decentralised network for continuous, distributed learning.

For an explanation on Federated Learning for Edge AI for IoT, watch OctaiPipe’s video here: https://youtu.be/OrLR8HTxJI0?feature=shared

Eric Topham, CEO of OctaiPipe said, “We’ve worked closely with our customers and community of data scientists over the past 12 months to understand their requirements and inform this important update of the OctaiPipe platform. OctaiPipe 2.0 makes it even easier to train, deploy and manage on-device Federated Learning at scale, while further enhancing data security, AIoT system resiliency, and lessening network & cloud dependency. Society depends on the resilience, performance and security of our Critical Infrastructure – by making Federated Learning for IoT easy to deploy, OctaiPipe is ensuring Critical Infrastructure can continue to be trusted in the age of AI.”

Launched in 2022, OctaiPipe is trusted by 20 companies and OEMs to simplify and automate distributed learning, fortify security and supercharge the performance of their Edge AI systems at unparalleled scale. Data scientists rely on OctaiPipe to build distributed AIoT (AI Internet of Things) solutions that learn and adapt at scale while minimising the overheads of monitoring, management, updates and audit.

Data scientists can leverage the OctaiPipe FL-Ops Platform-as-a-Service on the Cloud infrastructure of their choice and manage a database of their devices, models, experiments and deployments. Platform infrastructure agnostic, OctaiPipe is compatible with familiar tools such as PyTorch and scikit-learn. The OctaiPipe library enables large-scale FL and monitoring automation all from a familiar Jupyter notebook development environment. OctaiPipe is pre-packaged with examples for definition and setup, including pre-processing, feature engineering, FL model training and evaluation, deploying models for inference, detecting data drift and automated re-learning.

The newly released version 2.0 of OctaiPipe features a brand-new web front-end, making it even faster and easier for data scientists to manage devices, models, experiments and deployments. A new notifications page allows users to monitor ongoing deployments and experiments in one place. The combination of the familiar and productive Jupyter notebook development environment with the new web front-end further simplifies and automates deployment for users.

As part of the development of version 2.0, OctaiPipe engaged an external penetration testing team to robustly and independently verify the security of all platform components. An enforced TLS certificate is now deployed on OctaiPipe’s Rest API and automated penetration tests are now an integral part of OctaiPipe’s continuous build process.

In addition to Microsoft Azure, OctaiPipe 2.0 is also now available as an AWS Platform-as-a-Service (PaaS) solution. By positioning all storage and compute into the customer’s cloud infrastructure, OctaiPipe ensures the customer can remain in complete control of their data and intellectual property. This further improves the data security and privacy of customers’ systems, enhancing data compliance.

Watch the OctaiPipe v2.0 demo here: https://youtu.be/NCKB6tI_wck

Notes to Editors

Launched in 2022, OctaiPipe is an end-to-end Federated Learning (FL) Edge AI platform optimised for creating, deploying, and managing machine learning IoT solutions in Critical Infrastructure environments. Deployments on OctaiPipe are more affordable, private, scalable and resilient for on-device intelligence due to its federated machine learning capabilities, as well as its innovative Edge MLOps technology.

With its secure, resilient and cost-efficient federated Edge AI, OctaiPipe is removing the common barriers to market while maximising security and privacy, and minimising data transfers to rely less on cloud connectivity on a global scale.

The post OctaiPipe releases version 2.0 of its FL-Ops platform first appeared on AI-TechPark.