MLOPs enables easy sustainable recycling at the edge
MLOps made easy
Develop, deploy, and manage ML apps and edge devices with one platform.
Teknoir, offering MLOps platform and AI solution company, has been working with Seeed’s reComputer J2011 and reTerminal, with their no-code Dev Studio for industry 4.0 applications such as workers’ safety, manufacturing of workforce optimization, and preventative maintenance and smart city of recycling materials detection.
MLOPs enables sustainable recycling
Teknoir, along with its client-partner, has developed an AI Edge solution for automatically detecting recyclable materials. For this particular use case, the Teknoir Platform was deployed on hardware consisting of cameras bundled with NVIDIA Jetson or Raspberry Pi CM4-powered devices running the Teknoir Device Operating System. Coupled with built-in LTE and running Teknoir’s Orchestration Engine, these edge devices have secure connectivity to the Teknoir Cloud. Teknoir’s client-partner is able to use the Dev Studio for pushing their trained machine learning model, as well as managing the fleet of hardware and software.
“Seeed continues to serve as an instrumental resource for Teknoir with their offering of innovative edge AI hardware solutions. Seeed’s devices provide Teknoir with unique opportunities to develop AI solutions for its customers that address a variety of important use cases at the edge.”
Jonathan Klein, Founder & CEO at Teknoir
AI is changing the way we live and is no longer considered a novel technology. It is regarded as a utility, similar to electricity, that will fundamentally transform how we live and work. However, the complexities and high cost may cause companies to struggle with the idea of implementing AI in their workplace. Without efficient AI product workflow operation and system optimization, industries can easily fail to modernize, which will limit industries’ growth. Industries will continue to struggle with operational efficiency, unplanned maintenance & downtime, safety, data sovereignty, and high cost. Several issues such as barriers to entry, messy data, reliance on the cloud, and being unscalable are issues companies face while ongoing the digital transformation.
Teknoir has provided an easy-to-use MLOps platform to solve such issues and build AI accessible for everyone.
So what is MLOps or democratizing AI?
MLOps is the use of Machine Learning (ML) models by development operations (Ops) teams. It helps prepare data and tune ML models in a live production or digital twin environment for immediate debugging and performance improvements. It drastically reduces or removes the data cleansing efforts by ingesting structured and contextualized data at the source.
The democratization of AI is spreading AI development to a broader user base that includes those without prior knowledge of AI.
Introducing the Teknoir Platform
A revolutionary MLOps platform that develops, deploys, and manages ML models in the easiest and most cost-effective way.
The platform aims at reducing costs, making the work environment safer & more productive, and increasing reliability by predicting irregularities and maintenance issues before they happen.
Democratizing Artificial Intelligence
- Utilize existing talent in AI roles without needing to fill expensive data science and software developer positions.
- Connect directly to data sources at the edge to ingest structured and contextualized data to remove the necessity for complex data cleansing and preparation for model training.
- Reduce costs by relying less on the cloud and leveraging existing compute resources and lightweight, commoditized IoT hardware at the edge for both training and inferencing.
- Simplify the entire process of machine learning development, training, deployment, management, and maintenance through an easy-to-use, no-code, visual drag-n-drop user interface.
- Utilize data sets located from different sites while keeping sensitive training data private and secure with federated machine learning.
- Easy access to case-specific apps, models, and developer tools via a marketplace.
Teknoir No-Code Dev Studio
Teknoir provides a virtually codeless environment for app and machine learning model creation where inputs, outputs, and functions are configured using a drag-and-drop method. The Dev Studio can run in the cloud or directly on the embedded device at the edge for a true MLOps experience that results in structured and contextualized data ingestion, as well as easy model tuning and debugging.
Labeling and training simplified
Teknoir Label Studio, with its visual interface, makes it easy for users to select objects in images and videos using bounding boxes to annotate. This process automatically feeds the model training to improve detection confidence. This task-driven approach to labeling is incredibly efficient compared to other methods.
Hardware agnostic
The Teknoir Platform supports a variety of edge computing devices, including NVIDIA Jetson, Raspberry Pi, Khadas VIM3, as well as Seeed’s reTerminal and reComputer powered by Jetson Xavier NX. The Platform enables these devices to operate at the far edge, disconnected from the cloud, and fully encrypted.
Hardware
reComputer J2011 powered by Jetson Xavier NX
J2011 is a hand-size edge AI box built with Jetson Xavier NX 8GB module, rich set of IOs, aluminum case, cooling fan, and pre-installed JetPack System, ready for your next AI application development and deployment.
reTerminal, HMI device powered by Raspberry Pi CM4
reTerminal is powered by a Raspberry Pi Compute Module 4 (CM4), which is a Quad-Core Cortex-A72 CPU running at 1.5GHz and a 5-inch IPS capacitive multi-touch screen with a resolution of 720 x 1280. It has a sufficient amount of RAM (4GB) to perform multitasking and also has a sufficient amount of eMMC storage (32GB) to install an operating system, enabling fast boot-up times and a smooth overall experience. It has wireless connectivity with dual-band 2.4GHz/5GHz Wi-Fi and Bluetooth 5.0 BLE.
About Teknoir
Teknoir was founded in 2019 to reshape the industry’s future democratizing artificial intelligence with its MLOps platform for those that aren’t data scientists or programmers via an intuitive, no-code dev environment in a hybrid cloud approach that enables inferencing of AI data on lightweight embedded devices at the far edge to drastically improve performance, security, and scalability. Teknoir is enabling an AI creator economy with curated ML models and apps that can be monetized and distributed via its marketplace.
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