Computer vision models are used in a suite of prediction tasks such as Object Detection and Instance Segmentation that have applications in a spectrum of deep-tech pillars such as Healthcare/Medical (detecting and diagnosing diseases from radiology or pathology images), Manufacturing (defect detection from image scans), Agritech (plant/crop health check via images and photos), and more. However, to accomplish this, teams have to navigate between Data Ingestion, Image Labelling, Transfer Learning, Model Validation, Deployment, and Model Tuning. This can take upwards of 8-12 different tools that teams have to use, making a swift, collaborative approach to model building a difficult task.
This technology offer presents an end-to-end MLOps platform that alleviates such issues and allows teams to build robust computer vision models step-by- step with enterprise-standard practices internally while maintaining a collaborative approach. This platform is industry agnostic, which provides an adaptive model that allows the teams and researchers to convert their datasets into working models.
The platform is generally industry agnostic and seeks to provide the workflow, computation resources and standards for teams to build models for their own contexts. This platform can be used by both AI service providers, companies looking to automate processes, as well as researchers looking to bring their datasets into production. The following are use cases that have been tested with our partners, but are not limited to -
Computational Pathology and Medical Imaging
Agriculture and Food Technology
There's a staggering 250,000 shortage of ML / AI engineering talents with 83% of companies investing in big data projects. The computer vision market size is valued at USD10.6B in 2019 and is expected to grow at a compound annual growth rate of 7.6%. However, looking at MLOps, which is predicted to be worth $4 billion by 2025 (worth only $350 Million in 2019) - has a CAGR of about 50%. This means that more and more companies who are moving into AI and Machine Learning are starting to focus on actual data processes, model lifecycles, and the deployability of the models.
This platform covers demographics in industrial, deep-tech problem solvers, and researchers. The platform provides the team with the in-house capability to build a robust model that is robust and production-ready. Using the platform, teams are equipped with an advanced MLOps pipeline that provides both speed and cost benefits when developing computer vision capabilities. Finally, the web-based and "batteries-included" approach of the platform means that users need not write complex code or environment setup to get involved.