Applied AI Platform

Overview
Resources

AI platform for data annotations / review, AutoML, and ML predictions at scale

Typically machine learning projects involve sourcing of data, training, feature extraction, model building, fine tuning, and inferencing. The evolution of deep learning models has taken away the work of feature extraction and model building so as to help us focus on product development using AI. Below are some accelerators which will enable us to achieve this:

1. AutoML – Building an end-end reusable pipeline for NLP (NER, classification, QA) and Vision (Object detection, image classification, etc.) tasks, which would include all MLOps best practices being followed on AWS/Azure/GCP, will significantly accelerate development.

2. Annotation / Review tool – Most deep learning tasks need labeled / annotated data for training and active learning. Having a ready to execute accelerator tool for auto-annotation and auto-augmentation for reviewing ML outputs would enable active learning with a complete feedback loop.

3. ML REST Services – Building NLP/Vision and other ML REST service as an alternative to cloud providers AWS/Azure/GCP will enable us for in-house solutions for common ML tasks with easy integration and faster GTM

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