Saving Time with Personalized Videos Using AWS Machine Learning
January 28, 2021
CLIPr aspires to help save 1 billion hours of people’s time. We organize video into a first-class, searchable data source that unlocks the content most relevant to your interests using AWS machine learning (ML) services. CLIPr simplifies the extraction of information in videos, saving you hours by eliminating the need to skim through them manually to find the most relevant information. CLIPr provides simple AI-enabled tools to find, interact, and share content across videos, uncovering your buried treasure by converting unstructured information into actionable data and insights.
How CLIPr uses AWS ML services
At CLIPr, we’re leveraging the best of what AWS and the ML stack is offering to delight our customers. At its core, CLIPr uses the latest ML, serverless, and infrastructure as code (IaC) design principles. AWS allows us to consume cloud resources just when we need them, and we can deploy a completely new customer environment in a couple of minutes with just one script. The second benefit is the scale. Processing video requires an architecture that can scale vertically and horizontally by running many jobs in parallel.
As an early-stage startup, time to market is critical. Building models from the ground up for key CLIPr features like entity extraction, topic extraction, and classification would have taken us a long time to develop and train. We quickly delivered advanced capabilities by using AWS AI services for our applications and workflows. We used Amazon Transcribe to convert audio into searchable transcripts, Amazon Comprehend for text classification and organizing by relevant topics, Amazon Comprehend Medical to extract medical ontologies for a health care customer, and Amazon Rekognition to detect people’s names, faces, and meeting types for our first MVP. We were able to iterate fairly quickly and deliver quick wins that helped us close our pre-seed round with our investors.
Since then, we have started to upgrade our workflows and data pipelines to build in-house proprietary ML models, using the data we gathered in our training process. Amazon SageMaker has become an essential part of our solution. It’s a fabric that enables us to provide ML in a serverless model with unlimited scaling. The ease of use and flexibility to use any ML and deep learning framework of choice was an influencing factor. We’re using TensorFlow, Apache MXNet, and SageMaker notebooks.
Because we used open-source frameworks, we were able to attract and onboard data scientists to our team who are familiar with these platforms and quickly scale it in a cost-effective way. In just a few months, we integrated our in-house ML algorithms and workflows with SageMaker to improve customer engagement.
To see our architecture of AWS services, CLIPr advanced features and more, Continue reading this article on AWS