A walkthrough of using BERT with pytorch for a multilabel classification use-case
My experience deploying custom Machine Learning models, pipelines and prediction routines at Wootric Recently, Google Cloud AI Platform Serving (CAIP) added a new feature which Machine Learning (ML) practitioners can now use to deploy models with customized pre-processing pipelines and prediction routines using their favorite frameworks, all under one serverless microservice. In this blog post, I[…]
Note: An article about this from the CX perspective can be found here. The last few years have seen an explosion in companies adopting AI/ML solutions into their business pipeline. Huge advancements in Computer Vision and Natural Language Processing have made it possible to automate tasks we wouldn’t have attempted just 5 years ago. However, sometimes the utility of these methods can be lost in[…]
Whether it’s a brick and mortar company or a cutting edge SaaS enterprise, there is hardly a business that does not value customer feedback. Making sense of that feedback is tough. It’s easy to analyze feedback in a spreadsheet when you have a handful of customers, but as your customer base grows you need a system to help make sense of all of the feedback you receive. At Wootric, we built[…]
At Wootric, we've gotten quite a few requests from customers who've built their app using the Ionic Framework to test our web SDK with the framework. Here's how we did it.
PART II. [Go to Part I]
Our mission at Wootric is to help you win customers for life. One way we do this is by giving you the power to survey your customers across multiple channels. We serve customers in over 70 countries worldwide and have received over 2 million survey responses across web, mobile app, email and SMS.
by German Monfort (@gmonfort) and Prabhat Jha (@prabhatjha)
by Prabhat Jha (@prabhatjha) and Ryan Smith (@rnsmith49)
