Our mission is to stop businesses searching for insights and instead make the insights reach the businesses. We aspire to do it through our state-of-the-art SQL editor that shape-shifts into a full blown BI tool with powerful ML capabilities.
Angshuman Guha has been a practitioner of applied machine learning for 24 years, spanning Microsoft, Google, Yandex Labs, Sears Holdings and a startup.
He has worked on handwriting recognition, optical character recognition, web search, image recognition, conversion prediction for online advertisements, ad click prediction based on text, machine learning infrastructure and other machine learning problems.
His main technical strength lies in deep learning (neural nets), but he has worked on all state-of-the-art machine learning technologies including random forests, boosting, bagging and support vector machines.
He chose to remain a hands-on engineer/scientist instead of going up the management ladder. He is passionate about open-ended hard computer science problems, with focus on, but not limited to, modern machine learning paradigms. He holds two dozen technical patents in the US and outside.
In his 14 years of software building career spanning various teams at Microsoft, Vishal Joshi has worked on wide range of software products that cover the entire software stack.
He was one of the early member on the AzureML core algorithms team. He has 7 years of applied machine learning experience spanning diverse areas like ad prediction, search relevance, natural language processing in offce and spam detection in Exchange.
In his latest role as a DVP - ML & Analytics at Sears, he was responsible for modernizing their analytics stack, bringing in cloud-based data management, introducing ML and creating integration between myriad moving parts of that large and diverse enterprise.
Vishal loves building products that make others more productive.
We first met as members of an ML (machine learning) infrastructure team at Microsoft. We were working on a myriad machine learning technologies — neural nets, support vector machines, decision trees and forests, scalable linear models etc etc. That work was later subsumed by Azure ML — Microsoft’s cloud-based machine learning service.
We worked together again later, in the analytics division of Sears Holdings. We had an opportunity to learn the needs and problems of a large retail company and the largest loyalty program in the US. We experienced the state of data backends, the inefficiencies of old processes and the difficulties of communication and collaboration in a silo’ed environment.
Three lessons we garnered:
- It is essential to have a pipeline for getting the data from the fragmented backends (Teradata, Hadoop, etc) into the cloud. All subsequent analytics and ML work then become easier by orders of magnitude.
- It is important to treat the imported data as the only “truth”, the only source for all subsequent processes to draw from. No more unexplainable inconsistencies in reports and insights.
- The biggest bang for the buck comes from building end-to-end ML solutions in-house. One-off solutions with a myopic perspective are inefficient in the long run. Some of the powers of ML manifest only when there is a feedback loop from operations to data to insights back to operations.
However, the biggest lesson manifested later. It came gradually at first, but eventually crystallized with the dazzling clarity of the Simpsonian d’oh! With the current hype around ML and a serious shortage of skilled talent, many enterprises find themselves between a rock and a hard place. The state-of-the-art business intelligence tools are non-intuitive, need a ceaseless effort of tinkering by “data analysts” and do not exploit the biggest breakthroughs in Big Data and ML that have happened in the last two decades! While a lot of companies are selling BI tools and an ever larger number are claiming to do ML, there is a market need that remains unsatisfied.
That was the beginning of our journey.