Jerry has extensive experience in machine learning, storage systems, online service, distributed systems, virtualization, and OS kernel. He has worked on high performance and large-scale systems at companies such as: Lyft, Box, Twitter, Zynga, and Microsoft. He has also authored the open-source project Lib Crunch and is a three-time Microsoft Gold Star Award winner. Jerry completed his master’s degree in computer science at Shanghai University. His most recent startup is Datatron.
Datatron began in 2016 after you left Lyft. How did you initially conceive of the Datatron business concept?
When we worked at Lyft, we noticed that data scientist usually comes from diverse background like Math, Physics, Bio-Engineering etc. It is often very hard for them to get the engineering part of how their models work although they have good intuition on the model and math. That motivated us to start Datatron. We are not trying to help data scientist to find the best algorithm. We only come into picture after the algorithm is decided and to make the model deployment, monitoring and management more efficient.
Datatron was selected by 500 Startups to be included in the 18th cohort of accelerator companies. How did this residency personally influence you, and how you manage Datatron?
We did learn a lot from StartX and 500 Startup experiences which includes how to pitch to investors, how to find product/market fit, how to run sales/marketing which we don’t have experience personally before.
Datatron is a management platform for ML, AI, and Data Science models. Could you elaborate on some of the functionalities that are offered by your platform?
Our product has four modules now, Model Deployment, Model Minoring, Model Challenger and Model Governance.
Create and scale model deployments in just a few clicks. Deploy models developed in any framework or language.
Make better business decisions to save your team time and money. Monitor model performance and detect model decay as it happens.
Spend less time on model validation, bias detection, and internal audit processes. Go from model development to internal auditing to production faster than ever.
One of the use cases of Datatron is Demand Forecasting which is important for enterprises which need to plan and allocate resources. How does machine learning play into this?
Demand usually changes with both seasonality and trend, which is a typical machine learning problem. Machine learning models like ARIMA, Recurrent Neural Network (RNN) can learn from historic data to find the trend and seasonality automatically and make predictions based on that.
Which framework models (for example, TensorFlow) do you currently support?
We support most of the popular machine learning frameworks like sklearn, TensorFlow, H2O, R, SAS etc.
Which languages do models need to be built in to be supported by Datatron?
We support models in their native languages – Python, R, Java etc.
What are some of the types of industries which are best served by using the Datatron platform?
Fundamentally our platform is a horizontal solution which means it can be used by lots of different industries. As of now, we are trying to focus on financial service and telecommunication.
What are some of the most challenging aspects of data science that companies face, and why does Datatron solve this for them?
Lots of companies have different data science team already and those teams are using different tools to build their model and have different practice to manage their models. More and more enterprise realized that model is becoming an asset and will impact their top line directly. Having a platform can standardize the machine learning practice across the company becomes critical and required. Our platform can help to solve those issues.
Is there anything else that you would like to share about Datatron?
We got lots of inbound interests from big enterprises. At the same time, we are also building up our sales and marketing team to reach out to potential customers actively.
To learn more visit Datatron.
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