Interview: Joseph Sirosh, Microsoft on Azure ML and the Emerging Data Science Economy
We discuss what distinguishes Azure ML from its intense competition, the online machine learning university, current maturity level of Big Data solutions, important skills for data scientists and more.
During his 9 years at Amazon, he managed a variety of teams including forecasting, inventory, supply chain and fulfillment, fraud prevention systems, data warehouse and a novel data-driven seller lending business. Prior to Amazon, Joseph worked for Fair Isaac Corporation as VP of R&D. Joseph is passionate about Machine Learning and its applications and has been active in the field since 1990.
First part of interview.
Here is second and last part of my interview with him:
Anmol Rajpurohit: Q5. How do you differentiate Microsoft Azure Machine Learning from other competitive cloud-based analytics services such as those offered by Amazon and IBM?
In a broader sense, our differentiation is that Azure Machine Learning is part of a broad, connected data platform that includes services for every stage of the data lifecycle. This includes not only machine learning, but also Hadoop services such as Azure HDInsight, data orchestration and ETL services such as Azure Data Factory, cloud-based complex event processing such as Azure Streaming Analytics, and business intelligence tools such as Power BI. This combination enables advanced analytics at an entirely new level of ease and sophistication.
AR: Q6. At the Worldwide Partner Conference (WPC) 2014, Microsoft launched the new online Machine Learning University. What are the key short-term and long-term goals of that initiative?
JS: The goals of the initiative are to make machine learning more accessible and user friendly so that any developer anywhere in the world can use it as another tool in application building. I've been in this space a long time and haven’t seen
AR: Q7. You have been involved with Machine Learning for a long time. According to you what are the key reasons that we are currently witnessing an immense interest in the field of Machine Learning (which is more than half a century old)?
While machine learning has been around for a long time, usage was primarily restricted to people with deep skills and deep pockets. The cloud changes this dynamic completely.
Now, you can run compute for pennies on the dollar on the cloud and connect to systems and services that were previously stand-alone. The explosion of online data opens up opportunities for our customers to glean insights and make good choices to improve their business.
AR: Q8. With the focus of Big Data conversations shifting from "Promising Potential" to "Delivered Value", what are your thoughts on the current maturity level of Big Data solutions? What major obstacles have been conquered and what are the key challenges (or opportunities) in near future?
AR: Q9. You are aggressively hiring for talented scientists and engineers for Machine Learning and Data Science. Besides technical acumen, what are the key skills that you are looking for? What characteristics of an individual help you identify whether the person would be a right fit for your team?
JS: The key skills are a real understanding of data science, a great ability to develop services in the cloud, and facility with tools such as R, Python and Hadoop. I also think a key to success on our team, and really for any emerging market like this, is customer focus and a bias for action. I want everyone on my team, not just the data scientists, but sales, marketing, everyone, to be asking, “What is the customer problem we are really solving?” Then, have a strong bias for action, for building prototypes and iterating with the customer towards an end-solution in an agile manner.
AR: Q10. On a personal note, what are the books that you have been reading lately and would like to recommend?
JS: A book I really liked recently is “Running Lean: Iterate from Plan A to a Plan That Works” by Ash Maurya. It’s a great book about an iterative and lean approach to building a successful startup. The agile methodologies mentioned in that book are a great guide to any product innovator.
Related: