Rohan Kumar

Corporate Vice President, Azure Data, Microsoft

As the Corporate Vice President of Azure Data, Rohan is the engineering leader responsible for the product strategy, technical vision, long range planning, design, development/implementation, and engineering process involving the certification and release of SQL Server and all Azure Data Services, including SQL DB, Cosmos DB, Database for MySQL, Database for PostgreSQL, Database for Maria DB, SQL Data Warehouse, Azure Databricks, Azure Data Lake, HDInsight, Azure Stream Analytics, Azure Data Factory, Azure Data Catalog and Microsoft’s Analytics Platform System (APS).

As part of his charter, Rohan is focused on delivering core data platform services for Microsoft that allow IT professionals, DBAs, Data Scientists, Data Engineers and Developers to successfully develop, deploy, and manage data applications across Azure Data Services and SQL Server workloads.

Rohan joined Microsoft in July 1998 as a software development engineer in the core Windows file systems and storage team. He contributed to Windows XP and Windows Server 2003 before moving to the SQL Server team in July 2003. He has held various levels of engineering leadership roles in SQL Server since then and has contributed to SQL Server 2005, SQL Server 2008, SQL Server 2008 R2, SQL Server 2012, SQL Server 2014, SQL Azure, HD Insight Service and APS.

Rohan graduated with a Bachelor of Technology degree in Computer Science and Engineering from Indian Institute of Technology, BHU and a Master of Science degree in Computer Science from University of Massachusetts at Amherst.

UPCOMING SESSIONS

PAST SESSIONS

Spark + AI Summit 2020: Thursday Morning KeynotesSummit 2020

Clemens Mewal - Next Generation Data Science Workspace (Databricks) - 9:06 Lauren Richie - DEMO: Next Generation Data Science Workspace (Databricks) - 17:55 Matei Zaharia - MLflow Community and Product Updates (Databricks) - 27:40 Sue Ann Hong - DEMO: MLflow (Databricks) - 42:57 Rohan Kumar - Responsible ML (Microsoft) - 51:52 Sarah Bird - DEMO: Responsible ML (Microsoft) - 1:00:21 Anurag Sehgal - Data and AI (Credit Suisse) - 1:12:58


Introducing the Next Generation Data Science Workspace
Ali Ghodsi, Clemens Mewald and Lauren Richie

It is no longer a secret that data driven insights and decision making are essential in any company’s strategy to keep up with today’s rapid pace of change and remain relevant. Although we take this realization for granted, we are still in the very early stage of enabling data teams to deliver on their promise. One of the reasons is that we haven’t equipped this profession with the modern toolkit they deserve.

Existing solutions leave data teams with impossible trade-offs. Giving Data Scientists the freedom to use any open source tools on their laptops doesn’t provide a clear path to production and governance. Simply hosting those same tools in the Cloud may solve some of the data privacy and security issues, but doesn’t improve productivity nor collaboration. On the other hand, most robust and scalable production environments hinder innovation and experimentation by slowing Data Scientists down.

In this talk, we will unveil the next generation of the Databricks Data Science Workspace: An open and unified experience for modern data teams specifically designed to address these hard tradeoffs. We will introduce new features that leverage the open source tools you are familiar with to give you a laptop-like experience that provides the flexibility to experiment and the robustness to create reliable and reproducible production solutions.


Simplifying Model Development and Management with MLflow
Matei Zaharia and Sue Ann Hong

As organizations continue to develop their machine learning (ML) practice, the need for robust and reliable platforms capable of handling the entire ML lifecycle is becoming crucial for successful outcomes. Building models is difficult enough to do once, but deploying them into production in a reproducible, agile, and predictable way is exponentially harder due to the dependencies on parameters, environments, and the ever changing nature of data and business needs.

Introduced by Databricks in 2018, MLflow is the most widely used open source platform for managing the full ML lifecycle. With over 2 million PyPI downloads a month and over 200 contributors, the growing support from the developer community demonstrates the need for an open source approach to standardize tools, processes, and frameworks involved throughout the ML lifecycle. MLflow significantly simplifies the complex process of standardizing MLOps and productionizing ML models. In this talk, we’ll cover what’s new in MLflow, including simplified experiment tracking, new innovations to the model format to improve portability, new features to manage and compare model schemas, and new capabilities for deploying models faster.


Responsible ML - Bringing Accountability to Data Science
Rohan Kumar and Sarah Bird

Responsible ML is the most talked about field in AI at the moment. With the growing importance of ML, it is even more important for us to exercise ethical AI practices and ensure that the models we create live up to the highest standards of inclusiveness and transparency. Join Rohan Kumar, as he talks about how Microsoft brings cutting-edge research into the hands of customers to make them more accountable for their models and responsible in their use of AI. For the AI community, this is an open invitation to collaborate and contribute to shape the future of Responsible ML.


How Credit Suisse Is Leveraging Open Source Data and AI Platforms to Drive Digital Transformation, Innovation and Growth
Anurag Sehgal

Despite the increasing embrace of big data and AI, most financial services companies still experience significant challenges around data types, privacy, and scale. Credit Suisse is overcoming these obstacles by standardizing on open, cloud-based platforms, including Azure Databricks, to increase the speed and scale of operations, and the democratization of ML across the organization. Now, Credit Suisse is leading the way by successfully employing data and analytics to drive digital transformation, delivering new products to market faster, and driving business growth and operational efficiency.

Rohan Kumar, Microsoft | Keynote Spark + AI SummitSummit 2019

Rohan Kumar, Microsoft | Keynote Spark + AI Summit

Developing for the Intelligent Cloud and Intelligent EdgeSummit Europe 2018

It seems as if there are multiple stories daily about the various ways AI is impacting organizations and people across the world. Whether it’s intelligent applications making data-driven recommendations to customers, or machine learning being used to detect potential health risks- it’s hard to be surprised these days. Cloud computing has been a fundamental force driving our ability to use data science and machine learning to solve for scenarios that were once believed only to be achievable in science fiction. But, what about scenarios in remote places with little to no connectivity and with devices that have limited processing power? What if we could infuse this intelligence not only into applications in the cloud, but also onto the devices themselves right where the action is? This talk introduces the concept of how the intelligent cloud and intelligent edge maintained as one computing fabric can extend the reach of AI.

Developing for the Intelligent Cloud and Intelligent EdgeSummit 2018

It seems as if there are multiple stories daily about the various ways AI is impacting organizations and people across the world. Whether it’s intelligent applications making data-driven recommendations to customers, or machine learning being used to detect potential health risks- it’s hard to be surprised these days. Cloud computing has been a fundamental force driving our ability to use data science and machine learning to solve for scenarios that were once believed only to be achievable in science fiction. But, what about scenarios in remote places with little to no connectivity and with devices that have limited processing power? What if we could infuse this intelligence not only into applications in the cloud, but also onto the devices themselves right where the action is? This talk introduces the concept of how the intelligent cloud and intelligent edge maintained as one computing fabric can extend the reach of AI.