AI / ML Survival Guide: Conquer DataOps and Data Composability Challenges and Transform into a Truly Data-Driven Organization

AI / ML Survival Guide: Conquer DataOps and Data Composability Challenges and Transform into a Truly Data-Driven Organization

AI/ML is only as good as the underlying data. However, dataOps and data composability remain the most significant barriers for data scientists and engineers to uncover this value. With most of their time dedicated to preparing data for modern AI platforms, only 10% goes into generating and extracting value.

A shift to a unified means of accessing, joining, governing, and observing an enterprise’s data is needed. DataOS — the world’s first multi-cloud, programmable data operating system — is the shift. It provides a modern layer over current and legacy data systems to automate a significant portion of these tasks while reducing time, risk, and cost.

  • Data developers get an IDE-like platform for enterprise data and the tools they need to support AI/ML initiatives.
  • Business users get self-serve capabilities without worrying about pipelines and workflows.

In this presentation, you’ll discover how this new approach empowers data-driven decision-making as well as:

  • The systemic roadblocks you may not be thinking about
  • How to treat data as software
  • How your organization can leverage DataOS as a decision ops platform
  • And more!

Ash Damle
Head of AI & Data Science

Ash Damle has a long history of influencing, facilitating, and deploying AI solutions to solve what are currently some of the world’s most pressing challenges. With a degree from Massachusetts Institute of Technology (MIT) in both Computer Science and Mathematics, he has built a career and reputation as a technologist and data scientist passionate about applying big data to health and life as well as its intersection with design.

Over the last 25 years, Ash has researched, built, and applied AI technologies with leading institutions and organizations, including the UCLA AI and Robotics Lab, MIT's AI Lab, the MIT Media Lab, the US Navy, and NASA. He has published numerous papers and received patents in real-time unstructured semantic analysis.

He has collaborated with organizations around the globe, including the United States, China, the UK, Canada, Australia, France, Germany, India, and Japan. In addition, he's worked with leading venture capital firms, such as Sandbox Industries, Intel Capital, Khosla Ventures, Oak HC/FT, and many others to help realize a better tomorrow. He has helped raise over $100M in funding as an AI & Healthcare entrepreneur, investor, and advisor.

Ash has founded multiple startups with the goal of changing society to improve quality of life, including Healiom, WithMe Health,, Lumiata, and DeepAffects. He is also deeply involved with other organizations currently changing the world. He is an active investor and advisor to both healthcare and AI companies: CareLoop, LoopHealth, PeerWell, MyoKardia, Ellipsis Health, NuBiome, and ExtraEdge. Additionally, he is an active speaker at conferences, such as Health Datapalooza, Health 2.0, AMA, MIT AI, Nvidia GTC, and others.

In today's fast-paced world, data is essential to understanding customer behaviors, consumer trends, and real-time business insights. However, data is a complex and expensive problem to solve.

  • IT/Engineering resources are expensive and are at-capacity with projects and requests 
  • Legacy processes and systems struggle to keep up with ever-changing data demands 
  • Current data ecosystems lack self-service access to data for all who need it 

At The Modern Data Company, we saw the need for a modern approach to data. We’ve created a data platform that humanizes data and its access, breaks data silos, and transforms companies as they take steps toward data democracy and gaining real-time business insights.

Join our presentation, where you’ll also learn how Modern Data can help you get from data to decision in as little 4 weeks.

  • Modern layer over legacy systems - Instantly use your legacy systems in modern ways without having to modernize them. 
  • Data analysis without data movement - Assume less risk and cost by moving only the data that needs to be operationalized. 
  • Right-to-left data engineering - Start with the outcomes you need instead of worrying about writing pipelines. 
  • Modern composable architecture - Adopt the data architecture you want (e.g. data fabric, lakehouse, CDP, etc).