Speaker "Prasad Saripalli" Details Back



1. AI, ML & NLP for Enterprise and Government Applications: Core Concepts and Algorithms
2. AI, ML & NLP for Enterprise and Government Applications: Bridging the Last Mile Value Gap 


Workshop : AI, ML & NLP for Enterpriseand Government Applications: Core Concepts and Algorithms
This Workshop (4 Hours) is a technology, algorithms and application overview in depth (Python) on AI, ML and NLP applications in the Enterprise.  It is designed to help enterprise decision makers, data scientists and AI/ML engineers tounderstand the role and use cases of AI, ML and NLP and their applications for tangible RoI.  There is broad agreement in the industry and among research communities that AI will significantly add value to the Enterprise economies and global GDP as well.  However, there is a large gap between the generic optimism for revolutionary AI applications in the distant future such as cyborg physicians, fully automated clinics and care supported by robotics, and the current, near-term feasibility of AI, ML and NLP use cases from both business and technology points of view.  In this workshop, we will focus on this schism and first deconstruct it using a number of AI, ML and NLP use cases in a few key Enterprise verticals from the point of view of 4 stake holders –Consumer, Enterprise (Developer), Provider and Investor.  To this end, we will first provide an in depth introduction to Machine Learning, AI and NLP - their essential methods and algorithms, their differences, the tools used such as Python platofrms including NLTK, Tensorflow, PyTorch and OpenAI, and methods to map this learning to enterprise AI and ML applications of value.  This will help one to understand the essential innovative value and novelty of AI, ML and NLP methods in the context of Enterprise value creation and RoI.  Using a few specific Payer and Provider Use Cases, we will discuss how ML and AI can be used to enrich the analytics practice for Healthcare.  There is a hands-on (brief) imlementation module using Python libs such as ScikitLearn.  We will then conclude with a discussion on how to critically evaluate the ML, NLP and AI use cases, apps and start-ups, and identify the ones which could be profitably deployed in the near-term, intermediate term and long term.  This Workshop is based on a condensed version of 3 courses taught by Prasad Saripalli at the University of Washington and Northeastern University, in AI, ML and NLP.  
Workshop : AI, ML & NLP for Enterprise and Government Applications: Bridging the Last Mile Value Gap 
This Workshop (4 Hours) is a technology, algorithms and application overview in depth on how value is generated for tangible RoI in the Enterprsie and Governemn, and how Valuation methods can be developed to assess the value of AI, ML and NLP.  This is a strategy companion workshop to the above Technology workshop focused on AI, ML and NLP applications in the Enterprise.  It is designed to help enterprise decision makers, Product managers and leaders, investors , data scientists and AI/ML engineers to understand the critical need for rigorously evaluating the addressable market (TAM and SAM), RoI and enterprise value (EV) of AIML and NLP enterprises and methods for the same.  It helps the participants understand the last mile gap in the AIML enterprise which renders it difficult for startups and initiatives to succeed in the market.  This workshop has three hands on components with exercises  - (1) Methods for assessing the market and opportunity for AIML startups and use cases; (2) Valuation of AIML in startup investments and (3) use of machine learning in this value assessment.  
There is an urgent need for a quantitative valuation of AI and ML enterprises.  It is widely recognized that AI will have a significant impact on the GDP, expected to increase it by 14.5%, adding an additional $15.5 trillion to the world economy by 2030.  There are detailed analysis especially at the macrolevel (i. e., at the scale of economies spanning multiple industry verticals, and at the next level focused on a single vertical) to assess AI's value addition to the economy.  However, valuation and corporate finance experts recognize a current dire need for rigorous evaluation methods for AI, at the level of an individual company.  The question is, given a specific investment in AI at a specific company, how does one estimate the value created by such investment?Established methods of valuation are not directly aplicable to AI and ML investments, given the very novel and disruptive nature of AI ML.  Valuation experts refer to such innovative products as intangible assets, whose valuation is especially hard, prone to uncertainties and complex assumptions.  At the same time, it is also established thatthe intangible assets including IP, brand and potential future revenue expectations, often dominate the overall valuation of startups and mature companies currently.  Leaders of AI, ML and NLP product development will find it very valuable to gain a basic introduction and proficiency with the methods of value creation and value generation, to ensure that the AIML they build have a better chance of success in the marketplace.  Providing the necessary background knowledge, concepts and methods using hands on exercises is the goal of the current workshop. 


Prasad Saripalli serves as the Vice President of ML & AI and Distinguished Engineer at MindBody Inc. - a portfolio company of Vista which manages the world's fourth-largest enterprise software company after Microsoft, Oracle, and SAP. Earlier, he served as VP Data Science at Edifecs, an industry premier healthcare information technology partnership platform and software provider, where we built Smart Decisions ML & AI Platform with ML  Apps Front. Prior to joining Edifecs, he was chief technology officer and VP of engineering at, which provides military-grade cloud security solutions. Previously, he worked as chief technology officer and executive VP at ClipCard and as chief architect for IBM's SmartCloud enterprise. He also served as GPM on Microsoft's client virtualization team, which was responsible for shipping Virtual PC on Windows 7, and as a Dev Manager on the Citrix group that built Citrix Presentation Server (now Citrix XenApp).  Prasad has doctoral training in Engineering and Computer Science from the University of Florida and post-doctoral training from the University of Texas, and teached ML, Advanced ML, AI, NLP and Distributed Systems at Northeastern University.