Tara Jana is a marketing leader with 10+ years of experience at the intersection of AI, ML, NLP and data platforms, currently heading marketing at Credo AI after senior product marketing roles at Firebolt and Neo4j. She has repeatedly launched and scaled enterprise AI products—driving GTM for AWS AI services, DeepLens/DeepComposer education programs, and Neo4j developer tools—while building high-performing, geographically distributed teams. Known for combining deep technical fluency (SQL, cloud and graph databases) with narrative-first positioning, she has driven multi-million dollar adoption outcomes and strong analyst presence for generative AI and conversational platforms. Tara pairs executive education from MIT Sloan and Yale with hands-on product marketing craft, and unusually for a CMO-level marketer she still ships developer-facing content and technical enablement.
10 years of coding experience
17 years of employment as a software developer
Bachelors in Engineering, Computer Science, First Class with Distinction GPA (3.7), Bachelors in Engineering, Computer Science, First Class with Distinction GPA (3.7) at Visvesvaraya Technological University
Executive Education, Artificial Intelligence, Completed, Executive Education, Artificial Intelligence, Completed at MIT Sloan School of Management
Executive Acceleated Management Program, BUSINESS, MANAGEMENT, MARKETING, AND RELATED SUPPORT SERVICES, Successful Pass, Executive Acceleated Management Program, BUSINESS, MANAGEMENT, MARKETING, AND RELATED SUPPORT SERVICES, Successful Pass at Yale School of Management
The tutorial uses several Azure services to power a real-time chat infrastructure that is readymade for analytics. Event Hubs ingest chat messages received from websites running in Web Apps. Web Jobs are used to pull chat messages from Event Hubs, invoke the Text Analytics API to apply sentiment scores to each message and to forward messages to Service Bus Topics from which chat participants receive their messages. Stream Analytics is used to drive the archival of scored chat messages into Document DB and Azure Search is used to make the stored chat messages full text searchable.
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