Videos

Welcome to our Video Library, where you can access all the insightful videos. Our collection covers a range of topics, from foundational concepts to advanced strategies, providing you with a comprehensive understanding of the latest trends and innovations from past Devday, Conferences, etc.

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The Beginner’s Guide to All Things Sahaj
August 9, 2024
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Lending Sixth Sense to a Business
August 9, 2024
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Kickstarting your data journey webinar
August 9, 2024
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DevDay | Decentralized Finance: Business Opportunities and Trends III
August 9, 2024
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DevDay | Automatic Speech Processing for Voice AI
August 9, 2024
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DevDay | Starting your journey as Developer
August 9, 2024
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Revolutionizing Trade Finance: The Potential of Distributed Ledger Technology
June 13, 2024
Kickstarting your data journey webinar
By Anay Nayak
August 9, 2024
  • Understanding Data Journeys: The data journey involves defining goals, refining data, enriching it, and sharing insights. It often includes deploying machine learning models and iterating through multiple phases to enhance results.
  • Crucial Aspects: Focus on product thinking, data science for insights, and data engineering for reliability. Balancing these elements ensures a streamlined process and effective outcomes.
  • Iterative Approach: Start with basic models or prototypes, refine through iterations, and seek feedback to improve accuracy and functionality. This approach helps minimize cycle times and accelerates user access to valuable insights.
  • Early User Access: Provide business users early access to lower-level systems or dashboards to gather quick feedback and make necessary adjustments, which reduces overall development time.
  • Integration of Data Science and Engineering: Bridging gaps between data science and data engineering ensures models are effectively operationalized, enhancing overall efficiency and output quality.
  • Model Experimentation: Use frameworks for safe model rollouts, including feature toggles and A/B testing, to minimize risk and maintain business continuity during updates.
  • Cost Management: Prioritize efforts based on return on investment. Utilize cost-saving mechanisms like spot instances for processing jobs and focus on core business aspects rather than peripheral data systems.
  • Regulatory Compliance: Address regulatory requirements from the start. Consider using anonymized data and secure environments to manage personal information responsibly.
  • Data Quality and Downtime: Implement data quality checks and monitoring systems to ensure data accuracy and system reliability, preventing issues from impacting downstream processes.
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