Palak Kamdar
Vice President at Citi
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Experienced Data and AI Product Manager with 17 years of expertise spanning IT, Finance, Travel, Map, and Learning sectors. Transitioned from Senior Developer to Senior Product Manager, excelling in leveraging data-driven insights, user research, and analytics to enhance employee experiences. Passionate about integrating Generative AI to drive innovation and team success. Adept at leading global projects and delivering transformative outcomes across diverse domains, including Agentic AI transformation. Skilled in collaborating with OC leadership and cross-functional teams for impactful solutions.
Product Management: Strategic Planning, Product Lifecycle Management, Stakeholder Engagement, Roadmap Development, Agile Methodologies
Artificial Intelligence: Generative AI, LLMs, AI Governance, MLOps Implementation, Risk Governance Framework, AI for Tech
Data Expertise: Data Management, Governance, Quality, Snowflake, Databricks, Redshift, EMR, Data Warehousing, AWS Big Data, Data Analytics
Agile Methodology & Product Ownership: Product Board, Sprint Planning, Backlog Refinement, Epic and Story Mapping, Joint Review Sessions, JIRA, Confluence
Business Analysis: Requirements Elicitation, Prioritization, Acceptance Criteria, Traceability, Process Analysis (Current and Target State)
Engaged with a wide range of technologies, from Mainframes and web applications (PHP, Laravel) to contemporary big data tools (CI/CD pipelines, Tableau, Grafana, Hadoop, NoSQL, PostgreSQL).
Extremely passionate about mentoring and continuous learning. Data & AI Director for Women in Tech India, Executive member for AnitaB.org Mumbai chapter and speaker on Women TechNetwork Platform for 3 years.
Links
Strategic AI: Transforming Product Innovation and Roadmaps
Discover how to harness AI to be your friend and add spark to product roadmap, refine strategic decisions, and drive breakthrough innovations.
Learn actionable techniques to leverage data-driven insights, automate processes, and stay ahead of market trends.
Your take-aways -
Understand AI's Role in Product Strategy:
Master AI-Driven Tools
Develop a Proactive Strategy
Innovation Mindset
Everchanging need to place AI Governance
Explore the dynamic landscape of AI governance, highlighting the necessity for continuous evolution in policies and frameworks to address ethical, legal, and societal challenges posed by AI technologies. This session will delve into best practices for implementing robust AI governance structures to ensure responsible development, deployment, and use of AI systems in organizations. Understand Human in loop concepts.
Emerging Data roles embracing Generative AI and LLMs!
1. Introduction to Generative AI and LLMs - Explore the foundational concepts of Generative AI and Large Language Models (LLMs), their capabilities, and how they are transforming the data landscape.
2.The Role of Data Scientists in Generative AI - Understand how data scientists are leveraging Generative AI to enhance data analysis, model building, and predictive analytics, along with real-world case studies.
3.AI Engineers and the Future of Model Deployment: Discover the evolving responsibilities of AI engineers in deploying and maintaining Generative AI and LLMs, focusing on infrastructure, scalability
Concepts to Implementation understanding roles/responsibilities of team - MLOps
My professional background centers on Data Engineering products, specializing in facilitating collaboration among Data Engineers, Data Scientists, and Data Analysts.
Guiding them through their professional journeys can significantly enhance their collective growth within the dynamic landscape of contemporary Data.
I am prepared to deliver a session titled "Concepts to Implementation understanding roles/responsibilities of team - MLOps," which will delve into the integration of Machine Learning Operations into existing frameworks.
During this session, I intend to explore the contemporary dynamics of various Data roles and elucidate how teams can evolve autonomously with the right guidance and expertise. Furthermore, I will address bridging the gaps between Data Engineering and MLOps, as well as integrating Continuous Integration/Continuous Deployment (CI/CD) concepts with MLOps methodologies.
Take Ways
- Comprehend the intricacies of the data landscape and the specialized expertise each role within it entails.
- Establish correlations among roles in the data space, spanning from Data Engineer, Database Engineer, Data Scientist, ML Engineer, DevOps, to SRE Engineer, Data Analyst, Data Management Undercurrents etc.
- Harness the power of AI throughout the entirety of MLOps models.
- Delve deeply into learning pathways tailored to each profile, fostering the breakdown of silos through the adoption of the Product Operation Model concept.
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