Indranil Chandra
Architect ML & Data Engineer @ Upstox
Mumbai, India
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Data Ninja
Area of Expertise
Topics
Data x Design Thinking = Customer Delight
This talk will be an intersection of Design x Product x Data and focus on the fundamentals of applying Design Thinking principles (empathise, define, ideate, prototype & test) to Data problems and build successful data products that bring customer delight and revenue.
This talk will be an interactive session with the audience where the speaker & audience would together solve few interesting problem statements from Stock Trading & Broking domain by applying the design thinking principles.
This talk will also highlight a simple to use Data Project Governance framework - https://github.com/indranildchandra/DataScience-Project-Governance-Framework/tree/master that can help in identifying successful data projects and prioritising them. This is very relevant in today's world since building data products is the easier part now given the advent of GPT-3, however building the RIGHT data product that truly delivers a value to the customer is a rarity.
AI x Data x Design Thinking = Customer Delight
This talk will be an intersection of Design x Product x Data x AI and focus on the fundamentals of applying Design Thinking principles (empathise, define, ideate, prototype & test) to Data problems and build successful data products using AI that bring customer delight and also generate revenue.
This talk will be an interactive session with the audience where the speaker & audience would together solve few interesting problem statements from Stock Trading & Broking domain by applying the design thinking principles to build a data product using AI. The novelty of this talk revolves around the real-world products that the speaker has built using the same framework at one of the largest stock brokers in India - Upstox.
This talk will also highlight a simple to use Data Project Governance framework - https://github.com/indranildchandra/DataScience-Project-Governance-Framework/tree/master that can help in identifying successful data projects and prioritising them. This is very relevant in today's world since building data products is the easier part now given the advent of GPTs, however building the RIGHT data product that truly delivers a value to the customer is a rarity.
Modern Data Stack - Architectural Design Patterns
The talk will open with examples of different data requirement of different organizations at different stages in their data journey and introduce the classic architectural choices that can be adopted to solve them. This will lead us to an in-depth discussion on one of the most popular architecture choice - ELT and how modern data tools and technologies fit into the ELT paradigm with target architectures proposed for example use cases viz, Regulatory reporting, customer-facing graphical data representation, real-time ML inferencing and internal data driven decision making process. The talk will be closed with a discussion on Modern Data Architecture pillars viz, Data Lakes, Analytics Engineering, Data Mobility, Data Governance, Data Observability, Performance and Cost-effectiveness.
Modern Data Stack - Architectural Design Patterns
The talk will open with examples of different data requirement of different organizations at different stages in their data journey and introduce the classic architectural choices that can be adopted to solve them. This will lead us to an in-depth discussion on one of the most popular architecture choice - ELT and how modern data tools and technologies fit into the ELT paradigm with target architectures proposed for example use cases viz, Regulatory reporting, customer-facing graphical data representation, real-time ML inferencing and internal data driven decision making process. The talk will be closed with a discussion on Modern Data Architecture pillars viz, Data Lakes, Analytics Engineering, Data Mobility, Data Governance, Data Observability, Performance and Cost-effectiveness.
DL-on-the-Edge
Accessible and affordable compute power, volume and variety of data, better algorithms and frameworks are the 3 major reasons why Deep Learning as a field has exploded and has become so popular in the last decade. This talk will be primarily focused on the first aspect - compute power and how modern DL based solutions can be deployed on edge devices with minimal compute power. Solutions like NUC (Next Unit of Computing) and NCS2 (Neural Compute Stick - 2) will be discussed and how they can be leveraged to build solutions like auto-navigating robots and assistive devices with accompanying demos. The talk will be concluded with a discussion on principles of community building, collaborating on building open source solutions and how the demos presented were built leveraging the open source community.
Devfest Mumbai 2019 Sessionize Event
Indranil Chandra
Architect ML & Data Engineer @ Upstox
Mumbai, India
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