This talk brings you through how a leading global bank has leveraged modern data architecture to place data in the heart of operations and deliver cost savings over 1 million USD and increased agility over 250%.
Category - Data Innovation Summit APAC 2023
Drive Innovation through Right Model, Right Data & Right Time – Engin Cukuroglu, Confluent
In this competitive era, companies can only survive with innovation. They need to reiterate their current businesses, challenges and invent new solutions to stay competitive.
Lean Data Engineering: Supporting Data-Driven Organization at Scale – John G. Bibal II, Globe Telecom
As the organization grows and becomes more data-driven, demand for data exponentially increases. And with a lean data engineering team, we go through the process of managing to scale.
Unified Monitoring of Data Science Platforms, Self-service Analytics Platforms & MLOps – Seah Boon Keong, BAT
This talk discusses the use of Unified Monitoring to help provide centralized monitoring of deployed data analytics solutions in Data Science Platforms, Self-Service Analytics Platforms & MLOps.
Leverage Data Science for Decision-making in Maritime Sector – Balaji Sri Raj, PSA, Singapore
This talk dives into how advancements in data science can be leveraged in planning and optimization of port operations.
Growing Data Communities with Governance – Colin Zhang, Billigence
How can we standardize and automate analytics process, so that teams can spend more time conducting analysis or focusing on what actually matters?
Make your Data Intelligence Capabilities Core Business Competencies to Drive AI – Ivy Lim, Alation & Tat Keong Ng, GXS
During this talk, Ivy Lim from Alation, along with Tat Keong Ng from GXS, dive into data intelligence!
Master Constant Change and Drive Agility in Data Engineering – Paul Milinkovic, StreamSets
In modern data, massive changes are a constant: from mainframe-hosted applications, we’ve moved to cross-cloud, distributed applications.
Paradigm Shift Enterprise Data Architecture’s Data Discovery & Consumption: Data Mesh – Meenakshisundaram Palaniappan, MSD
Organizations struggle to balance between avoiding centralized, tightly-governed data warehouses and suffering from poor data quality and discoverability issues.
AI for Everyone: MLOps Abstracted at Enterprise Level – Ankur Verma, Amazon India
End-to-end system design which abstracts out different processes in a typical ML project. Hyper configurable system governing the 3 main processes of ML project - Data Pipelines, Model learning and end consumption.
