Building AI & Data products requires strategizing across data collection & scaling techniques, accounting for uncertainty in AI outcomes, and building feedback loops for the AI flywheel effect. In addition to ML techniques strategies, MLOps and Data architectures are also critical. This talk will go through frameworks, challenges and best practises on these topics.
What does it take to ensure our workforce is always one step ahead of the industry need? If tech evolution is a continuous process, how can L&D keep up with the curve? Is it time to treat ‘skilling’ as a business investment and not an expense? So many questions. The bottom line? To build organizations that are capable of innovating. We must unlearn our conventional notions of leadership and rethink how continuous learning can happen.
Sumanth Palepu, Senior Director-Edology, in his keynote address will answers these questions and present you solutions on cultivating life-long learning.
Creating ML models needs an understanding of the tools and domain knowledge. Given the difficulty in getting data scientists that understand your domain, several AI/ML initiatives never see the light of day. Another challenge is data preparation and real time operational data availability.
Qlik AutoML is our automated machine learning capability that allows business analysts to generate models, make predictions, and test scenarios all within a simple, no-code user experience. With this capability, you can transform your business analyst, who already understands your domain and business, into a citizen data scientist. In addition, Qlik Data Integration helps you leverage real time streaming data from your operational systems streamlining your data pipelines with minimal IT resources.
Being data-driven organisation is table stakes in today’s hyper competitive environment. In this talk, we will start by sharing our Data Platform strategy at Udaan, which has enabled us to unlock value from our data by embedding data driven decision making in all workflows. Then we will touch upon the worrying trend that nearly 85% of AI/ML models don’t make it to production and will share personal experience on how to mitigate the same. We will end by sharing the data science problem landscape for B2B e-commerce and will go through one use case demonstrating the various ML Ops capabilities that has enabled successful deployment of sophisticated, state-of-the-art ML model.
Businesses must make crucial decisions when they should. And it can only take place if businesses are able to utilise the value of data in real time. They have the ability to quickly and digitally crunch information, examine trends, obtain insights, and make judgments. Data may speed up decision-making within a company and be a growth multiplier for businesses. The panel will be frank about the difficulties businesses have using data to its full potential.
Through the rise of digital commerce, marketers and advertisers have relied heavily on third-party cookies to target audiences online. However, plans to phase out third-party cookies by the end of 2024, has left marketers unsure of how to deliver personalized experiences at scale; this could end up costing up to $10 billion in ad revenue.
As the death of third-party cookies looms, the need of the hour is to find an AI-enabled approach to build data strategies and identity platforms to keep delivering personalized experiences.
Merkle’s Geetha Apathotharanan & Sonia Thakurani share an innovative AI-enabled enterprise identity solution to help brands create unique customer IDs and map customer journeys using first-party data; thus enabling more contextual and personalized customer experiences in a cookie-less world.