Scaling ML/AI has been a major challenge for medium to large organisations. Most organisations can productionize only 30% of their ML efforts.
Join us for a deep dive into how we are confronting these challenges at Rakuten. With this session, you can learn about Rakuten’s Data Science Platform, Architecture and Modernize Feature Store capabilities that help in productionizing ML predictions faster at scale and enable collaboration across organizational boundaries to build value.
Data Science Platforms & Analytics
Rakuten Data Platform provides Data and Analytics services to various services across Rakuten Group. It today collects, manages and serves data sets from 70+ services with 43+ integrated services, 20K+ managed datasets and 6500+ weekly Active Users.
In this session we will briefly touch overall data landscape, Journey, the evolution of this platform over time, high-level architecture, the common challenges we are trying to address and the approach we have taken to address those challenges. We will then drill down into the Data Discovery & Utilization part of the platform which is the key to the democratization of Data within Rakuten Group.
You will also have a sneak peek into New products & toolsets which we are building some of which are in alpha/beta stages today.
Most chatbot projects fail. In this session, we’ll first look at the standard approach that leads to the ‘typical’ chatbot you’ll encounter on many websites or mobile apps, and why it never works. Then, we’ll present the approach we use at Rakuten to ensure our chatbots provide real value to our businesses through natural, conversational experiences that empower customers to self-serve successfully. Finally, we’ll walk through a specific example of our Rakuten Mobile AI Assistant, show the steps we are taking to keep its performance high, improve its coverage over time, and measure its value to communicate back to the business.
This talk will touch upon the need for digital automation for behaviour analysis. Human behaviour analysis and activity analysis is an extremely complex subject. AI has been attempting to play a bigger role in the domain of human activity analysis. The need for human analysis allows better solutions to be built for 1.Media, 2. Sports, 3. Consumer Packaged Goods, 4. Manufacturing. Video is known to be the source of truth for capturing information. The challenge with video is: it’s expensive to analyse the unstructured data inside videos and generate useful insights on what is happening across a sequence of frames( read activities). With this background, we would like to cover how the need for an end to end activity recognition platform has evolved and has been well received for use cases specific to the Consumer Packaged Goods industry. This platform is capable of enabling DIY Video AI for activity recognition.
1. A data annotation component with the ability to do automated annotation
2. A component to build models that can be deployed on-premise or in the cloud.
3. A component for inferencing, correction of inferences and feedback to model building and training.
4. An insights component that is capable of analysis of detections and generation of intuitive analytics insights.
5. How an integration between AI in the cloud and AI on edge has been implemented and pushed to production.
We will also cover how all of the above are helping large multinational majors in the CPG segment apply their domain knowledge to generate new behaviour insights and apply activity recognition to analyse product use and measure the effectiveness of product redesign initiatives.
Routing diagnostics show traffic is the biggest cause of ETA errors. To improve Routing, ETAs, and Navigation, we need to improve our Traffic prediction accuracy. In this session, we will talk about i) how does traffic work? ii) How is traffic used in Routing? iii) why does traffic matter?
Explainable AI (XAI) tends to refer to the movement, initiatives, and efforts made in response to AI transparency and trust concerns, more than to a formal technical concept. Interpretable ML is one of the most talked-about topics in the AI domain, to convert the black-box algorithm into easy to comprehend solutions to improve adoption for real-life business problems. Interpretable ML is the degree to which a human can understand the cause of a decision. We plan to talk about some of the cutting edge algorithms in the world of Explainability. Interpretability/ during the conference, explaining the importance of this topic along with real business applications and examples.
A glimpse of what it takes to enable unparalleled convenience to Swiggy’s customers and how AI powers decisions at each stage of the customer journey.
