Join our capstone event for Retail and Consumer goods attendees at Summit featuring keynotes and panel discussions with Instacart, 84.51, Walgreens, Shipt and PetSmart followed by networking.

MLOps is one of the widely discussed topics in the ML practitioner community. Streamlining the ML development and productionalizing ML are important ingredients to realize the power of ML, however it requires a vast and complex infrastructure. The ROI of ML projects will start only when they are in production. The journey to implementing MLOps will be unique to each company. At DoorDash, we’ve been apply...

DoorDash was using a data warehouse but found that they needed more data transparency, lower costs, and the ability to handle streaming data as well as batch data. With an engineering team rooted in big data backgrounds at Uber and LinkedIn, they moved to a Lakehouse architecture intuitively, without knowing about the term. In this session, learn more about how they arrived at that architecture, the proc...

Using tensorflow with big datasets has been an impediment for building deep learning models due to the added complexities of running it in a distributed setting and complicated MLOps code, recent advancements in tensorflow 2, and some extension libraries for Spark has now simplified a lot of this. This talk focuses on how we can leverage the best of both Spark and tensorflow to build machine learning and...

In this session we will present how Anheuser-Busch InBev (Brazil) has been developing and growing an ML platform product to democratize and evolve AI usage within the full company.



Our cutting-edge intelligence product offers a set of tools and processes to facilitate everything from exploratory data analysis to the development of state-of-the-art machine learning algorithm...

Over the last few years two new approaches for data management have developed across the data community: Data Lakehouse and Data Mesh. The Data Lakehouse is an open architecture that pushes the technological advancements of a Data Lake by adding data management capabilities proven by a long history of Data Warehousing practices. Data Mesh on the other hand is addressing data management challenges from an...

In retail, the right quantity at the right time is crucial for success. Many fresh product retailers struggle with bad forecast which causes bad customer satisfaction but also a lot of food waste. In this session we share how an end-to-end demand forecasting solution has helped some of our retailers to improve efficiencies and sharpen fresh product production and delivery planning.

The pre...

Within Wehkamp we required a uniform way to provide reliable and on time data to the business, while making this access compliant with GDPR. Unlocking all the data sources that we have scattered across the company and democratize the data access was of the utmost importance, allowing us to empower the business with more, better and faster data.

Focusing on open source technologies, we've b...

Tredence' s OSA accelerator is a robust quick-start guide that is the foundation for a full Out of Stock or Supply Chain solution. The OSA solution focuses on driving sales through improved stock availability on the shelves. The following components make up the OSA accelerator.



• Identifying OOS Situation: ML models to identify the Out-Of-Stock scenario in a store at a SKU ...

Demand forecasts of Items are integral to the health of Retail Operations, as accurate forecasts lead to improved decision-making and outcomes in replenishment, capacity, and resource planning. Granular Demand/Sales Forecasts at an Item-Store Level often yields more valuable insights and action plans but is often avoided due to the difficulty in predictions at this granularity, especially for slow moving...