MACHINE LEARNING WEEK EUROPE

Deep Learning World is the premier conference covering the commercial deployment of deep learning. The event’s mission is to foster breakthroughs in the value-driven operationalization of established deep learning methods. As part of Machine Learning Week, Deep Learning World will be held alongside five Predictive Analytics World conferences: PAW Business, PAW Financial, PAW Healthcare, PAW Industry 4.0. and PAW Climate. This conference covers generative AI – including large language models like ChatGPT and image generators like DALL-E 2. Generative AI is powered by deep learning. As marked on the conference agenda, several of this year’s sessions – and one workshop – pertain to generative AI.

To effectively manage the explosion of data volumes and deliver high-performance analytics, many businesses are choosing to move to a data lakehouse, an architecture that combines the flexibility and scalability of data lake storage with the data management, governance, and analytics capabilities typically associated with a data warehouse.

The avalanche of data generated by companies and their customers should be a competitive advantage, but most companies are struggling to manage it all. Data accessibility, quality, governance and security are all critical for operationalizing machine learning applications, so the stakes are high. Emerging frameworks are helping us come to grips with these issues but the answers go beyond just technology.

Come to San Diego and participate in an interactive summit designed for business, data science, and IT leaders who are responsible for selecting, managing, and delivering value from analytics applications, AI/ML, business intelligence, and the data that fuels them. Organizations count on analytics to innovate, attract and retain customers, improve efficiency, and determine risk amid today’s unpredictable and fast-changing economic landscape. However, TDWI finds that many struggle to move ahead. Organizations face difficulties that lead to user frustration, errors, and higher costs. As your analytics models, workloads, and data pipelines grow in number and complexity, orchestrating the journey into production and operationalization is a challenge.