The global AI market size is projected to grow from ~ USD 380 billion in 2022 to USD ~1.4K billion in 2029 at a compound annual growth rate of 20.1% in the forecast period. With the surge in demand and interest in AI-powered technologies, many new trends are emerging in this space. This QCon London track invites tech professionals and executives, who are involved with the AI technology in some capacity, to see what’s next in the realm of Artificial Intelligence and Machine Learning trends.
From this track
Strategy & Principles to Scale and Evolve MLOps @DoorDash
Wednesday Mar 29 / 10:35AM BST
MLOps has become a major enabler to successfully operationalize ML applications and for ML practitioners to realize the power of ML to bring impact to business. The journey to implementing MLOps will be unique to each company.
Sr. Engineering Manager @DoorDash & Author of Beginning Apache Spark 3, Speaker and Conference Committee Chair
Cognitive Digital Twins: A New Era of Intelligent Automation
Wednesday Mar 29 / 11:50AM BST
Traditionally, Digital Twins have been helping businesses make data-driven decisions, increase efficiency, and improve the overall performance of their physical assets.
Intelligent Industry Lead @Capgemini
Responsible AI: From Principle to Practice!
Wednesday Mar 29 / 01:40PM BST
Enabling responsible development of artificial intelligent technologies is one of the major challenges we face as the field moves from research to practice. Researchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by the use of machine learn
Principal PM Manager @Microsoft
Graph Learning at the Scale of Modern Data Warehouses
Wednesday Mar 29 / 02:55PM BST
Data warehouses have become a staple for enterprises, providing a wealth of information that can be harnessed to improve decision-making through the use of machine learning (ML).
Founding Engineer @Kumo.ai
Simplifying Real-Time ML Pipelines with Quix Streams: An Open Source Python Library for ML Engineers
Wednesday Mar 29 / 04:10PM BST
As data volume and velocity continue to increase, the need for real-time machine learning (ML) is becoming more pressing. However, building real-time ML pipelines can be complex and time-consuming, requiring expertise in both ML and streaming application development.
CTO & Co-Founder @Quix