Emerging AI and Machine Learning Trends

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

Session Machine Learning

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.

Speaker image - Hien Luu
Hien Luu

Sr. Engineering Manager @DoorDash & Author of Beginning Apache Spark 3, Speaker and Conference Committee Chair

Session Digital Twins

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.

Speaker image - Yannis Georgas
Yannis Georgas

Intelligent Industry Lead @Capgemini

Session AI

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

Speaker image - Mehrnoosh Sameki
Mehrnoosh Sameki

Principal PM Manager @Microsoft

Session Graphs

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).

Speaker image - Subramanya Dulloor
Subramanya Dulloor

Founding Engineer @Kumo.ai

Session python

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.

Speaker image - Tomáš Neubauer
Tomáš Neubauer

CTO & Co-Founder @Quix

Date

Wednesday Mar 29 / 10:35AM BST

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Track Host

Mehrnoosh Sameki

Principal PM Manager @Microsoft

Mehrnoosh Sameki is a principal PM manager at Microsoft, where she leads emerging Responsible AI technology and tools and for the Azure Machine Learning platform. She has cofounded Error Analysis, Fairlearn and Responsible AI Toolbox and has been a contributor to the InterpretML offering. She earned her PhD degree in computer science at Boston University, where she currently serves as an adjunct assistant professor, offering courses in responsible AI. Previously, she was a data scientist in the retail space, incorporating data science and machine learning to enhance customers’ personalized shopping experiences.

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