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


Strategy & Principles to Scale and Evolve MLOps @DoorDash

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.

Hien Luu

Sr. Engineering Manager @DoorDash


Responsible AI: From Principle to Practice!

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

Mehrnoosh Sameki

Principal PM Manager @Microsoft


Cognitive Digital Twins: A New Era of Intelligent Automation

Traditionally, Digital Twins have been helping businesses make data-driven decisions, increase efficiency, and improve the overall performance of their physical assets.


Wednesday Mar 29 / 10:00AM PDT



QCon London 2023
March 27 - 29, 2023


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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 AnalysisFairlearn 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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