Architecture for the Age of AI

Artificial intelligence, especially Machine Learning, Deep Learning, and Large Language Models, is increasingly becoming one of the critical factors to the success of our modern applications.

This track focuses on sharing practitioner-driven insights on what works (and what doesn't) on AI-focused software architectures, enabling you to build and sustain the AI-based systems of the future.

We will explore the latest trends and techniques for building modern software architecture for AI systems and applications.

From this track

Session

When AIOps Meets MLOps: What Does It Take To Deploy ML Models at Scale

Tuesday Apr 9 / 10:35AM BST

In this talk, we introduce the concept of AIOps referring to using AI and data-driven tooling to provision, manage and scale distributed IT infra. We particularly focus on how AIOps can be leveraged to help train and deploy machine learning models and pipelines at scale.

Speaker image - Ghida Ibrahim

Ghida Ibrahim

Chief Architect, Head of Data @Sector Alarm Group, Ex-Facebook/Meta

Session AI/ML

Mind Your Language Models: An Approach to Architecting Intelligent Systems

Tuesday Apr 9 / 11:45AM BST

As large language models (LLMs) emerge from the realm of proof-of-concept (POC) and into mainstream production, the demand for effective architectural strategies intensifies.

Speaker image - Nischal HP

Nischal HP

Vice President of Data Science @Scoutbee, Decade of Experience Building Enterprise AI

Session

Connecting the Dots: Applying Generative AI (Limited Space - Registration Required)

Tuesday Apr 9 / 01:35PM BST

Details coming soon.

Session

Flawed ML Security: Mitigating Security Vulnerabilities in Data & Machine Learning Infrastructure with MLSecOps

Tuesday Apr 9 / 02:45PM BST

The operation and maintenance of large scale production machine learning systems has uncovered new challenges which require fundamentally different approaches to that of traditional software.

Speaker image - Adrian Gonzalez-Martin

Adrian Gonzalez-Martin

Senior MLOps Engineer, Previously Leader of the MLServer Project @Seldon

Session

Large Language Models for Code: Exploring the Landscape, Opportunities, and Challenges

Tuesday Apr 9 / 03:55PM BST

In the rapidly evolving landscape of software development, Large Language Models (LLMs) for code have emerged as a groundbreaking tool for code completion, synthesis and analysis.

Speaker image - Loubna Ben Allal

Loubna Ben Allal

Machine Learning Engineer @Hugging Face

Session AI/ML

Lessons Learned From Building LinkedIn’s AI Data Platform

Tuesday Apr 9 / 05:05PM BST

Taking AI from lab to business is notoriously difficult. It is not just about picking which model flavor of the day to use. More important is making every step of the process reliable and productive.

Speaker image - Felix GV

Felix GV

Principal Staff Engineer @LinkedIn

Date

Tuesday Apr 9 / 10:35AM BST

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Track Host

Fabiane Nardon

Data Expert, Java Champion & Data Platform Director @totvs

Fabiane is a seasoned computer scientist with years of experience building data-intensive solutions that have made a tangible impact across various industries. She was the chief architect of the Sao Paulo Healthcare Information System project, which was then considered the world’s largest JavaEE application and won the 2005 Duke’s Choice Award for its innovation. At Tail, where she served as partner and CTO, she helped pioneer the use of data science and machine learning in digital advertising.

Fabiane has been an active contributor to the open-source community, having led the JavaTools Community at java.net, where over 800+ projects were created. A frequent speaker at major tech conferences, she is well-regarded for sharing practical insights and real-world experience. She has also served on the program committees for conferences like JavaOne, TDC and QCon, helping to shape industry conversations.

Named a Java Champion by Sun Microsystems in recognition of her contributions to the Java ecosystem, Fabiane now works as the Data Platform Director at Totvs, Brazil’s largest tech company. Her current work focuses on simplifying the development of data-driven solutions, making it easier for organizations to harness the power of data to solve real-world challenges.

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