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

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. In this talk, we will explore the current developments of these models, understand how they are trained, and how they can be leveraged with custom codebases. Additionally, we will address potential risks and challenges, including privacy concerns, using insights from projects like the BigCode initiative and StarCoder models.


Loubna Ben Allal

Machine Learning Engineer @Hugging Face

Loubna Ben Allal is a Machine Learning Engineer in the Science team at Hugging Face working on Large Language Models for code & synthetic data generation. She is part of the core team behind the BigCode Project and has co-authored The Stack dataset and StarCoder models for code generation. Loubna holds Mathematics & Deep Learning Master's Degrees from Ecole des Mines de Nancy and ENS Paris Saclay.

Read more
Find Loubna Ben Allal at:


Tuesday Apr 9 / 03:55PM BST ( 50 minutes )


Fleming (3rd Fl.)


From the same track

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

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


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


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


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

Tuesday Apr 9 / 01:35PM BST

Details coming soon.