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

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. From provisioning the right amount and type of GPUs and CPUs, to selecting the right cluster and cloud provider, to understanding the relationship between quality of experience (QoE) metrics like model precision and serving latency from one hand, and quality of service (QoS) metrics like processing speed, memory size and memory bandwidth from the other, we help you think through the right questions to consider when selecting, fine-tuning and deploying the ML models powering your business AI strategy. 


Speaker

Ghida Ibrahim

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

Since April 2023, Ghida has been the Chief Technology Architect and head of Data at Sector Alarm, one of Europe’s top providers of smart home solutions and a KKR portfolio company. 

Prior to that, she spent 5+ years as a technical lead at Meta/Facebook building AI tools and systems to help scale and optimize Meta’s edge computing infrastructure, used to serve billions of people across Meta family of apps.

Ghida’s experience also includes working for major European Telcos in roles at the intersection of distributed computing and advanced analytics. She holds a PhD and Masters in computer Engineering from Institut Polytechnique de Paris.

Outside of work, Ghida is an expert technical advisor on new technology trends for the World Economic Forum, the French Secretary of Investment, among others. she also occasionally lectures at university and speaks at conferences
 

Read more

Date

Tuesday Apr 9 / 10:35AM BST ( 50 minutes )

Location

Churchill (Ground Fl.)

Slides

Slides are not available

Share

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

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

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

Tuesday Apr 9 / 01:35PM BST

Details coming soon.