REGISTRATION & LIGHT BREAKFAST
WELCOME NOTE & OPENING REMARKS
Setting up MLOps In An FMCG To Realise The Value of AI
ML DESIGN & DEVELOPMENT
Tom Ewing - Saint-Gobain
Machine Learning Engineering: From Prototype to Production
Tom Ewing is a Principal Machine Learning Engineer, at Saint-Gobain, a building materials manufacturer. Tom has created the Machine Learning Engineering team and his work includes productionising a number of rules-based and AI models generating impact and value; developing improved ways of working for the wider Data Science and Analytics team, incorporating coding practices, testing and governance to pave the way for rapid model productionisation; and also creating the underlying architecture to support AI deployment including automated model pipelines and a feature store database.
ML Performance in the Real World: What They Don’t Teach You in School
COFFEE & NETWORKING BREAK
ML FRAMEWORKS & INFRASTRUCTURE
Pavlos Mitsoulis-Ntompos - King
APIs: They Matter More Than You Think in Machine Learning
There’s been a great deal of work in ML research during the last decade. What we've seen the last 3 years is a new movement orthogonal to ML, ML Ops. ML Ops is still in its infancy, but its premise is how to create an ML infrastructure that will promote best practices and expedite ML projects. ML practitioners can be seen as chefs; they need the proper tooling to unveil their talent. However, the interface of this tooling is crucial to make them useful to ML practitioners. In this talk, I’ll provide my view on the importance of APIs and interfaces in Machine Learning.
Pavlos Mitsoulis has 10 years of Machine Learning and Software Engineering experience. Currently, he is an Engineering Manager of ML Ops team at King (part of Activision Blizzard), leading King's central ML Platform. Additionally, he is the creator of Sagify, an open-source library that simplifies training, tuning, evaluating, and deploying ML models to SageMaker.
Natalia Koupanou - Tide
Natalia is an experienced Senior Manager, Lead Data Scientist with a demonstrated history of working in the financial services industry and building established delivery teams. Before starting a career in fintech, Natalia worked in data across a number of tech companies in various sub-sectors such as ecommerce (Zoro) and proptech (Zoopla). Skilled in Strategy, Machine Learning, Data Modeling and Analytical Skills. Natalia enjoys mentoring and blogs about her technical and personal experiences on Medium (medium.com/@nataliakoupanou).
Key ideas for Optimizing ML Training Pipeline and Model Accuracy
Andy McMahon - NatWest Group
Andy is a data scientist and machine learning engineer with extensive experience of taking analytics solutions from ideation to production. Andy has experience in model building, software development and technical management and is particularly interested in the challenge of deploying machine learning based software products at scale. Andy has developed solutions in logistics, energy and now in the financial services industry that have created millions of dollars of value.
• In 2022 Andy was named “Rising Star of the Year” at the British Data Awards.
• In 2021 Andy's book, “Machine Learning Engineering with Python”, was published by Packt.
• In 2019 Andy was named “Data Scientist of the Year” by the Data Science Foundation.
MODEL TRAINING & MANAGEMENT
Building ML Ops Platform - Challenges and Considerations
Chris Sarakasidis - ITV
Modern MLOps: Simplifying and automating ML pipelines using Databricks and Kubernetes in AWS
Christos Sarakasidis is an experienced Machine Learning engineer with a keen interest in modern DevOps, software engineering and cloud computing. He has previously worked in research developing algorithms to solve problems in algebra & topology and helped major tech-driven organisations to build in cloud robust ML solutions. Christos joined in February 2022 ITV and is currently the Lead Machine Learning engineer. His role includes the creation of automated ML solutions to enable data-driven decisions across various ITV departments.
Creating Reproduceable ML Pipelines & Environments
COFFEE & NETWORKING BREAK
TOOLS & TECHNIQUES
Demystifying the MLOps Infrastructure Stack
Embedding Trusted AI Within Your Models
PANEL: From Concept to Production - The Best Opportunities to Utilise MLOps
END OF DAY 1
DOORS OPEN & LIGHT BREAKFAST
Automating ML Workflows: How Can You Benefit?
Making AI Work For You
Ghida Ibrahim - Meta
An Intro To AIOps: How To Scale IT Operations With AI
In this talk, the speaker covers how to leverage AI and quantitative techniques in order to scale and optimize large scale product infrastructure in the cloud or on the edge. In particular, they will explain how techniques like time series forecasting, operations research, and statistical and causal inference could be leveraged to optimize infrastructure investments and resource allocations, enable predictive maintenance, and allow building infrastructure that is more aware of the needs of products such as video, real time messaging and the metaverse
Ghida is a lead quantitative engineer at Meta (previously Facebook) where she uses automated decisioning and advanced analytics to help scale and optimize Meta internal cloud and edge infrastructure, used to serve billions of people across Meta family of apps and products. Prior to joining Meta, Ghida worked for 6+ years in the Telco and media industries in multiple analytics and engineering roles, mainly focusing on optimizing large scale distributed systems. She holds a PhD and master’s (Diplome d’Ingénieur) in computer engineering from Institut Polytechnique de Paris.
Ghida also teaches a course on using AI for scaling IT operations at the university of Oxford. She is a TED speaker and an Expert of the World Economic Forum, providing expertise on the future of computing. In the past, Ghida prepared and delivered the first online course on data science in Arabic attracting 30k+ learners, and built an award-winning platform for connecting refugees to opportunities, among others.
Ghida's expertise is at the intersection of computing infrastructure, data engineering and AI. It covers Edge Computing, Cloud Computing, Content Delivery Networks, time series analysis, operations research, statistical inference, ETL, expert systems, recommender systems, machine learning and federated learning, among others.
COFFEE & NETWORKING BREAK
SCALING & MANAGEMENT
Harpal Sahota - MATCHESFASHION
The journey of MLOps at MATCHESFASHION
MLOps at MATCHESFASHION started to gain traction approximately two years ago. Fast-forward those two years and we’re developing our own bespoke framework to monitor data/model drift, reproduce exact datasets using CDC/delta and we now have several models in production serving customers in real-time. This is scratching the surface of what we’re currently working on in the MLOps space. This talk will walk you through the conception of the framework, its uses and how MLOps is used within the company
Harpal started working as a Lead Data Scientist at MATCHESFASHION in July 2020. As a Lead, he was involved in building the Data Science team from scratch, educating stakeholders on the value of Data Science and was a key contributor to the digital transformation of business operations utilising Machine Learning and Artificial Intelligence. He has a PhD in Computation Biology and has 10 years experience in solving complex business problems with innovative data solutions.
There Is No Such Thing As MLOps
Model Management, Deployment, Lineage & Monitoring
Deploy & Accelerate Optimisations to Improve Your Business Outcomes
Advanced ML Methods For Automating Image Labeling
A Framework For Understanding, Exploration and Forecasting
PANEL: The ROI of MLOps: Do the Pros Outweigh the Cons?
END OF SUMMIT