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Register your interest for 2024

Understand the Practice of Data Engineering in the Real World

As data science extends its reach across an enterprise, the need for better management, workflow, production and deployment practices increase. The challenges of deploying and monitoring models in production, managing data science workflows and teams, and understanding ROI are a few of the issues organizations wrestle with.

Learn best practices for effective data science management

Sessions in this broad focus area will look at uses cases, best practices, and stories from the field to show how to effectively incorporate data science practice into the wider business process.  This focus area will look beyond data sourcing and modeling towards the many challenges teams need to overcome to effectively apply data science in their organization.

Register your interest for 2024

ODSC EUROPE 2024 CONFERENCE

Register your interest for 2024

What You'll Learn

Data science has many focus areas.  The goal of this track is to accelerate your knowledge of data science through a series of introductory level training sessions, talks, tutorials and workshops on the most important data science tools and topics.  

  • Machine Learning Pipelines

  • Kubeflow & Kubernetes

  • Automated Machine Learning

  • Data Science Architecture

  • Debugging Machine Learning

  • Data Gathering

  • Data Analysis

  • Data Transformation & Preparation

  • Model Training & Development

  • Model Validation, Monitoring, and Re-training

  • Best Practices & Uses Cases

Past MLOps & Data Engineering Speakers

Senior Cloud AdvocateMicrosoft

Henk Boelman

Henk is a Cloud Advocate specializing in Artificial intelligence and Azure with a background in application development. He is currently part of the AI cloud advocate team and based in the Netherlands. Before joining Microsoft, he was a Microsoft AI MVP and worked as a software developer and architect building lots of AI powered platforms on Azure.

He loves to share his knowledge about topics such as DevOps, Azure and Artificial Intelligence by providing training courses and he is a regular speaker at user groups and international conferences.

Build and Deploy PyTorch models with Azure Machine Learning (Keynote)

Product Manager, MLOps and TF OSSGoogle

Robert Crowe

A data scientist and ML enthusiast, Robert has a passion for helping developers quickly learn what they need to be productive. Robert is currently the Senior Product Manager for TensorFlow Open-Source and MLOps at Google and helps ML teams meet the challenges of creating products and services with ML. Previously Robert led software engineering teams for both large and small companies, always focusing on moving fast to implement clean, elegant solutions to well-defined needs. You can find him on LinkedIn at robert-crowe.

MLOps v LMOps – What’s Different?(Talk)

 

Senior ML EngineerThoughtworks

Meissane Chami

Meissane Chami serves ThoughtWorks, Inc. as a Senior ML Engineer, advising and developing innovative data science and machine learning solutions from proof of concept to production. She has gained expertise setting up innovation frameworks and conducting fast cycle proof of concepts. Her primary areas of expertise are in Natural Language processing, MLOps, DevOps, cloud computing, containerisation and Python. She holds a MSc degree in Machine Learning and Data Science form University College London School of Engineering.

ML Governance: A Lean Approach(Talk)

Senior Solutions ArchitectDatabricks

Dillon Bostwick

Dillon Bostwick is a Solutions Architect at Databricks, where he’s spent the last five years advising customers ranging from startups to Fortune 500 enterprises. He currently helps lead a team of field ambassadors for streaming products and is interested in improving industry awareness of effective streaming patterns for data integration and production machine learning. He previously worked as a product engineer in infrastructure automation.

Getting Up to Speed on Real-Time Machine Learning(Talk)

Head of Developer RelationSeldon

Ed Shee

Ed Shee, Head of Developer Relations at Seldon. Having previously led a tech team at IBM, Ed comes from a cloud computing background and is a strong believer in making deployments as easy as possible for developers. With an education in computational modelling and an enthusiasm for machine learning, Ed has blended his work in ML and cloud native computing together to cement himself firmly in the emerging field of MLOps.

Avoiding Crisis With Model Explainability(Talk)

Principal Data EngineerThoughtworks

Ryan Dawson

Ryan Dawson is a technologist passionate about data. Ryan works with clients on large-scale data and AI initiatives, helping organizations get more value from data. His work includes strategies to productionize machine learning, organizing the way data is captured and shared, selecting the right data technologies and optimal team structures, as well as writing the code to make it happen. He has over 15 years of experience and, as well as many widely read articles about MLOps, software design, and delivery. is author of the Thoughtworks Guide to Evaluating MLOps Platforms.

ML Governance: A Lean Approach(Talk)

Senior Enterprise Solutions ArchitectDatabricks

Avinash Sooriyarachchi

Avinash Sooriyarachchi is a Senior Solutions Architect at Databricks. His current work involves working with large Retail and Consumer Packaged Goods organizations across the United States and enabling them to build Machine Learning based systems. His specific interests include streaming machine learning systems and building applications leveraging foundation models. Avi holds a Master’s degree in Mechanical Engineering and Applied Mechanics from the University of Pennsylvania.

Getting Up to Speed on Real-Time Machine Learning(Talk)

Director of Product, AI Cloud SolutionsGraphcore

Tim Santos

Tim is leading Graphcore’s Cloud Solutions product to help AI & ML software development teams build AI products and deploy ML capabilities in production. Tim has worn many hats in his career, from being a research engineer, data scientist and leading MLOps teams. Along the way, he’s gained experience across all stages of the development lifecycle, taking AI applications from experimentation to deployment.

Generative AI in Practice: How to build your own Stable Diffusion API(Workshop)

Director of Data ScienceFinancial Times

Leanne Fitzpatrick

Leanne is Director of Data Science at the Financial Times and is a passionate, experienced data leader having built and developed empowered data science and analytics teams for a variety of businesses; from startups to large organisations. Leanne is in her element when developing and implementing strategic, technical and cultural solutions to getting data & analytical capabilities into the operational ecosystem. She is an active part of the data and technology community, sharing innovation and insights to encourage best practice, from Manchester, UK to Austin, TX and is an Advisory Panel Board Member. Outside of all things data you can ask Leanne about her golf swing (it’s not good – yet), her passion for American Football (specifically the Cincinnati Bengals), her latest sewing project, and her love for good music, food and whisky.

Upgrading your engine without stopping the car: How the FT is improving our ML deployment practice with minimum disruption(Talk)

Inventor and Creator of PyCaret | Product Director - Artificial Intelligenceantuit.ai

Moez Ali

Innovator, Technologist, and a Data Scientist turned Product Manager with proven track record of building and scaling data products, platforms, and communities. Experienced in building and leading teams of data scientists, data engineers, and product managers. Strongly opinionated tech visionary and a thought partner to C-level leadership.

Moez Ali is an inventor and creator of PyCaret. PyCaret is an open-source, low-code, machine learning software. Ranked in top 1%, 8M+ downloads, 7K+ GitHub stars, 100+ contributors, and 1000+ citations.

Globally recognized personality for open-source work on PyCaret. Keynote speaker and top ten most-read writer in the field of artificial intelligence. Teaching AI and ML courses at Cornell, NY and Queens University, CA. Currently building world’s first hyper-focused Data and ML Platform.

Automate Machine Learning Workflows with PyCaret 3.0(Workshop)


Why Attend

Immerse yourself in talks and workshops on AI in MLOps and Data Engineering

With numerous introductory level workshops, you get hands-on experience to quickly build up your skills

Post-conference, get access to recorded talks online and learn from over 100+ high-quality recording sessions that let you review content at your own pace 

Take time out of your busy schedule to accelerate your knowledge of the latest advances in data science

Learn directly from world-class instructors who are the authors and contributors to many of the tools and languages used in data science today

Meet hiring companies, ranging from hot startups to Fortune 500, looking to hire professionals with data science skills at all levels

Get speaker insights and training in AI frameworks such as TensorFlow, MXNet, PyTorch, Spark, Storm, Drill, Keras, and other AI platforms

Get access to other focus area content, including ML/DL, Data Visualization Big Data, and Open Data Science

Who Should Attend

  • Data scientists looking to configure data pipelines for building and deploying Machine Learning algorithms.

  • Data scientists seeking to learn automation skills for experimentation and execution of Machine Learning algorithms

  • Anyone interested in understanding underlying frameworks in terms of machine configurations, storage indexing or data loading 

  • Business professionals and industry experts looking to understand data science in practice

  • Software engineers and technologists who need to configure and administer Machine Learning Analytics related technologies such as Kafka, Spark, and Hadoop

  • CTO, CDS, and other managerial roles that require a bigger picture view of data science

  • Technologists in the field of MLOps looking to break into data science

  • Students and academics looking for more practical applied training in data science tools and techniques

ODSC EUROPE Hybrid Conference 2024

Register your interest for 2024

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