ODSC Europe Virtual Conference | June 8 - 10, 2021

Data Engineering & MLOps

Focusing on the practice, engineering, workflows and DataOps of data science
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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.

ODSC EUROPE 2021

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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

ODSC EUROPE Virtual Conference 2021 | June 8 - 10

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Why Attend

Software Engineer, Core DeveloperInria, Scikit-learn

Olivier Grisel

Olivier Grisel is a machine learning engineer at Inria. He is a member of the team of maintainers of the scikit-learn project. Scikit-learn is an Open Source machine learning library written in Python. His work is supported by the Fondation Inria and its partners.

Hands-on Machine Learning Engineer with scikit-learn(Full-Day Training)

David Talby: NLP for healthcare
CTOJohn Snow Labs

David Talby, PhD

David Talby is a chief technology officer at Pacific AI, helping fast-growing companies apply big data and data science techniques to solve real-world problems in healthcare, life science, and related fields. David has extensive experience in building and operating web-scale data science and business platforms, as well as building world-class, Agile, distributed teams. Previously, he was with Microsoft’s Bing Group, where he led business operations for Bing Shopping in the US and Europe and worked at Amazon both in Seattle and the UK, where he built and ran distributed teams that helped scale Amazon’s financial systems. David holds a Ph.D. in computer science and master’s degrees in both computer science and business administration.

Model Governance: A Checklist for Getting AI Safely to Production(Talk)

Co-Founder and CTOIguazio

Yaron Haviv

Yaron Haviv is a serial entrepreneur who has been applying his deep technological experience in data, cloud, AI and networking to leading startups and enterprise companies since the late 1990s. As the co-founder and CTO of Iguazio, Yaron drives the strategy for the company’s data science platform and leads the shift towards real-time AI. He also initiated and built Nuclio, a leading open source serverless platform with over 3,400 Github stars and MLRun, Iguazio’s open source MLOps orchestration framework.

Prior to co-founding Iguazio in 2014, Yaron was the Vice President of Datacenter Solutions at Mellanox (now NVIDIA), where he led technology innovation, software development and solution integrations. He was also the CTO and Vice President of R&D at Voltaire, a high-performance computing, IO and networking company which floated on the NYSE in 2007. Yaron is an active contributor to the CNCF Working Group and was one of the foundation’s first members. He presents at major industry events and writes tech content for leading publications including TheNewStack, Hackernoon, DZone, Towards Data Science and more.

MLOps Spotlight: Scaling NLP Pipelines at IHS Markit(Track Keynote)

Building Operational Pipelines for Machine and Deep Learning(Talk)

EvangelistClearML

Ariel Biller, PhD

Researcher first, developer second, in the last 5 years Ariel worked on various projects from the realms of quantum chemistry, massively-parallel supercomputing and deep-learning computer-vision. With AllegroAi, he helped build an open-source R&D platform (Allegro Trains), and later went on to lead a data-first transition for a revolutionary nanochemistry startup (StoreDot). Answering his calling to spread the word on state-of-the-art research best practices, He recently took up the mantle of Evangelist at ClearML. Ariel received his PhD in Chemistry in 2014 from the Weizmann Institute of Science. With a broad experience in computational research, he made the transition to the bustling startup scene of Tel-Aviv, and to cutting-edge Deep Learning research.

Deconstructing MLOps (Track Keynote)

Managing DirectorUntrite

Kamila Hankiewicz

Kamila Hankiewicz is a Managing Director of Untrite, an AI company helping companies make better use of data they already have; we provide an AI engine which pulls data from silos and understands the links and relevance between them. Kamila is a vivid advocate for diversity and empowering women in technology; she co-founded NGO Girls in Tech London and Poland. The local chapter has an active member base of more than 9 000 people, with more than 125 000 globally.
Kamila’s past life includes working as a Management Consultant with a focus on banking, where she was involved in digital transformation projects (such as Santander’s £1.65bn worth project Rainbow). Kamila is a frequent speaker on the subject of humanising work with use of AI. Her latest talks include those for BigDataLDN, Rasa, Future of AI and Women in AI. She hosts “Humans of AI” video interviews with prominent people solving some of the world’s toughest problems with the use of AI – https://www.youtube.com/channel/UC7qPUVnjrzb4oFwtAmDOTtw. Some of her guests include: Lord Tim Clement-Jones from House of Lords, David Barber from UCL, Rana el Kaliouby from Affectiva and many more.

The Missing Link: How AI Can Help Create a Safer Society and Better Businesses (Talk)

Chief ScientistThe Institute for Ethical AI & Machine Learning

Alejandro Saucedo

Alejandro is the Director of Machine Learning Engineering at Seldon Technologies, where he leads large scale projects implementing open source and enterprise infrastructure for Machine Learning Orchestration and Explainability. Alejandro is also the Chief Scientist at the Institute for Ethical AI & Machine Learning, where he leads thedevelopment of industry standards on machine learning bias, adversarial attacks and differential privacy. With over 10 years of software development experience, Alejandro has held technical leadership positions across hyper-growth scale-ups and has delivered multi-national projects with top tier investment banks, magic circle law firms and global insurance companies. He has a strong track record building departments of machine learning engineers from scratch, and leading the delivery of large-scale machine learning system across the financial, insurance, legal, transport, manufacturing and construction sectors (in Europe, US and Latin America).

Production Machine Learning Monitoring: Principles, Patterns and Techniques(Talk)

Marie Skłodowska-Curie Research FellowUniversity of Munich

Julia Schulte-Cloos, PhD

Julia Schulte-Cloos is a Marie Skłodowska-Curie funded LMU Research Fellow at the Geschwister Scholl Institute of Political Science at LMU Munich. Her main research interests lie in comparative politics, political behavior, research methodology, and reproducibility. Julia Schulte-Cloos has earned her PhD from the European University Institute.

Head of Data and InformaticsRehab Neural Engineeering Labs, University of Pittsburgh

Max Novelli

Max Novelli is a Software Engineer, Data Architect, and Data Manager with a “Laurea” degree from Politecnico di Milano, Italy. He currently works as “Head of Informatics and Data” at Rehab Neural Engineering Lab at the University of Pittsburgh.
His latest interests are in Big Data and its management, structure, visualization, and curation applied to research data — although he never lets go any opportunity to play with hardware and customized experimental equipment. In his current position, he is responsible for the entire lab’s IT infrastructure and the safety, integrity, validation, and curation of experimental data. He is also leading R&D projects spanning from data visualization to data analysis and translating them into viable production tools. His focus is in developing visual tools to explore data structure and to assess the integrity of complex experimental data as well as using neural networks to further study and prove specific experimental results. He has been heavily involved in publishing open-access large datasets into public domain under the Open Science initiative of National Institutes of Health.
When Max is not lost in “computer land,” he enjoys spending time with his family and friends, mountain–biking, hiking trails, swimming, walking (better on the beach), cross-country skiing, eating good food, sipping good wines, and drinking good espresso. He also invests a considerable amount of energy practicing, teaching, and experimenting with yoga and body movement.  Lately, he has discovered rock climbing and is trying to perfect his climbing skills.

What Do I See in This Data? Visual Tools to Enhance Data Understanding(Talk)

Sr. Principal Data Engineering ArchitectRed Hat

Guillaume Moutier

Guillaume Moutier is a Sr. Principal Data Engineering Architect at Red Hat, focusing his work on data services, AI/ML workloads and data science platforms. Former CTO of Laval University in Canada, he is constantly looking for and promoting new and innovative solutions, but always with a focus on usability and business alignment brought by 20 years of IT management experience.

Smart City Data Pipeline, an Edge to Core Data Story(Talk)

Senior Product ManagerMicrosoft

Saurya Das

Saurya Das is a PM in the Azure ML organization focused on the Hybrid strategy and Kubernetes.

Enterprise ready ML Model Training on Hybrid Cloud, leveraging Kubernetes (Talk)

Lead Data ScientistDataswati

Luis Blanche

Luis spent his education and early career in the realm of Transport (Civil Engineering Diploma, MSc Transport from Imperial College London, French Civil Aviation Authority) but he took the train of data science and machine learning in 2014 with Kaggle and Coursera as teachers. He spent about 3 years as a Data Scientist at Quantmetry, a Data Science and AI Consultancy based in Paris where he helped several big companies in multiple sectors develop solutions involving Natural Language Processing, Geospatial data, and Time series. He has recently joined Dataswati to take the lead of the Data Science team. At Dataswati, they develop a SAAS called PowerOP that offers agro-industrial companies easy integration and visualization of their production data as well as explainable and actionable AI services like quality analysis, smart alerting and recipe or settings recommendation.

Build an ML Pipeline with Airflow and Kubernetes(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 Virtual Conference 2021 | June 8 - 10

Register Now
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