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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.

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 Operations (MLOps) 

  • Data Engineering

  • Experimentation to Production

  • Data Science DevOps

  • Agile Data Science

  • Data Science Architecture

  • Runtime Pipelines

  • Model Monitoring & Auditing

  • Manage Data Science in Your Organization

  • Collaborative Practices and Tools

  • Team Management

  • Data Science Workflows

  • Data Provenance & Governance

  • Best Practices & Uses Cases

  • Cross Industry & Cross Enterprise Challenges

ODSC APAC Virtual Conference 2024

Register your Interest

Confirmed Data Engineering & MLOps Speakers

Head of Data Science Evangelism and MarketingOuterbounds

Hugo Bowne-Anderson, PhD

Hugo Bowne-Anderson is a data scientist, writer, educator & podcaster. His interests include promoting data & AI literacy/fluency, helping to spread data skills through organizations and society and doing amateur stand up comedy in NYC. He does many of these at DataCamp, a data science training company educating over 3 million learners worldwide through interactive courses on the use of Python, R, SQL, Git, Bash and Spreadsheets in a data science context. He has spearheaded the development of over 25 courses in DataCamp’s Python curriculum, impacting over 170,000 learners worldwide through my own courses. He hosts and produce the data science podcast DataFramed, in which he uses long-format interviews with working data scientists to delve into what actually happens in the space and what impact it can and does have. He earned PhD in Mathematics from the University of New South Wales, Australia and has conducted biomedical research at the Max Planck Institute in Germany and Yale University, New Haven.

 

AI ResidentMeta

Danni Li

Danni Li is an AI Resident at Meta. She is interested in building efficient AI systems and applications to solve real-world problems. Her current research focuses on on-device ASR models and optimization techniques.

ML on-device: Building Efficient Models(Talk)

Co-founder & CEOOuterbounds

Ville Tuulos

Ville has been developing infrastructure for machine learning for over two decades. He has worked as an ML researcher in academia and as a leader at a number of companies, including Netflix where he led the ML infrastructure team that created Metaflow, a popular open-source framework for data science infrastructure. He is a co-founder and CEO of Outerbounds, a company developing modern human-centric ML. He is also the author of the book Effective Data Science Infrastructure, published by Manning.

Human-Friendly, Production-Ready Data Science with Metaflow(Talk)

Principal Program ManagerMicrosoft

Seth Juarez

My name is Seth Juarez. I currently live near Redmond, Washington and work for Microsoft.​

I received my Bachelors Degree in Computer Science at UNLV with a Minor in Mathematics. I also completed a Masters Degree at the University of Utah in the field of Computer Science. I currently am interested in Artificial Intelligence specifically in the realm of Machine Learning. I currently work as a Program Manager in the Azure Artificial Intelligence Product Group.​

I’ve been married now for 21 years to a fabulously talented woman and have two beautiful daughters, and two feisty sons.

Session Title: Ask the Experts! ML Pros Deep-Dive into Machine Learning Techniques and MLOps

Abstract: Experienced machine learning engineers and data scientists care about ways to easily get their models up and running quickly and share ML assets across teams for collaboration. Collaborate and streamline the management of thousands of models across teams with new, innovative features in Azure Machine Learning. Come and join us in this interactive session with our product experts and get your questions answered on the latest capabilities in Azure Machine Learning!

Domain Chapter Lead Spark New Zealand

Habib Baluwala

Habib Baluwala, a dedicated data leader with a PhD from Oxford, serves as the Domain Chapter Lead at Spark New Zealand. With over 15 years of experience in data engineering and data science, he has developed a deep understanding of how data can drive business success. Habib’s exceptional leadership and communication skills enable him to effectively engage with stakeholders, lead high-performing teams, and drive data-driven decision-making across the organization. He actively explores AI governance for responsible and ethical AI implementation. Committed to continuous learning and teamwork, his expertise is exemplified by his Chief Data Officer certification. A seasoned leader, Habib’s unique combination of technical expertise and leadership skills empowers him to deliver innovative data solutions that support business growth.

Enabling AI Transformation: MLOps Infrastructure, AI Command Centre, and Data Science in Telecommunication(Talk )

Principal Developer Advocate, Data EngineeringAmazon Web Services

Suman Debnath

Suman Debnath is a Principal Developer Advocate (Data Engineering) at Amazon Web Services, primarily focusing on Data Engineering, Data Analysis and Machine Learning. He is passionate about large scale distributed systems and is a vivid fan of Python. His background is in storage performance and tool development, where he has developed various performance benchmarking and monitoring tools.

Build Classification and Regression Models with Spark on AWS(Workshop)


Why Attend

Accelerate and broaden your knowledge of key areas in data science, including deep learning, machine learning, and predictive analytics

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 post conference

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

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

Get hands-on training in the the tools and woklfow essentatio for MLOps and data engineering.

Network at our numerous lunches and events to meet with data scientists, enthusiasts, and business professionals.

Get access to other focus area content, including machine learning & deep learning, data visualization, and much more

Who will attend

  • Data scientists moving beyond model experimentation looking to understand production workflow

  • Data scientists seeking to improve the overall practice of management and development

  • Anyone interested in understanding better collaborative and agile management techniques as applied to data science

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

  • Software engineers and technologists who need to work with data science workflows and understand the unique requirements of these systems

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

  • Technologists in the field of DevOps, databases, project management and others looking to break into data science

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

ODSC Newsletter

Stay current with the latest news and updates in open source data science. In addition, we’ll inform you about our many upcoming Virtual and in person events in Boston, NYC, Sao Paulo, San Francisco, and London. And keep a lookout for special discount codes, only available to our newsletter subscribers!


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