Overview

Machine learning applications are rapidly adopted by industry leaders in any field. The growth of investment in AI-driven solutions created new challenges in managing Data Science and ML resources, people and projects as a whole. The discipline of managing applied machine learning teams, requires a healthy mix between agile product development tool-set and a long term research oriented mindset. The abilities of investing in deep research while at the same time connecting the outcomes to significant business results create a large knowledge based on management methods and best practices in the field. The Workshop on Applied Machine Learning Management brings together applied research managers from various fields to share methodologies and case-studies on management of ML teams, products, and projects, achieving business impact with advanced AI-methods.

Important Dates

Call for talk proposals

The workshop focuses on main aspects of a successful ML resource management: project lifecycle, people management and ML quality and excellence. We focus on a combination of soft skills applications along with data-driven and empirical approaches to efficiently resolve ML management challenges.

The ML projects life-cycle management include research resources allocation and collaboration with product development, to achieve innovative and applicable outcomes. We solicit presenting real case-studies and high level working model proposals. At the same time, the people management aspects include the unique properties of ML talents and the specific challenges in building AI organisations, fostering research culture and growing research driven individuals in a business driven environment. Finally, the ML excellence topic include aspects of high-quality ML models and working processes, together with tooling and best practices to ensure them.

We solicit talk proposals for the plenary talks session. The proposals should be 0.5-4 pages. We also invite panel round-table discussion proposals. Please include in the submission:

• Description of the talk/panel discussion:
  • Title
  • Abstract of the talk proposal or round table proposal
  • Potential discussion points
  • An explanation about relevance of this talk/panel discussion to the workshop
• Infor about the presenter:
  • A short bio of the main presenter (~100 words)
  • A brief company or project portrait (~60 words)
• Optional:
  • References to any existing public materials by the authors on the discussed topic

Proposals should be submitted electronically via Easychair in the following link. Please indicate if you are submitting for a talk, a roundtable. The organizers reserve the right to reassign a talk to roundtable discussion. The review process is single-blind, therefore please include the author details in the submission.

Topics of Interest

The key target audience for this workshop are ML leaders, in different industries and academia, ranging from small teams to department and company leaders. Moreover, the workshop can provide unique insights to any ML practitioner about the processes of managing applied research and share knowledge and ideas between different organisations. While hosted at an academic venue, the applied nature of the workshop allows to apply data-driven approaches on the art of machine learning management.

• Managing Machine Learning Projects
  • ML Projects life-cycle management
  • Research management
  • Collaboration with product development
  • Integration of ML solution in organization
  • Agile Data Science
  • Case studies and evaluation
  • Presenting ML solutions
• People Management in ML
  • Hiring and building ML teams
  • ML talent development
  • Building mission based teams
  • Culture of applied ML
  • Community building
  • Stakeholder management in ML
  • Measuring success in DS team
• ML Excellence
  • Maintaining quality in ML
  • System and infrastructure management
  • Best practices in ML workflow
  • Knowledge management
  • ML assets management
• ML as a product
  • How to make a successful product from ML tech
  • Challenges of measuring value of ML
  • Stages of ML product development
• **Generative AI for ML managment
• GenAI tools for team managment
• GenAI effects on delivery and time managment

How to attend

The workshop is a part of the ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING that will happen in Barcelona, August 26th, 2024, more information can be found on the official site of the conference: https://kdd.org/kdd2024/. In order to attend the workshop you need to register to the main conference. Registration will open soon. You don’t need to submit a paper if you want to attend. The conference will be in person.

2024 Program

Invited speakers

Priya Ponnapalli, Google, Senior Director

Priya Ponnapalli is a senior director of engineering at Google, where she is responsible for building AI/ML infrastructure and services to accelerate research, and translate breakthroughs into products; this includes infrastructure and tooling for building and launching responsible AI models and applications at scale.

Prior to this, Ponnapalli was a director of applied science at Amazon Web Services (AWS), where she led the Amazon Machine Learning Solutions Lab, a global organization that works with AWS’ largest and most strategic customers to solve their business needs using machine learning across all industries: from improving fan engagement for sports organizations such as the National Football League (NFL) and Formula 1, to accelerating drug discovery for healthcare and life sciences customers such as Janssen. Priya joined AWS from Eigengene, a personalized-medicine startup she co-founded. Ponnapalli was named to the 2021 Business Insider list of 100 people transforming business, recognizing her work in leading businesses into the machine learning landscape.

For her Ph.D. in electrical and computer engineering at the University of Texas at Austin, Ponnapalli defined and demonstrated the higher-order generalized singular value decomposition (HO GSVD), a multi-tensor decomposition, and the only framework that can create a single coherent model from multiple two-dimensional datasets by extending the GSVD from two to more than two matrices.

Round-table discussions

• Who writes prompts? Who creates training data? Who does model-fine-tuning? Setting a durable partnership structure between AI and partner teams in the world of GenAI. Presenters: Conrad De Peuter
• Whatamix: Blending up feeds with composable recsys DAGs. Presenters: Jared Casale and Grace Li
• Integrating Artificial Intelligence with Product Innovation: Enhancing User Engagement and Transactions at PicPay. Presenters: Yan Werneck
• Transforming iFood’s Content Classification: AI-Powered Efficiency and Scalability with Large Language Models. Presenters: Anna Castro and Murilo Menezes
• Delivering successful enterprise GenAI solutions. Presenters: Eirini Spyropoulou

Organizers