Join us as we explore what it takes to

Bring machine learning innovations to life

Virtual Event | Available on-demand

Companies today are utilizing machine learning to deliver outsized business outcomes. However, developing ML is incredibly challenging. It’s a slow, iterative process; one that is complex, with many moving parts, diverse stakeholders, and potential pitfalls. The field is nascent, with tools and techniques changing quickly. Sharing insights from successful machine learning projects across industries advances ML and accelerates its potential to solve challenging problems.

Join us at CONVERGENCE 2022 to learn from leaders driving innovation with machine learning, who have faced development challenges and successfully delivered business value with machine learning. In this virtual event, you will discover emerging tools, approaches, and workflows that can help you effectively manage an ML project from start to finish. Choose from business and technical tracks with presentations from experts in data science and machine learning, who will share their best practices and insights on developing and implementing enterprise ML strategies.


Resham Sarkar

Data Products & Analytics at Slice

Oren Etzioni

CEO at Allen Institute for Artificial Intelligence (AI2)

Shivika Bisen

Lead Data Scientist at PAXAFE

Uri Goren

Head of Recommendation at Argmax

Emily Curtin

Senior Machine Learning Engineer at Mailchimp

Eduardo Bonet

Staff Full Stack Engineer – MLOps  at GitLab

Gary Marcus

CEO and Founder at Robust.AI 

Abubakar Abid

Machine Learning Team Lead at Hugging Face

Rochelle March

Head of ESG Product at Dun & Bradstreet

Kevin Stumpf

Co-Founder and CTO at Tecton

Peter Gao

CEO at Aquarium Learning

Gideon Mendels

Co-Founder and CEO at Comet

Niko Laskaris

Head of Strategic Projects at Comet

Vidhi Chugh

Staff Data Scientist at Walmart Global Tech


1:00pm ET

ML Highlights from 2021 and lessons for 2022

Oren Etzioni, CEO at Allen Institute for Artificial Intelligence (AI2)

2021 was a year full of advances in machine learning, natural language processing, and computer vision. Inspired by Sebastian Ruder’s blog post, ML and NLP Research Highlights of 2021, this talk will summarize 15 highlights and suggest lessons for 2022 and beyond

Technical Track

Business Track

1:30pm ET

Testing ML models for production

Shivika K Bisen, Lead Data Scientist at PAXAFE

Machine learning models are an integral part of our lives and are now becoming indispensable for decision-making process in many businesses. When ML algorithms make a mistake, it can not only adversely affect the user trust but can also cause loss of businesses and in some sectors – loss of life (health). How do you know that the model you’ve been developing is reliable enough to be deployed in the real world? In this talk, we are going to have a closer look at the Testing ML model for production. Main components of the talk will be :- a) Unit testing b) API Integration testing c) Simulation testing for ML model

Recommendation systems: From A/B testing to deep learning

Uri Goren, Head of Recommendation at Argmax

Recommendation systems got a lot of focus in recent times due to the increase in online shopping. Recommendation always goes hand in hand with measurement and experimentation. In this talk we would cover contextual-bandits, a technique that combines both aspects and bakes machine/deep learning into the process. Contextual bandits are increasingly adopted in the industry, and is being used by recommendation giants such as Netflix, Facebook, Expedia, and many more.

2:00pm ET

Data Quality Assessment using TensorFlow Data Validation

Vidhi Chugh, Staff Data Scientist at Walmart Global Tech

The research shows that the majority of the ML projects do not make it to production. In this talk inspired by data-centric AI, we will discuss the typical production woes and how maintaining good quality data plays a crucial role in developing a successful machine learning model. The audience will get to learn various sources and types of deviations and errors that can degrade data quality. The talk emphasizes fostering data culture and will conclude with a demo using TensorFlow data validation.

Stop Making Data Scientists Do Systems

Emily Curtin, Senior Machine Learning Engineer at Mailchimp

Data Scientists aren’t Systems Engineers, so why do our tools expect them to understand arcane k8s errors? Why do our people systems effectively model them as weird web developers? Many organizations are lacking in a practical understanding of the Data Scientist persona from a UX perspective. By defining what Data Scientists are good at, and more importantly what they’re not good at, we as MLOps professionals and organizational leaders can build on that understanding and let Data Scientists do their best work.

3 Key Takeaways

  • The best tools for Data Scientists are low/no-systems, not low/no-code.
  • Velocity comes from good tooling; quality comes from good incentives.
  • Infrastructure abstraction should be a top priority for MLOps professionals.

2:30pm ET

It's The Data, Stupid! How Improving ML Datasets Is The Best Way To Improve Model Performance

Peter Gao, CEO at Aquarium

When working to improve an ML model, many teams will immediately turn to fancy models or hyperparameter tuning to eke out small performance gains. However, the majority of model improvement can come from holding the model code fixed and properly curating the data it’s trained on! In this talk, Peter discusses why data curation is a key part of model iteration, some common data and model problems, then discusses how to build workflows + team structures to efficiently identify and fix these problems in order to improve your model performance.

Informed Guesser, Minimum Viable Model, Heuristic First: Using ML to solve the Right Problems

Eduardo Bonet, Staff Full Stack Engineer – MLOps at Gitlab

As Machine Learning passes its hype, the industry now enters a more mature scene where ML is not perceived anymore as a magical wand, but as a risky, yet powerful, tool to solve a new set of problems, that requires heavy investments in people and infrastructure. In this product-focused talk, we will be looking at steps we can take to decrease the risk of Machine Learning solution dying on the prototype phase: what types of problems are best fit, ideas on how to handle stakeholder expectations, how to translate Business Metrics into Model Metrics, and how to be more confident if we are solving the right problems.

3:00pm ET

15 min break

3:15pm ET

Panel Discussion: How to put ML successfully into production

Shivika K Bisen, Lead Data Scientist at PAXAFE
Emily Curtin, Senior Machine Learning Engineer at Mailchimp
Eduardo Bonet, Staff Full Stack Engineer – MLOps at Gitlab
Niko Laskaris, Head of Strategic Projects at Comet

Technical Track

Business Track

4:00pm ET

How Feature Stores Enable Operational ML

Kevin Stumpf, Co-Founder and CTO at Tecton

Getting Machine Learning applications into production is hard. When those applications are core to the business and need to run in real-time, the challenge becomes even harder. Feature Stores are designed to solve the data engineering challenges of production ML applications, tackling four key problems:

1. Real-time and streaming data are difficult to incorporate into ML models
2. ML teams are stuck building complex data pipelines
3. Feature engineering is duplicated across the organization
4. Data issues break models in production

Talk by Resham Sarkar

Resham Sarkar, Data Products & Analytics at Slice

Abstract coming soon.

4:30pm ET

Building Interactive Machine Learning Demos Fast

Abubakar Abid, Machine Learning Team Lead at Hugging Face

Building machine learning demos is important so that non-technical collaborators and endpoint users (e.g. customers, business teams, quality testers) can provide feedback on model development. However, it can be a time consuming process as it involves front end engineering, design experience, and model deployment. In this presentation, we will talk about an open-source Python package, Gradio, which allows machine learning engineers to quickly generate a visual interface for their ML models entirely in Python. Gradio makes accessing any ML model as easy as opening a URL in your browser. We will provide a technical overview of Gradio and discuss real world use cases in which Gradio has been used to accelerate machine learning workflows.

Building a global data supply chain to support an ML-built sustainability dataset on companies and their value chains

Rochelle March, Head of ESG Product at Dun & Bradstreet

Global changes have highlighted the interconnections and dependencies on ecosystems, natural resources, business relationships and economies. Traditional financial data fails to fully capture this systemic and quickly changing landscape that is our modern world. ESG (environmental, social, governance) data can serve as valued information for informing which companies are adapting to current challenges and which are lagging behind. For Dun & Bradstreet, this means extending its efforts around business transparency to generate ESG intel on millions of public and private companies and their supply chains that can bring better awareness on business partnerships and investments. This presentation will showcase analysis that explores the relationship between ESG metrics and company resiliency during volatile events, and will provide a deep dive into how the Dun & Bradstreet teams rapidly built a global data supply chain and ML-enabled tools to support constant updating, maintenance and ingestion of structured and unstructured ESG-related sources to support the world’s largest repository of ESG company data.

5:00pm ET

Wrap up

Gideon Mendels, Co-Founder and CEO at Comet

Participating companies

And many more!


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March 2, 2022