Name: ML System Design for Continuous Experimentation
Start: 2022-01-24T17:42:00Z
End: 2022-01-24T17:42:41.000Z
Location: BrightTALK
Rating: 0

Comet provides a self-hosted and cloud-based MLOps solution that enables data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams. Learn more at www.comet.com

As AI gains wider adoption across industries, technical teams are vetting and using a growing number of tools and resources for developing and deploying machine learning models. The nascent MLOps ecosystem is evolving rapidly, and many tools in this ecosystem tend to be disconnected, which can lead to the emergence of information silos and/or brittle workflows that can stifle growth and innovation.

In this session, join the teams at Superb AI, AI Infrastructure Alliance, and Comet as we cover:
-How a modular approach to the ML tech stack enables teams to choose the best tools for their use case, data, and business
-Why modular ML tech stacks provide more seamless integration between tools
-How to assess MLOps tools for modularity

Accelerating AI Value with Modular MLOps

GE Healthcare is using AI and ML for operational workflow improvements, case prioritization, medical imaging, dictation/NLP and diagnostics. These projects are delivering REAL impactful business contributions, including reducing MRI imaging time by up to 50% while improving image quality, 30-50% reduction in exam time and 70% reduction in no-show rates. 

Listen to this in-depth interview and learn:
-How large of an AI/ML team is needed for these impactful projects
-What level of industry/domain expertise is needed by AI practitioners
-What are key strategies to embed AI models into complex hardware
-The key to building an organizational AI structure within a large organization

Machine Learning in the Field: an interview with Vignesh Shetty of GE Healthcare

In this roundtable, enterprise ML leaders from Uber, The RealReal and WorkFusion will discuss the past year of their work in the field. You will learn:
-Key challenges faced by enterprise teams and how they are overcoming them
-Helpful tools and approaches for building and deploying ML at scale
-Surprising discoveries that have changed the way enterprise teams build AI


Olcay Cirit, research scientist and tech lead at Uber AI
Artsiom Strok, VP data science and ML at WorkFusion
Chris Brossman, The RealReal
Gideon Mendels, CEO, Comet

Roundtable: Developing Machine Learning at Enterprise Scale

As teams ramp up their AI/ML capabilities, the biggest challenge they face is deploying their models intro production. Especially for machine learning engineers and data scientists, building a robust stack is critical for creating scalable and repeatable MLOps practices and processes.

In this webinar, join the teams at Pachyderm and Comet as they cover:
-What MLOps entails, and the components of a robust stack
-The challenges teams face when scaling their models intro production
-The importance of monitoring both data and code for your models

Building Your Futureproof Stack for MLOps

As machine learning expands and larger organizations begin deploying across bigger teams, the need to efficiently operationalize becomes critical for enterprises.

In our discussions with leading organizations utilizing ML like The RealReal and Uber, we have compiled real-world case studies and organizational best practices for MLOps in the enterprise.

Join us for a discussion where we'll explore the benefits of MLOps and discuss when and how to deploy MLOps in your ML. We'll review three real world case studies that will answer key questions:
-When to start implementing in MLOps?
-How to start implementing in MLOps?
-How to measure the value of your MLOps strategy?

Lessons From the Field in Building Your MLOps Strategy

While academic research has been improving consistently, many organizations are struggling with translating ML into business value. Overcome the operational hurdles of ML teams. Watch now and learn how to:

-Create value for your business with AI,
-Introduce ML to your business and scope your initiatives appropriately, and
-Manage the ML lifecycle with visibility and ability to reproduce models

We also share case studies and examine the shortfalls of traditional and agile practices applied to ML teams and what you can to differently to get results.

Running Effective Machine Learning Teams

An independent survey has uncovered yet another hurdle to delivering value with AI. Challenges related to people, process, and tools are creating friction that makes it difficult to track model training runs and results, collaborate with colleagues, and iterate faster during the complex process of developing machine learning. This friction can cause delays in ML development that delay or halt model deployment to production.

But there’s good news: In this webinar, Comet CEO and co-founder Gideon Mendels will review the results of Comet’s 2021 ML Practitioner Survey and share insights for overcoming ML development challenges. You will learn about:

-Key challenges faced by ML practitioners
-Helpful tools and approaches for building and deploying ML at scale
-Three characteristics to consider when evaluating a machine learning platform

Overcoming Machine Learning Development Challenges

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."

Stop Making Data Scientists Do Systems

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.

Recommendation systems: From A/B testing to deep learning

Research shows that one of the top challenges faced by data scientists and machine learning (ML) engineers is reproducibility, or the ability to run an algorithm on particular datasets with similar results. If you are part of a mid-market or enterprise team looking to advance your use of ML, reproducibility can be a barrier to ensuring positive outcomes and scaling great work.

In this webinar, you will learn about:

The four aspects of reproducibility in machine learning
A five-point reproducibility checklist you can use to ensure reproducibility in ML experimentation across your organization
How a machine learning platform can assist with ML reproducibility

Reproducibility in ML Development: Optimizing Collaboration

While ML model development is a challenging process, the management of these models becomes even more complex once they're in production.

Shifting data distributions, upstream pipeline failures, and model predictions impacting the very dataset they're trained on can create thorny feedback loops between development and production.

In this webinar, we will
-Examine some native ML workflows that don't take the development-production feedback loop into account and explore why they break down.
-Showcase some system design principles that will help manage these feedback loops more effectively.
-Explore several industry case studies where teams have applied these principles to their production ML systems.

ML System Design for Continuous Experimentation

Artificial Intelligence

Data Distribution

Data Science

Machine Learning

ML Lifecycle

ML Workflow

MLOps

System Design

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ML System Design for Continuous Experimentation

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