Navigating AI Challenges

In this AI leaders’ roundtable discussion, participants delved into various facets of AI applied research, encompassing topics such as scaling AI within organizations, deploying deep learning models, data-centric AI, the pros and cons of open source, and model plus data version control. 

The insights shared during this dialogue offer valuable perspectives on these key aspects of the ever-evolving AI landscape.

Scaling AI Applied Research

One of the core challenges in the realm of AI applied research is effectively dividing engineering and research tasks within organizations. The participants emphasized the importance of tailoring this division to the specific problems at hand. While some organizations choose to allocate unspecialized individuals to particular functions, others opt for growing teams based on functionality. The key takeaway here is the delicate balance between specialization and functionality, a crucial aspect of implementing AI solutions in practice.

Deploying Deep Learning Models

The discussion on deploying deep learning models sheds light on the approaches and tools employed by the participants. The conversation touched upon the deployment of models on AWS EC2 instances, often with an abstraction layer to streamline various tasks. However, the participants acknowledged that this process can be manual and time-consuming. They also discussed the pursuit of alternatives and highlighted the challenges associated with deploying models on GPUs due to the associated costs.

Data-Centric AI

The concept of data-centric AI took center stage as the participants underscored the paramount role of data in AI projects. They emphasized the value of harnessing user data to train models and enhance solutions. The iterative process of obtaining labeled data from user interactions and utilizing it to train models was a focal point of the discussion. While automation was recognized as an essential goal, the participants acknowledged the complex nature of achieving full automation in data-centric AI.

Open Source

The benefits and drawbacks of open-source technologies were discussed during the conversation. The consensus was that open source aligns well with companies targeting developers or those operating in developer-centric markets. It can serve as a tool to attract potential engineers and facilitate recruitment. However, the participants emphasized that open source may not be a one-size-fits-all solution, stressing the need to carefully consider the specific use case and product before embracing open-source solutions.

Model and Data Version Control

The participants shared their experiences with model and data version control, highlighting the use of tools like Data Version Control (DVC) to link data and models to GitHub repositories. DVC streamlines version control, providing the flexibility to easily revert to previous iterations. However, the discussion also touched upon the challenges of managing permissions and organizing data within S3 buckets.


In summary, the roundtable discussion emphasized the importance of finding the right equilibrium between specialization and functionality when scaling AI within organizations. It tackled the challenges of deploying deep learning models and the quest for cost-effective alternatives. The value of a data-centric approach was accentuated, underlining the significance of collecting and leveraging user data. The conversation also contemplated the suitability of open source for different companies and extolled the benefits of model and data version control using tools like DVC.

These insights offer a comprehensive overview of the multifaceted world of AI, providing a compass for navigating the complexities and seizing the opportunities that lie ahead.

Disclaimer: All guests’ views are their own and do not represent their employers’.