Successful machine learning and artificial intelligence projects require large volumes of data that is properly labelled. The challenge is that most data is not clean and well annotated, requiring a scalable data labeling process. Ideally this process can be done using the tools and systems that already power your analytics, rather than sending data into a black box. In this episode Mark Sears, CEO of CloudFactory, explains how he and his team built a platform that provides valuable service to businesses and meaningful work to developing nations. He shares the lessons learned in the early years of growing the business, the strategies that have allowed them to scale and train their workforce, and the benefits of working within their customer’s existing platforms. He also shares some valuable insights into the current state of the art for machine learning in the real world.
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- Integrating data across the enterprise has been around for decades – so have the techniques to do it. But, a new way of integrating data and improving streams has evolved. By integrating each silo independently – data is able to integrate without any direct relation. At CluedIn they call it “eventual connectivity”. If you want to learn more on how to deliver fast access to your data across the enterprise leveraging this new method, and the technologies that make it possible, get a demo or presentation of the CluedIn Data Hub by visiting dataengineeringpodcast.com/cluedin. And don’t forget to thank them for supporting the show!
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- Your host is Tobias Macey and today I’m interviewing Mark Sears about Cloud Factory, masters of the art and science of labeling data for Machine Learning and more
- How did you get involved in the area of data management?
- Can you start by explaining what CloudFactory is and the story behind it?
- What are some of the common requirements for feature extraction and data labelling that your customers contact you for?
- What integration points do you provide to your customers and what is your strategy for ensuring broad compatibility with their existing tools and workflows?
- Can you describe the workflow for a sample request from a customer, how that fans out to your cloud workers, and the interface or platform that they are working with to deliver the labelled data?
- What protocols do you have in place to ensure data quality and identify potential sources of bias?
- What role do humans play in the lifecycle for AI and ML projects?
- I understand that you provide skills development and community building for your cloud workers. Can you talk through your relationship with those employees and how that relates to your business goals?
- How do you manage and plan for elasticity in customer needs given the workforce requirements that you are dealing with?
- Can you share some stories of cloud workers who have benefited from their experience working with your company?
- What are some of the assumptions that you made early in the founding of your business which have been challenged or updated in the process of building and scaling CloudFactory?
- What have been some of the most interesting/unexpected ways that you have seen customers using your platform?
- What lessons have you learned in the process of building and growing CloudFactory that were most interesting/unexpected/useful?
- What are your thoughts on the future of work as AI and other digital technologies continue to disrupt existing industries and jobs?
- How does that tie into your plans for CloudFactory in the medium to long term?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Reading, UK
- Ruby on Rails
- Natural Language Processing (NLP)
- Computer Vision