The intended audience for this post is both technical and non-technical stakeholders with the purpose of determining and explaining ML Technical Debt. Understanding of ML Technical Debt prevents various stakeholders, e.g., Project Manager, ML Engineers, Data scientists, Customers, and Investors from being blindsided by the excitement of a current/proof of concept ML product to only find out later that the ML product predictions are useless after a few months. Worse still, the expected final ML product never seems to come to a point of deployment value and the ML technical debt cannot be paid off due to financial and time constraints. Machine Learning (ML) Technical debt is the debt incurred by the deployment of an ML system without developing the code, infrastructure, tools and processes necessary for efficient iteration coupled with a lack of understanding and foresight of the actual ML product requirements. Provision of continuous and actual value for deployed ML systems is hindered by accumulated ML technical debt as the deployed ML system is unable to react to the changes and fails to achieve the necessary performance consistently.

Putting machine learning into production in the enterprise is not easy: Many organizations are struggling to implement the technology at scale. But it is possible to make the process of building, scaling, and deploying enterprise machine learning solutions repeatable and predictable. Join Tom Davenport, President's Distinguished Professor of IT and Management, Babson College; Alex Breshears, senior product manager, Production Machine Learning, Cloudera; and Abbie Lundberg, business technology analyst, Lundberg Media for a discussion of the specific challenges enterprises face in machine learning and how they can create an end-to-end, factory-like capability. The content was created by the speakers of this event. The MIT Sloan Management Review editorial staff was not involved in the selection, development, or broadcast of this event.

Machine learning promises to address many of the challenges faced by network security analysts; however, there are still many obstacles that prevent widespread adoption of machine learning within security operations centers (SOC). The first major challenge is one of trust as discussed in our previous post. The second major set of challenges is around the complexity of deploying machine learning in a production environment. Once a machine-learning model has been trained and validated in the lab, there is often an equal if not larger effort required to deploy that model in a repeatable, production environment.\Transitioning Data science typically operates using an iterative batch process.

Jan Machacek is a passionate technologist who shares his expertise and passion for software as the editor of the Open Source Journal, regularly contributes to open source projects and speaks at conferences in the UK and abroad. Jan is the author of many open source projects (various Typesafe Activators, Reactive Monitor, Akka Patterns, Akka Extras, Scalad, Specs2 Spring, etc.), books and articles. It's all about Containers, Serverless and Reactive Programming right now! ProgSCon London will explore these trends through engaging talks delivered by leading industry experts. Several talks will also feature various aspect of Blockchain, Microservices and Big Data. If you are a software developer looking to sharpen your skills and learn from the best in the industry, then ProgSCon London 2017 is the place you need to be at!