Collaboration on complex development projects almost always presents challenges. For traditional software projects, these challenges are well known, and over the years a number of approaches to addressing them have evolved. But as machine learning (ML) becomes an essential component of more and more systems, it poses a new set of challenges to development teams. Chief among these challenges is getting data scientists (who employ an experimental approach to system model development) and software developers (who rely on the discipline imposed by software engineering principles) to work harmoniously. In this SEI blog post, which is adapted from a recently published paper to which I contributed, I highlight the findings of a study on which I teamed up with colleagues Nadia Nahar (who led this work as part of her PhD studies at Carnegie Mellon University and Christian Kästner (also from Carnegie Mellon University) and Shurui Zhou (of the University of Toronto).The study sought to identify collaboration challenges common to the development of ML-enabled systems.

By combining physics-based simulations, data mining, statistical modelling and machine learning techniques, predictive engineering analytics can analyse patterns in the data to construct models of how the systems you gathered the data from work. IoT and sensors are already transforming products and mining the stream of information from products will be critical for maintaining products and designing their replacements. For many industries, the products they create are no longer purely mechanical; they're complex devices combining mechanical and electrical controls. That means engineering different systems, and the ways they interface with each other, and with the outside world. At one level you're coping with electromechanical controls, at another, you're creating a design that covers the cooling requirements for the electronics.

Uber Eats has been one of the fastest-growing food delivery services since the initial launch in Toronto in December 2015. Currently, it's available in over 600 cities worldwide, serving more than 220,000 restaurant partners and has reached 8 billion gross bookings in 2018. The ability to accurately predict delivery times is paramount to customer satisfaction and retention. Additionally, time predictions are important on the supply side as we calculate the time to dispatch delivery partners. My recent talk covered how Uber Eats has leveraged machine learning to address these challenges. With the mission "Make eating well effortless, every day, for everyone" one of our top priorities is ensuring reliability.

The business challenge was, therefore, to optimize the utilization of MTR's limited resources--people, tools, workspace and time (four non-traffic hours every day)--and yet be able to comply with the statutory and safety regulations. In 2005, MTR embarked on a project called the Engineering Works & Traffic Information Management System (ETMS) which uses artificial intelligence (AI) for planning, scheduling and managing engineering works. The business challenge was, therefore, to optimize the utilization of MTR's limited resources--people, tools, workspace and time (four non-traffic hours every day)--and yet be able to comply with the statutory and safety regulations. In 2005, MTR embarked on a project called the Engineering Works & Traffic Information Management System (ETMS) which uses artificial intelligence (AI) for planning, scheduling and managing engineering works. The ETMS helps MTR to efficiently plan and execute preventive and corrective engineering works during the limited time available in the non-traffic hours.

JUST after midnight, the last subway car slips into its sidings in Hong Kong and an army of engineers goes to work. In a typical week, 10,000 people carry out 2600 engineering works across the system – from grinding rough rails smooth and replacing tracks to checking for damage. People might do the work, but they don't choose what needs doing. Instead, each task is scheduled and managed by artificial intelligence. Hong Kong has one of the world's best subway systems.

The move comes as Qi Lu, a longtime Microsoft engineering leader who was most recently executive vice president of the Applications and Services Group that includes Bing and Office, leaves the company to focus on his recovery from a bicycle accident. Lu, a former Yahoo engineer with dozens of patents to his name, is widely respected within the technology industry, but a rare public speaker outside of it. Satya Nadella, Microsoft's chief executive, worked for Lu when Nadella led engineering work on the Bing search engine. "Qi exemplifies what it means to have a deep sense of mission, purpose and authenticity in everything one does," Nadella said in an email to employees on Thursday. "His greatest impact is the people he has inspired. I count myself among them."

Within the rail industry, anything which helps keep trains moving, avoiding operational delays and improves customer experience, is worth pursuing. Many OEMs are now investing significant resources into one of the most valuable and potentially rewarding currencies in business: Big Data. In rail, and specifically when it comes to rolling stock maintenance, big data is synonymous with Condition Based Maintenance (CBM) and Predictive Maintenance (PM). Thanks to the rapidly expanding scale of manufacturing and asset maintenance industries, they are now adapting to the wider applications of advanced algorithms on consumer generated big data. Though CBM and PM are commonly adopted practices in rail industry, the scope of CBM is far wider than that of PM.