It is difficult to understand the rapid expansion of some AI providers’ teams. When all the marketing around AI is around efficiency gains, surely, it is not just about the ML but also about augmenting the end-to-end process.
Today machine learning (ML) is enhancing many of the things we do—from helping us to determine a good deal on a car to aiding more behind-the-scenes processes, such as payment fraud detection within banks. However, while machine learning is being used extensively, it is not being used efficiently or, indeed, to its full potential.
In many automation projects, the machine learning component usually only constitutes a small portion of the overall process, which is then completed manually by rapidly expanding support teams. There is no doubt that machine learning provides additional value to many of its enterprise user businesses, but many organisations do not yet realise that by continuing in this manner, profits will eventually plateau or even run at a loss as the cost of running the system outweighs the gains.
There is an enormous amount of effort that goes into managing machine learning systems, often by very large teams working hard. In some cases, teams work round the clock to ensure a system is up to date by attempting to maximise a system’s effectiveness using the latest data trend changes. This work is often repetitive and frequently requires input from several teams, before a process is complete and can be used in a live environment. This slows the process down, leading to potential loss of profits through both lost sales and operational losses.
Autopilot ML: The Next Stage of Man-Machine Synergy
Enter Autopilot ML—automatic end-to-end machine learning process optimisation piloted by machine learning. As the name suggests, an autopilot flies the plane without human intervention. There is no reason the machine cannot remove this manual human effort and produce even better results from the machine learning components which drive core parts of a business. The human oversees what is happening and can step in to add some SME knowledge to improve performance, or if there are problems, but for the most part, the machine is left to continue its work.
As the technology used in machine learning model management becomes more and more refined, just as in the airline industry, more and more control can be released to the autopilot. It is one of the many tools that pilots use to do their job and improve their output, safety margin and productivity.
Introducing Autopilot ML to replace some of the manual processes involved in ML today can drastically reduce the amount of repetitive, expensive work that teams involved with ML management need to do, allowing them to focus on more valuable activities. The automated approach also ensures the best continual performance as the machine is always looking for improvements and making changes to ensure this.
Implementing Machine Learning Into a Business
Apart from the process improvements of an Autopilot ML approach, greater emphasis can be placed on recording clear reasons for any decisions taken by the machine. This will help make the future decision compliance with regulators much clearer. This window into the system’s internal workings also gives managers greater confidence in a machine’s ability to manage processes and make the best decisions.
You may ask, what has stopped from this being done until now? Well, a data scientist’sfocus has traditionally been around extracting insight from the available data for a wide variety of tasks, such as enhancing product offerings, market research, automating difficult tasks and many others. In some cases, data scientists are required to create bespoke models for the problem but with the most up-to-date data. As you can imagine, this is quite inefficient. With this automated with Autopilot ML, the data scientists can be freed up to work on other high priority business items.
Implementing machine learning into a business has been a priority for many organisations, usually with the process of managing new capability thrown to the sidelines, with little concern given to the manual effort that this introduces. But now that machine learning systems are proving their value, the focus is now due to shift to offloading this manual effort with machine managing the machine.
Future of Machine Learning
In the future, it’s clear that certain elements of the wider machine learning lifecycle will be increasingly supported by machines. Continuing with manual processes is not sustainable for the machine learning management process solutions of the future. This is why the next milestone in AI has to be to ensure that products and processes are optimised, or in other words, autopilot ML.
Autopilot ML is quickly becoming a necessity as the number of team members to manage the new capability grows and profits slow down. Smaller, more applied teams will be required in future to work alongside machines to create a system which is highly agile, performant, and perhaps most important of all, cost effective. Contrast this to today’s sprawling, inefficient operation and it’s clear that this is the way ML model management is heading.
Process automation is continuing to innovate and provide increased efficiency and profit gains in the places it’s implemented. The automation revolution isn’t coming, it’s here—so prepare your business for streamlining more effective, engaged staff and increased profit.