Software engineers today are always looking for ways to improve their development efforts. Many have adopted IDEs (Integrated Development Environments), RAD (Rapid Application Development) Tools, and have eschewed the ideology of the waterfall development process, instead choosing to leverage the benefits of agile development methods.
Those disciplines, which are readily demonstrated by today's savvy coders and developers, lend themselves to what many consider to be the next big thing in software design, machine learning. The benefits and ideologies demonstrated by the capabilities of machine learning are on the cusp of becoming disruptive to how users and systems interact. What's more, the delivery of data, intuitiveness of an interface, and the ability to correlate and mine data has big implications for those looking to leverage technologies such as big data, business analytics, and so forth.
Yet, there remains a certain truth behind those benefits, none of which will be possible unless coders get behind the design of machine learning, and bring their special set of skills to bear to create the next generation of intelligent applications.
Professional development practices have a significant impact on the success of building applications that leverage machine learning. Some of those disciplines include:
- Project Management: Successful applications designers have a strong understanding of taking a project driven approach to any new development effort. That understanding results in effectively structuring a project by laying out the core elements and keeping things organized, helping to spawn collaboration, and bring additional creativity into the design process.
- Simplification: Application designers often have the knack of breaking a project down into its simplest steps and building on small successes. Much of that simplification ideology lends itself well to the concept of automation, where common project tasks are automated, bringing speed and consistency to software design.
- Repeatable Processes: Using best practices as well as intelligently designed procedures allows senior developers to build a process that can be repeated time and time again, making it simpler for additional coders to become involved and even extend ease to deployment.
- Validation: Testing of developed code is a "must do" step, and drives the "Testability" of any software project. For the most part, testing can be automated and relies heavily on the simplification (or modularization) of classes, functions, or other elements.
- Maintainability: The skills/processes outlined above all conspire to drive one of the most important elements of any development project: the ability to maintain the application. Simply put, the development process is only a small part of the overall life of an application and that application often goes through maintenance iterations to introduce improvements and added capabilities. Only by combining structured design, automated tasks, repeatable, and testable processes can a developer hope to build applications that have long-lasting potential.
The disciplines outlined above lend themselves quite well to the field of machine learning, and directly correlate to most of the phases in a machine learning project. For example, when data is being mined from an originating source and undergoing transformation into something digestible by a machine learning system, automation and repeatability become core competencies for success.
Another example comes in the form of appropriate algorithm usage, where machine learning methods are being tested to determine what method is most suitable for the problem at hand. That testing can be automated using many of the same skill sets that developers use for software testing processes, speeding the realization of results, and better identifying which machine learning algorithm is best for the task at hand.
Also, once a method is chosen and put to use to solve complex problems, maintainability becomes a key component for success. An easily maintained process can be tailored to meet changing project needs and derive new answers from existing data sets. When combined with the other elements of proper design and execution, a machine learning project becomes a viable solution for a multitude of analysis problems, without introducing undue complexity.
Those previously discussed examples outline why application developers and coders should be involved with machine learning solutions design. Currently, much of algorithm design and associated coding for machine learning solutions rests with researchers and mathematicians, whose core competencies are not normally associated with the development of code. That often results in code that is hard to test, modify, document and implement; this creates a situation where a machine learning project could be destined for failure.
Obviously, the goal of any machine learning project is implementation, which requires a working prototype of the machine learning tool. This is a task best left to those educated in the nuances of the application development.
What's more, programmers can leverage machine learning to improve their own applications. Many programmers may be put off by perceived complexity and preconceived notions about machine learning code. However, the simple fact of the matter is that machine learning methods are built as a way to address a specific decision problem. Basically, incorporating machine learning means takes the approach of identifying a complex problem in an application, and determining whether that problem can be better solved by machine learning.