- Machine Learning projects can be managed using Agile methods.
- In AI development, several standard software development practices are continuing to evolve.
- AI Systems face challenges in reproducibility, a critical component of software systems.
- ML systems have dependencies not just in code but potentially in data as well, make sure you track them carefully.
- A work in progress, ML system development has surfaced some new requirements, but still requires best practices and tools.
ML (machine learning) has become a formidable technology that has created new opportunities and solutions in numerous industries. In addition to making predictions about insurance and finance, these systems can also predict medicine and personal assistants.
The building of these systems has relied on many established practices of Software Engineering, but many teams feel the need to increase their knowledge to support the new applications. We’ll discuss some fundamental practices for Agile software development, and the challenges posed by Machine Learning applications in this article.
Twelve Principles in the Machine Learning Context
- It is our top priority to satisfy the customer through early and continuous delivery of valuable software.
- Accept changes in requirements, even during development. The agile process harnesses change for the customer’s competitive advantage.
- Deliver working software frequently, from a couple of weeks to a couple of months, with a preference for shorter timelines.
- Business people and developers must collaborate daily for the project’s success.
- Build projects around motivated people. Ensure they have the appropriate environment and support, and then trust them to get the job done.
- Face-to-face communication is the most efficient and effective method of conveying information to and within a development team.
- Progress is primarily measured by working software.
- Sustainable development can be achieved through agile processes. Users, sponsors, and developers should all be able to maintain a constant pace for as long as possible.
- Continuous attention to technical excellence and good design enhances agility.
- Simplicity is the art of minimizing the amount of work to be done.
- Self-organizing teams produce the best architectures, requirements, and designs.
- The team evaluates how it can become more effective at regular intervals, before tuning and adjusting its behavior.
Machine learning projects: How to use agile methodologies
An inability to develop a highly accurate model in-house, high uncertainty, and the search for accurate models. The three main challenges of implementing a machine learning project are the following.
In agile project management, there is a framework for dealing with highly uncertain projects, like machine learning.
Scrum provides the light at the end of the tunnel for agile development. A medium to which machine learning will be applied. Then how do we do it? So far, the following has been figured out:
1. Start with small to zero investment
Licensing costs for AI solutions aren’t cheap. In the cloud, for instance, a natural language processing solution for the enterprise can cost $200,000. Rather, it makes sense to test the waters with a small or no investment Proof of Value (PoV).
2. Shift to agile driven contracting
Once you have finished your PoV and shown that your selected AI will meet the needs of your business, it’s time to talk to procurement. Traditional time and materials will not suffice, nor will fixed-price contracts.
An agile project management process focuses on delivering value rather than just solving a problem. The idea is to pay only when value is delivered.
3. Design thinking for requirements gathering
Stanford Design School, now known as Hasso Plattner Institute of Design, established Design Thinking by focusing on three steps: Understand, Improve, and Apply. A core value of agile project management is the value of empathy that is emphasized to do good project design. It is best to begin by understanding the current customer pain-point or challenge before diving right into the features of a machine learning solution. By doing so, we can determine what features to build and what the user experience should be.
4. Scrum for project implementation
Scrum is at the heart of our implementation. It is more effective at dealing with big unknowns, risks, and changes that are typical of machine learning projects, in contrast to traditional project management models.
There are only four major events in this streamlined process: Sprint Planning, Sprint Development, Sprint Reviews, and Sprint Retrospectives. This method cuts through bureaucracy quickly and gets our business users something they can use right away. Each sprint lasts two weeks, with a build ready to use at the end of the 10th day.
5. Build the machine learning model with business
Agile and machine learning is a match made in heaven. Scrum emphasizes the importance of business involvement in project success. Rather than heavily rely on the technical team, we asked the operations experts to build the machine learning model with the geeks. In the design thinking sessions, we define a vision that we translate into data and machine science problems that the model can reflect.