Machine learning is a powerful, innovative technology that makes it possible to obtain new solutions in various areas of our lives. According to Statista’s website, machine learning software is used to apply artificial intelligence (AI), which allows systems to automatically or “artificially” learn and improve features based on experience without being specifically programmed to do so.
Machine learning makes it possible to get predictions in various areas, such as insurance, finance, medicine, personal assistants, and self-driving cars. The creation of these systems was based on many established software development methods, but many teams find it necessary to extend these methods to support these new applications. This article will look at a few of the leading agile software development practices and challenges faced by machine learning applications.
What is machine learning?
Machine learning is a promising technology that offers new opportunities and solutions in many sectors.
Conventional software development methods have been used to create these systems, but new software development methods are required to implement them. Therefore, the Agile project management approach is designed to deal with projects with a significant level of uncertainty.
The Importance of Agile Methods in Machine Learning
Forbes notes that ML projects are driven not by code, but by data, based on which training should be obtained. What is needed is a project management methodology that considers the various data-centric needs of AI and considers the application-centric use of models and other artifacts created during the AI life cycle.
The more companies that use Agile platforms from Easy Agile and others, the more machine learning teams and development environments will demand. Creating machine learning technology and offering a more holistic approach to project development is helping to increase its acceptance. Organizations can use machine learning with Agile methods.
Using Agile Methods for Machine Learning
Agile brings together ideas and data from many fields to offer more transparent solutions. It improves the adoption of machine learning, which leads to an increase in demand in all sectors. Here are some ways to use agile methods in your machine learning projects:
Effective project management
Agile is all about gathering feedback from stakeholders through iterative testing and rapid prototyping. When managing projects, all teams will be more active in fulfilling their responsibilities.
Optimization of valuable assets
Agile makes software development possible in a dynamic environment. A deadline was set for machine learning, resulting in a more efficient allocation of resources. Teams can move on to other roles or projects after completing the current task.
Fast decision-making
Agile dramatically speeds up the decision-making process in development. Data comprehension, interpersonal interaction, and information processing were found to improve with this approach.
The implementation of Agile resulted in a 60% increase in revenue. With little overall impact, additional benefits lead to broader industry adoption.
Agile methods give key performance indicators
Links to Agile working methods should be shown when measuring organizational results and evaluating performance. This propensity for flexible data management enables long-term commitments to be met.
Work plans are broken down into weekly or biweekly sprints. The project management office maintains strict control over the project deadlines. Members are responsible for well-defined work, and roles with specific results are directly linked.
Machine learning on rails Agile development
Modern methods, tools, and software development methods used by top software development companies usually rely on Agile development. Agile is generally about helping product teams develop valuable tools in predictable and agile ways. The methods used in Agile-like development will help machine learning projects in the long run.
Introducing something fresh and new is a surefire way to counter stagnation. Thus, the purpose of the Agile method is to propose a hybrid strategy that meets the organization’s expectations and creates the basis for the continuous iterative development of learning management projects with the least risk.
Author’s bio: Anastasiia Lastovetska is a technology writer at MLSDev, a software development company that builds web & mobile app solutions from scratch. She researches the area of technology to create great content about app development, UX/UI design, tech & business consulting.
