5 Ways Agile is Going to Rule Machine Learning Projects in the Near Future

As a framework and a methodology, Agile is one of the most popular ways of developing core systems. With interactivity and engagement woven into the development process, Agile is proven to provide greater efficiencies across parameters. This increases ROI for development teams that are focusing on innovative solutions to complex challenges.

Machine learning projects are increasingly being developed via Agile methodologies. From voice assistants to real-time predictions, Agile is being used to constantly upgrade machine learning solutions. Projects also tend to focus heavily on regular testing and feature development, giving rise to a core need for Agile as a team-wide methodology.

Effective Project Management Through Agile

Agile generally involves multiple layers of stakeholder inputs, with cyclical testing and rapid prototyping. This requires a hands-on approach on behalf of the project manager, with the teams having to become a core component of constant communication. Agile aids in enhancing the level of communication within the project, creating greater bonds between team members. This leads to a more efficient management structure, allowing insights to flow freely.

Ideas, features, and feedback can be introduced into the loop at any time, making the process dynamic and innovation-oriented. This leads to the idea that markets are increasingly digitized and keeping up with the latest features is key. Agile allows machine learning projects to be market-focused and to achieve project objectives in a timely manner.

Projects are also made increasingly transparent, with all levels of development having access to each domain. This leads to a more holistic approach to project development, with all employees having access to critical information. Transparency also allows managers to become more efficient, allowing them to keep a track of core development.

Accelerating Decision-Making in Design

A global survey of over 1,300 IT decision makers found that Agile played an important role in expediting the decision-making process. The methodology was shown to have improved interpersonal communication, data insights, and information processing. The insights showed that companies that adopted Agile saw a 60% increase in revenue generation as well. The overall benefits are incremental, leading to greater adoption across industries.

Digital transformation is being fuelled by accelerated decision making across technologies. When it comes to machine learning, Agile is allowing companies to meet consumer demand at a rapid pace. It’s creating better technology solutions that can be scaled effortlessly. When Agile is truly a part of engineering, design and testing domains, it can radically transform an organization.

“Today, efficient and agile operations are critical to supporting the pace at which a business must move in the digital economy and yet businesses are challenged by manual processes, legacy systems, and insufficient data.” — Sibjyoti Basu, Partner and Alliance Leader at EY Consulting.

Companies are looking for ways to enhance their product portfolios and challenge the status quo in computing. Accelerating the decision-making process is just one of the many ways that Agile is going to rule machine learning in the coming years.

Optimizing Core Resources (Talent, Teams, Technology)

Agile allows companies to optimize their valuable assets in the form of talent and technologies. The teams are also assigned as per the desired outcome, with iterative development being at the center of the project. Teams can then interact with one another to find the best solution for that unique challenge. This optimizes the time and effort of each resource, giving greater competitive advantages to companies.

When engineering is in direct contact with R&D, there is synergy across the board. Agile ensures that all efforts dedicated to machine learning projects are optimized from the beginning. There is no ambiguity in the process and each developer understands their role in the process.

Whether that be facial recognition or chatbot development, Agile creates a dynamic environment for all resources to participate in. This makes the machine learning project timebound, leading to greater efficiencies through optimizing resource allocation. Teams can also be shifted to new functions or projects after their role is completed for a task.

The project cycle is kept on track, with all members having the right resources with which to work. Execution is of the essence, making resource optimization one of the main areas where Agile is going to be a core component of all ML projects.

Rapid Validation of Data Models

Agile is particularly powerful in the rapid validation of hypotheses, especially in the healthcare domain. It allows developers to test different models and data scientists to have more accurate information. When dealing with large data sets, it’s best to have a methodology that provides flexibility and scale. That’s where Agile comes in.

Agile allows data teams to validate their models at a much faster rate. Teams can then iterate on various models and data sets to deliver better results. They can also design new models based on clearer data points, and rapid-test them until success. Agile allows teams to have a singular focus, while constantly providing key insights that can enhance the overall project.

This blends into the artificial intelligence domain, with accelerated validation. AI models can also be developed with the help of machine learning and Agile methodology, to create greater business impact. All areas within high-tech domains, such as finance, healthcare, and manufacturing, can leverage Agile to create real-time rapid validation.

Increasing Adoption of Machine Learning

With the help of Agile, more companies will be interested in working with machine learning teams and development environments. This increases the adoption of machine learning as a technology, along with providing a more holistic approach to project development. Companies can innovate using machine learning while working within the Agile paradigm.

When compared to traditional models, machine learning may become increasingly complex thereby enhancing cognitive load. Companies may lean away from using machine learning because they may not be comfortable working with technology. However, in the case of Agile, companies can unlock the benefits of machine learning without feeling overwhelmed or challenged.

Agile takes in insights and information from all domains to create more transparent solutions. This increases the adoption of machine learning, giving rise to greater demand across industries.