How to Speed Up Machine Learning in Enterprise Applications

Machine learning (ML) is a key part of operating an intelligent organization today. Often used interchangeably with artificial intelligence (AI), ML is actually better understood as an advanced method for data analysis that relies heavily on statistics and other methods to make data-informed predictions.

As such, machine learning in enterprise software applications delivers the greatest benefit when used to predict outcomes (such as component failure or likelihood a deal will close) or next best actions (e.g., in customer support or marketing automation). Finding ways to speed up machine learning helps companies shorten ML’s time to value and increase its revenue-earning ROI.

In this article, we’ll explore 6 actionable ways to accelerate machine learning in enterprise software applications and maximize its impact on business success.

Quick Takeaways

There is no single perfect solution — taking a goal-first approach to ML enables greater flexibility and faster time to value.
Software development teams should be included in discussions around machine learning in enterprise applications.
Outsourcing (and then partnering outside experts with your internal developers) is often more cost-effective and strategic than hiring an in-house ML team.
Open-source prototyping frameworks like TensorFlow allow for early and free testing of ML models before any code is written.

5 Ways to Speed Up Machine Learning in Enterprise Applications

Focus on the goal (not a specific solution)

As anyone well-versed in software development will tell you: There’s never one single solution to a problem. That’s why it's important to focus first on defining your goal for ML, and only then explore appropriate ML models.

Once you've defined your goal, rather than wasting time seeking out the perfect model or algorithm, work with your development team to find one that sufficiently balances effectiveness and ease of implementation. You will actually speed up machine learning in enterprise applications more quickly if you start with something that's "good enough" then work to continuously improve it based on feedback and results.

This idea — that you should focus on delivering early, though imperfect results, then iterate on it — is a central tenet of the reigning continuous improvement models today from agile to devops. Why is this approach so popular? It works!

Outsource to experts

Companies often hesitate to outsource software development because of the associated costs (leaving aside concerns about IP). While it's true that hiring expert support can be expensive, the time savings alone can justify the investment.

Machine learning projects that may take internal teams months (or even longer) to execute can be completed in weeks with the help of an ML expert. Frankly, unless you have the talent in house, hiring a consultant may be the only way for you to push through bottlenecks and other delays slowing down your machine learning implementation.

Outsourcing ML development also provides you with a level of flexibility that maintaining an expert internal team does not, particularly when it turns out you need someone with specific expertise to complete a particular project. By partnering such experts with your internal software development team, you can often achieve the best of both worlds (expertise AND institutional knowledge).

Involve your software development team

That being said, your software development team may not be totally in the dark with regard to machine learning. Indeed, your developers may have valuable input related to which machine learning approaches are most feasible and likely to be effective with your applications. For this reason, it can be a good idea to get them involved in your acceleration efforts sooner rather than later (for example, when you've finally decided what you want to implement).

To put it simply, make your development team a key part of discussions around machine learning development and opportunities at your organization.

Use existing prototyping frameworks

Early testing of ML avoids problems that may arise post-implementation, and testing is one part of the process that’s actually quite uncomplicated. With a quick Google search, you can find multiple open-source frameworks that allow you to test potential machine learning models before your development team starts writing code. Frameworks to try include Keras, Ludwig, TensorFlow, and OpenCV.

Automate retraining

Business doesn't stand still. Data changes and evolves. Machine learning models must evolve as well to keep up. ML retraining should be automated and planned well in advance.

Ideally, this planning will include your ML team (internal and external), software developers, engineers, DevOps teams, business stakeholders, and any other relevant parties who play a part in the retraining model’s development and refinement.

The Big Takeaway

By design, machine learning models are built with eye towards ongoing performance improvement (the "learning" part means they should get better over time). Still, that doesn’t mean ML should get the “set-it-and-forget-it” treatment. Active planning, continuous delivery, ongoing assessment, and collaboration across participating teams and individuals are all key to speeding up machine learning and maximizing its ROI.

How LeanIX Can Help

To understand machine learning’s potential for your own software applications, you first need to know what your tech stack includes. You also need to ensure a reliable flow of data. LeanIX solutions provide visualizations of your entire IT infrastructure, including application landscape reports that help you map data flows and uncover opportunities where ML can make the biggest difference.

Learn more about LeanIX solutions or schedule your demo today!