Making Machine Learning Accessible for Test Engineers

For years, adopting machine learning (ML) in test and measurement environments felt out of reach—demanding specialized data science expertise, extensive training, and resources most test engineers simply didn’t have.

During NI Connect 2025, Alon Malki explored how this is changing in the Revolutionizing Validation and Manufacturing session. Instead of diving into ML’s complexity, he spotlighted NI solutions designed to give busy test engineers a practical ML advantage—no data science background required.

Accessible ML with NI SystemLink and NI OptimalPlus GO

To provide context, Malki walked the audience through the essential ML lifecycle—learning from data, acting on insights, validating results, and adapting to changes. He then introduced two NI solutions that are leading the way for test engineers who want to begin embracing machine learning.

NI SystemLink is an integrated, scalable solution that streamlines validation lab operations. It supports the full ML lifecycle through built-in features for data collection, continuous monitoring, automated retraining, and seamless adaptation.

NI OptimalPlus GO redefines manufacturing efficiency by delivering traceability across the entire product lifecycle, from supply chain to end user. By providing a single source of truth, advanced analytics, and intelligent, rules-driven actions, it enables teams to leverage their data to maximize yield and reduce costs. 

Practical Applications That Deliver Results

What makes NI SystemLink and  NI OptimalPlus GO particularly valuable are their guided workflows and reusable ML templates that significantly lower the barrier to entry and allow engineers to quickly integrate MLinto existing processes. Malki highlighted three practical applications:

• Trend Detection models monitor and forecast changes in test data, including yield rates and measurement consistency. The system automatically alerts teams when metrics approach or cross defined thresholds, enabling proactive intervention.
• Test Point Reduction integrates directly with measurement tools to strategically decrease sampling points, balancing the need to minimize escapes with configurable trade-offs between test time savings and quality.
• Anomaly Detection represents a significant advancement over traditional approaches. While conventional methods required extensive datasets of known issues to train effective models, NI solutions enable immediate implementation without historical examples, significantly reducing manual effort.

Real-World Impact: Efficiency and Quality Improvements

The impact of NI's simplified ML tools isn’t theoretical—it's measurable and proven. Test engineers using the test-point reduction model in RF testing have experienced dramatic efficiency gains with average test times decreasing by over 60% while maintaining accuracy and quality standards.

In production environments, noise and anomaly detection capabilities have automated the typically labor-intensive process of inspecting waveforms and test data. This approach reduces costs and frees up valuable engineering time while quickly identifying systematic issues before they escalate.

Sustaining Success Through Continuous Improvement

Deploying ML models is just the beginning of the journey. As Malki emphasized during his presentation, long-term success requires ongoing optimization to ensure the models remain accurate and effective. Engineering teams must be prepared to adapt their models as data patterns evolve and new challenges emerge. Luckily, NI SystemLink and NI OptimalPlus GO already include automated change detection re-training support.

Empowering Engineers to Drive Innovation

NI is working to eliminate the friction that once made machine learning seem inaccessible. With intuitive tools and guided workflows now available, test engineers can accelerate data-driven decisions and focus on innovation. To learn more about how these solutions can accelerate the move to machine learning and transform your test process, explore NI SystemLink and NI OptimalPlus GO.