Years ago, I implemented my first clustering algorithm for a Genetic Algorithm targeting multi-objective optimization. The code was written in C and ran on what was then standard personal computing hardware. The entire setup, including a few key parameters, was so straightforward it could be explained faster than a cup of coffee cools—this simplicity and clarity are what make Trad-ML approaches so valuable.

Over the years, I've observed various ML methodologies being applied with differing levels of effectiveness and transparency across business sectors. Below, I highlight the enduring factors that underscore the relevance of Trad-ML.

The Continuing Relevance of Trad-ML:

Model Performance:
Problem-focused solutions inherent in Trad-ML exhibit a natural elegance. Whether it's making predictions, classifying data, providing recommendations, interpreting natural language, or clustering datasets, these tasks are ubiquitous across sectors like finance, human resources, and healthcare. Trad-ML not only offers time-tested models and established benchmarks ensuring a base level of effectiveness but is also supported by robust open-source frameworks like PyTorch, TensorFlow, and SciKit-Learn. These frameworks and benchmarks empower even non-specialist full-stack software engineers to achieve baseline standards of model performance effortlessly.

Model Explainability:
The necessity for humans to understand the reasoning behind machine-based decisions becomes critical when these decisions impact areas such as health, finance, and employment. Trad-ML, supported by well-established ML-Ops practices, enhances the auditability and interpretability of both data-driven and model-driven parameters. This is especially important as AI safety standards like ISO 42001 place a premium on the explainability provided by the datasets used in ML operations.

Model Costs:
The high cost of AI-specific, purpose-built GPU/TPU architectures often makes them prohibitive for many businesses, thus stifling innovation at the grassroots level. In contrast, most Trad-ML techniques can be implemented using generic, widely available GPU/CPU architectures. This compatibility means that most existing cloud and personal computing resources are already suitable for training and deploying Trad-ML models, significantly simplifying financial approval processes for CFOs and tech leaders.

Acknowledging the Impact/Unlock of Gen-AI:
The most profound influence of Gen-AI in the business and technical realms is its role in elevating ML/AI from a secondary optimization tool to a primary implementation approach. This shift, driven by the momentum of Gen-AI, has established both traditional and generative machine learning as pivotal elements in technological deployments.

Additionally, there are also problem domains specifically suited for Gen-AI. I will cover them in detail and approaches to measure there effectiveness in separate article. Stay tuned!