Measure First In a Machine Learning Model – 5 Reasons Why It Matters

The way we train machine learning models is fundamentally flawed. The current process for working on unsupervised learning problems lacks visibility. As a result, data scientists and machine learning experts cannot determine the amount of data needed to build the correct model or if they’re using the right data. In fact, it’s predicted that 87% of AI projects will never make it into production. The majority of today’s data science workflow comes down to guesswork, which are both time-consuming and expensive.

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Every field of science and engineering starts with measurement. Before we build a car, a plane, a bridge, or a computer chip, we measure something. Every engineer quotes some version of the trusted axiom, “measure twice, cut once.” Unfortunately, today’s data science and machine learning experts subscribe to the ideology that we should just throw more GPUs at the problem. Therein lies the problem, no one is measuring the learnability of their date sets before building machine learning algorithms.

How can organizations address this glaring pain point? The solution is simple: machine learning experts should improve the learnability of their data by measuring it first. By doing so, businesses can minimize the uncertainty associated with machine learning model. Here are five reasons why this should matter to your organization:

1. Invest the right amount of time and money in data creation

Measurement allows data scientists to consciously and accurately choose how much data is needed ahead of time. They can understand whether the right type of data is being created, thus enabling them to better estimate the hours required to solve the problem at hand. And most importantly, measurement provides visibility that allows data scientists to know when a model has failed, allowing them an opportunity to abandon the project before spending more money and time.

2. Get a handle on costs

Compute, storage, and processing costs add up all too quickly. Thankfully, you can deduce how long it will take to train and retrain your models through measurement and know upfront the risk that a model might not work. This information allows organizations to better understand the deployment costs associated with a machine learning project.

3. Eliminate bias

Identify potential biases before building models whether the available data represents the target operating environment fairly and accurately. Biases in algorithms could have large consequences, so it’s encouraged to look at tools for diagnosing bias in predictive modeling or hire talent that is trained in potential problems with data collection.

4. Explain what your machine learning models are doing

Measuring first allows organizations to understand the driving force behind the quality of their models and gain insights into the information in the data, providing a high-level explanation as to model activity.

5. Minimize the probability of overfitting

If a model is built without learning a rule, it merely memorizes the data causing it to overfit; thus, it would be unable to handle novel input. The module would not be applicable in the real world, resulting in a failed machine learning workflow. Measurement allows data scientists to understand this risk prior to building a model.

We should no longer continue to operate in the dark, throwing more money, compute, and data at machine learning problems. The solution is taking a more systematic, engineered approach to machine learning, which will allow businesses to increase their return-on-investment. Ultimately, data scientists will reduce the amount of guesswork involved when solving problems that come across their desks, enabling them to tackle more complex, valuable issues.

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