Predictive Maintenance

Predicting failures before they happen can save you time and money. Akkio’s machine learning algorithms can help you build models that predict when maintenance is needed, so you can keep your equipment running smoothly.


In the industrial world, a single broken machine can lead to a massive disruption in production. The cost of unexpected downtime can be enormous, so many companies have turned to predictive maintenance to try to avoid these costly disruptions. In fact, the average cost of unplanned downtime is around $260,000 per hour. Globally, this adds up, and the world’s largest manufacturers lose a shocking $1 trillion per year to machine failures.

Predictive maintenance is the process of predicting when a machine will fail, so that issues can be predicted and corrected before they become a serious problem.

The challenge of predictive maintenance is that most failures are not preceded by any obvious warning signs. This means that data must be collected and analyzed in order to build models that can predict when a failure is likely to occur. Akkio's machine learning algorithms can help you build these models quickly and easily.

Once you have a model that can predict failures, you can use it to schedule preventive maintenance tasks. This can save you time and money, as well as prevent unexpected downtimes that can have a major impact on your business.

Predictive Maintenance With No-Code AI

To get started, sign up to Akkio for free. As with any machine learning task, the first step is getting historical data and picking a column to predict.

Historical Data

We’ll use a Kaggle dataset titled pump sensor data. The dataset comes from a water pump that experienced 7 system failures in the last year. There are 52 sensor units that collected data, which we can use to predict water pump failure and take corrective action.

We will use the “machine_status” column to predict water pump failure. This column has two main values: Normal and recovering (which indicates a failure). The first step is to import the data into Akkio. The data is stored on Kaggle as a CSV file, so we can just upload it as a table. You'll then get a preview of the dataset, including modifiable data types, columns, and row count.

The Akkio Flow Editor showing a predictive maintenance dataset.

Each of the sensor values is potentially indicative of failure probability, and each will be given a corresponding weight in the model. For example, perhaps a particularly high or low reading of a group of sensors is more likely to indicate a pending failure.

These sorts of relationships in the data are automatically discovered by the model. Crucially, the file includes our desired target variable, or whether the pump failed.

Building the Model

Now, we can click on the second step in the AI flow, which is “Predict.” Under “predict fields” you can select the column to predict, named “machine_status.”

You can add or remove as many columns as you’d like, and we'll build a model based on the selected columns. Then just hit “Create Predictive Model,” and you’re done.

The Akkio Flow Editor showing a model to predict machine failure.

You can select a longer or shorter training time—ranging from 10 seconds to 5 minutes—for potentially more accurate models. Keep in mind that longer training times will not always necessarily perform better.

Also note that you don’t pay for model training time, unlike with many typical automated machine learning tools, so feel free to build as many models as you’d like.

Analyzing the Model

After a model is created, you get a simple overview, which highlights the top fields, model prediction quality, sample predictions, data segments, and more. 

The Akkio Flow Editor showing the accuracy of our predictive maintenance model.

We can see that we’ve managed to build a highly accurate model. We can ignore the third column value (named “broken”), since it has only 1 occurrence in over 24,000 rows. Overall, we have a 99.54% accuracy, with robust predictions for both normal and recovering water pumps.

There’s also the option to “See Model Report,” which lets us easily collaborate with others and share these model details with anyone, even if they don’t have an Akkio account. In short, we’ve discovered that it’s very easy to build an accurate model to optimize inventory.

Deploying the Model

Now that we’ve built a model, it’s time to deploy it in the real world.

With Akkio, it becomes trivial to deploy complex machine learning models. Deployment via web is an easy way to instantly serve predictions. It’s also possible to deploy through Salesforce, Google Sheets, Snowflake, and the Akkio API, with many more methods coming soon.

The Akkio Flow Editor showing model deployment options. 

Further, you could deploy the model in practically any setting with Zapier, a no-code automation tool that connects tools with “Zaps.” Zapier allows you to link up thousands of different tools, so no matter where your data is coming from, it’s easy to connect it to Akkio to get a prediction, even if you don’t have any technical expertise.

If you’re looking for more technical power, you can also use Akkio’s API, which is formatted as a Curl command, allowing you to send a GET request that contains your flow key, API key, and input data, and get a prediction back.


We've explored how predictive maintenance works and how Akkio can help you build predictive models quickly and easily.

Some of the benefits of using predictive maintenance are that it can help you save time and money, prevent unexpected downtimes, and improve your overall production process.

If you're interested in learning more about predictive maintenance or Akkio, please don't hesitate to request a demo or sign up for a free trial.

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