The flow navigator is the heart of Akkio’s no-code AI. You can access the Flow Navigator by clicking on any flow.
In Akkio, a flow is an end-to-end AI model from data input to model deployment. The flow navigator is the visual interface used to connect data, build an AI model, and deploy, entirely without code.
Pro-Tip: If you want to use the same flow for different purposes, you can duplicate it from the “All Flows” homepage!
Below, you can see an example of the “Churn Prediction Demo” flow.
In just a few clicks, you can use the flow navigator to add data from your favorite sources (e.g. Google Sheets, Snowflake, CSV, or Salesforce), build AI models in as little as 10 seconds, and deploy via Zapier, API, web app, and more.
Predict (train your model)
You can build an AI model to make predictions on your connected data by clicking on “Add Step,” and then “Predict.”
Then, you’ll need to select the column to predict.
For example, in the Churn Prediction Demo, that column is simply called “Churn.” This is an example of classification, as the model will predict that a customer will either churn, or not.
The “Training Mode” section gives you several options for model training:
Fastest (10 seconds)
High quality (1 minute)
Highest quality (5 minutes)
The “highest quality” selection means more training time, but it won’t always lead to higher accuracy, due to a phenomenon known as “overfitting,” which is when a machine learning model only learns the training data well, but does not generalize to new data. Feel free to try out various training modes, as you won’t be charged at all for training time.
In any case, upon clicking “Predict,” a model and model report will be generated, as seen below.
To build a forecasting model, the steps are exactly the same, in that you connect an input dataset, and select a column to predict.
However, the resulting model and model report will be different. In a forecasting model, the “prediction quality” will be shown as a value that predictions are “usually within,” as well as an RMSE score. Further, the “sample predictions” will be quantitative values, instead of classes like “Yes” or “No.”
Datasets are the fundamental building blocks of Akkio, and indeed fundamental to all machine learning tasks.
Akkio comes with several datasets pre-included as part of “demo” flows, so you can see how the product works. This includes:
An insurance dataset
An employee attrition dataset
A telecom customer churn dataset
A credit card dataset
A historic conversions dataset
A restaurant reviews dataset
An avocado prices dataset
You can easily add or delete datasets, whether from a CSV, Excel file, JSON file, Google Sheets, Salesforce, Snowflake, or (soon) Airtable.