Overview of Aito

Documentation

April 25, 2019

Aito is a database integrated with machine learning functionality. Databases commonly focus on searching and filtering known information, but Aito adds the ability to predict the yet unknown information.

Aito can predict the unknown

When data is uploaded to Aito, the patterns and features of the data are analyzed and indexed. This information is used query-time to provide rapid responses to any request. You could for example ask Aito to predict the category of a product.

Predict the category of a product
Predict the category of a product

The example is a simplified one but the same logic applies to larger and complex datasets – insert known data to Aito and you're able to query the unknowns.

Tables and columns

Aito models your data in a similar way as traditional relational databases: as a collection of tables with a pre-defined data structure. The whole database is described with a schema object, which looks like this:

{
  "schema": {
    "users": {
      "type": "table",
      "columns": {
        "id": { "type": "String" },
        "name": { "type": "String" },
        "age": { "type": "Int", "nullable": true }
      }
    },

    "messages": {
      "type": "table",
      "columns": {
        "id": { "type": "String" },
        "user": { "link": "users.id", "type": "String" },
        "text": { "type": "Text", "analyzer": "English" }
      }
    }
  }
}

Schema supports basic data types, nullable fields and one-way links. The type information allows Aito to better analyze the features of the data and also links between tables are used as information for machine learning functions. Uploaded data is validated against the given schema.

How does Aito work?

At the heart, Aito is a statistical machine that analyzes the inserted data into features with statistical properties. Aito uses these statistical properties to perform machine learning operations.

How the featurisation is done, depends on the field type. For example the Text type supports an "analyzer" option which allows you to control how a text field is splitted into features. Some queries, for example Relate, return the features instead of the actual values of the field.

In addition to splitting into features, Aito calculates statistical properties for them. You can access some of the statistical properties through Relate API. The returned properties include the frequency of the feature, the frequency given some condition, relationships with other features, etc.

You can read more about Text field analyzers from our API docs.

How is Aito different?

Aito is a different type of approach to making your software smart. In many cases, that's the business need behind machine learning technology. The obvious benefit of Aito is convenience – deployments, hosting, scaling, monitoring, and security are taken care for you. Other distinctive features of Aito are automatic feature selection and query-time modeling.

The easiest way to understand is by comparing the workflow of a "traditional" machine learning project to how it would be done with Aito. As an example, let's consider a few smart features in modern email services. To automatically detect spam emails, traditionally you'd go through these basic steps to integrate the machine learning functionality to your email service:

Traditional ML-workflow

Now you can classify email as spam or not spam with the deployed model. These steps would be then iterated again when the model needs to be retrained with new data.

After this you implement a new smart feature which suggests who you should most likely add to the CC field of the email. You have gained experience of the dataset to work more efficiently, but you still need to do the same steps again:

Traditional ML-workflow

.. and this continues for each different machine learning problem. The Aito workflow on the other hand is more similar to a workflow of using a database:

Aito workflow

After the data has been inserted into Aito, you can solve a dynamic set of problems which are solvable in the limits of the query language. In the email service example, we would now be able to:

  • Classify emails as spam or not spam
  • Predict the most likely person who to add into the CC field
  • Search emails which match to a given phrase
  • Match a relevant ad based on the email body text
  • Explain what features of an email are most typical for a spam email
  • .. and much more! Check out the API docs for examples.

The teaching of Aito happens by inserting new data points via the API.

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