Making ML as easy as SQL — introducing the predictive database

Could AI be made radically more accessible and faster to use? How about, if you could get the predictions, the recommendations and the AI functionality with queries like this:

{
  "from": "engagements",
  "where": {
     "customer": "john.smith@gmail.com"
  },
  "recommend": "product",
  "goal" : "purchase"
}

In a typical tech environment, it is extremely easy to find applications for AI/ML. The end users have gotten used to AI-driven features like recommendations. Features like personalization can provide huge benefits for both the user and the business. AI-related features are a common discussion topic both in the product teams, startups and in the management. There is simply an abundance of ideas and the desire to bring AI to the software.

Yet, while AI is huge, it can be inaccessible for the average development team. Not every development team is armed with a data scientist. Even more, it is often prohibitively expensive. The image below depicts one way to frame a typical machine learning project. What the picture omits is, that the process can take weeks or months, it can cost tens or hundreds of thousands of euros to get it through, and the results are not always what was the expected.

The workflow of a typical ML modeling project

Few product teams can spare ‘extra 50k€’ to try a smart functionality that might improve the product. As a consequence, maybe over 90%, of the value-adding smart functionality is not financially feasible.

Given all this, the true question is that could AI be made radically more accessible and faster to use?

The predictive database

To truly understand the significance of the predictive database, let’s consider the following scenario. You have a database that provides you the normal database operations for your grocery store. You can use the database to list the historical customer purchases like this:

{
  "from": "purchases",
  "where": {
     "customer": "john.smith@gmail.com"
  }
}

Now, your PO has done some interviews and found the customers complaining that filling the weekly shopping list is a huge hassle. What would you do? Perhaps you could use the following query for predicting the customer’s next purchases:

{
  "from": "purchases",
  "where": {
     "customer": "john.smith@gmail.com"
  },
  "predict": "productIds",
  "exclusiveness": false
}

The query results will list the customer’s next purchases by the purchase probabilities. You can use it to prefill the shopping basket with the weekly butters and milks. It can also be used to provide recommendations.

But there is even more. How about, if you have some impression and click data, and your PO, your customers and the team itself desire the personalized search? Let’s try the following query to recommend ‘milk’ related products:

{
  "from": "impressions",
  "where": {
     "customer": "john.smith@gmail.com",
     "product.text": {
       "$match": "milk"
     }
  },
  "recommend": "product",
  "goal": { "click": true }
}

The query returns the products containing the word ‘milk’ by the probability the customer might click it. If the customer is lactose-intolerant, the lactose-free products will be listed first. As such, the query seamlessly combines the soft statistical reasoning with the hard text search operation to get the sought results.

The grocery store demo using the AI queries

There is a wide array of other AI-related problems that can be quickly solved with queries. For example, you can form simple queries to propose tags for products, to propose personalized query words, match email-lines with the products in the database or explain the customer purchases and behavior. As such, these simple queries can provide the intelligent user experience, the process automation and analytics.

The impact

Now consider the previous vision, the examples and the fact that we implemented the vision. The dramatic reduction of cost in implementing the smart functionality means that it becomes economical to:

1. Add the AI functionality into the internal tools, PoCs, the MVPs and the smaller products.
2. Add the numerous smaller AI functionalities, like the little things that help ease the users’ lives.
3. Make the software thoroughly smart and include all the smart functionality from the AI features’ buffet.

Overall, the changing economics can work to democratize the AI and make AI affordable; it could fundamentally change the way the AI is used.

Meet Aito

This vision of the predictive database was implemented by our startup called Aito. Our product, also called Aito, is currently in open beta, and it is offered as a SAAS for developers around the world.

As a predictive database, Aito lets you query both the known facts like a normal database and the unknown like the AI / ML solutions. It also lets you seamlessly combine the database filtering with the machine learning scoring functionality.

The workflow with the Aito AI queries

Aito exists to support the developers, who value quick time to market, and who are looking for powerful, yet simple tools, that can solve a wide range of AI and data related problems.

Interested? Check out this documentation to have a closer look — or just try it out yourself.

Originally published at https://aito.ai.
Written by Antti Rauhala, Co-Founder and Chief Data Scientist at aito.ai