How to deliver successful AI products with Keboola?

In this article, we’ll look at:

• Key Keboola features that streamline AI productization.
• AI models you can launch using Keboola.
• Sneak peek into Keboola's upcoming AI features designed to simplify DataOps and AI initiatives.
• Our forthcoming EmPower event with focus on AI and its business applications. 

The core Keboola offering: Features to simplify AI initiatives

At its core, Keboola is a platform that helps you automate all your processes: from streamlining data operations to launching new AI proof of concepts.

Keboola offers self-serviced and fully governed features that help you:

1. Make your data self-serviceable. Whether you’re building a data warehouse, data lake, or data mesh architecture, Keboola gives you the tool to set up your data organization in days instead of months. Over 250 pre-built connectors help you integrate data from sources to destinations with a button click. Data templates set up end-to-end data pipelines for the most common use cases in a couple of clicks or an API call. And you don’t have to worry about DataOps. Automated security, scalability, data lineage, monitoring, and observability come out of the box with Keboola.

2. Built for everyone. With Keboola, your software engineers can build with tools that accelerate their processes: low-code features (in Python, R, Julia, SQL), CDC replications, development branches, CLI automation features, and sandboxes are just some of the many features they love. Moreover, domain experts without technical expertise can also build their data products with no-code features, such as no-code transformations and the drag-and-drop visual flow builder. Both can collaborate on the same AI projects side-by-side using different tools, and share and document data assets with the Data Catalog.

3. Automation by design. Keboola is built to automate every aspect of your data productization lifecycle. From code templates to automated backend scaling, Keboola liberates engineering resources to spend more time developing products than maintaining your system.

4. Multi-cloud scalability. Keboola supports every major cloud provider (Google Cloud Platform, Azure, Amazon Web Services), and its architecture is designed to scale seamlessly and practically infinitely. The platform grows with you and your product needs. Bonus: you can use all the machine toolboxes and APIs of every cloud provider within Keboola. Making it a multi-cloud data scientist toolbox. 

5. Prototyping governed toolbox. Moving fast is in Keboola’s DNA. This is why so many features are devoted to building proof of concepts. From sandboxes that abstract data from production resources to integrations with Streamlit that streamline turning your data into web applications, Keboola is the prototyping machine you were looking for.

6. Easy to use and share. It’s important to put your data products into the hands of your users. Whether the users are data builders or data consumers it doesn't matter, the product needs to be intuitive. This is why Keboola is designed to be easy to use and shared, so more people can test your data products without a steep learning curve.

All these characteristics make Keboola the ideal partner for building data applications. But what about data applications that focus on artificial intelligence?

What AI models can you launch with Keboola?

Keboola allows you to build any AI product. Let’s look at a couple that our clients built:

1. Olfin Cars, a leading seller of new and used cars in the Czech Republic, used Keboola and Marketing BI to collect historical data on purchases, competition, and online behavior and fed that data into AI algorithms. The AI algorithms predicted the marketing messages that were more likely to convert prospects into customers on their website. The result? A 760% increase in sales in a single quarter.
2. Rohlik, the e-commerce unicorn, uses Keboola and real-time machine learning algorithms to predict the number of bags that will be used to pack a customer’s order to plan courier routes as efficiently as possible.
3. Another e-commerce customer uses Keboola to analyze orders with natural language processing (NLP) algorithms and used the spotted anomalies to build a fraud detection system that preemptively warns them of order fraud.
4. Harri built Harri-IQ, a full-AI platform for external users that included NLP algorithms, recommender systems, and schedule optimization for the hospitality business on top of Keboola. The punchline? They’ve done it from start to finish in 3 months. 

How can Keboola support so many different AI use cases - from recommender systems to credit risk scoring, from fraud detection to schedule optimization?

Because Keboola is designed as a plug-and-play platform, where you bring the best tools for the job and Keboola automates the processes that slow you down in building your AI products.

Whether you’re using Google’s AutoML to train machine learning models that predict the next financial time series or Python’s PyTorch library to train computer vision algorithms that classify customer complaints, Keboola streamlines your AI productization end-to-end.

Sneak peek: Keboola is launching its new AI features

Despite there being many tools to optimize processes and products with AI, we’ve seen a massive gap in the underserved DataOps world. So we’re launching a private beta AI program that will automate a wide variety of DataOps activities with artificial intelligence:

Improved error messages

Error messages are often hard to interpret and even harder to resolve. Engineers get stuck figuring out what they should do to resolve the errors. Recognizing this issue, Keboola is building a state-of-the-art natural language processing engine that will turn the error messages into human-readable messages, and use past errors and solutions to recommend the best course of action to resolve the problem as the error is being raised.

Automated data governance

Documentation is an overlooked art and skill. But due to tight deadlines and the monotony of the task, engineering teams skip documentation processes leaving many data assets underspecified. This pushes the mess downstream, where data consumers find it hard to interpret tables without significant support from the IT teams. Keboola is introducing intelligent agents based on AI technology that generate documentation with one click.

Streamlined impact analysis

We often rely on governance policies and use data lineage to determine who did what and where. So if we need to change a piece of the code, we can manually inspect how this will impact the entire system. With the use of AI, Keboola will cut the time for such impact analysis. By generating knowledge graphs from the metadata produced, Keboola’s recommender system will be able to guide you in the execution plans. This feature will suggest where you need to add code and how this will impact the entire system.

Curious about other AI products we and our customers are building?

Get tickets to the frontline of AI productization: Join us at EmPower Event Series

EmPower is Keboola’s series of online events that showcase how modern companies are turning their data assets into data products. 

Here is what you can expect to hear at an EmPower event:

1. In-depth show-and-tells from our customers deep diving into how they are using Keboola and their hidden tricks to productize AI products.
2. New Keboola features that support AI product development.
3. Planned future AI features on Keboola’s roadmap that will get you ahead of competitors. 
4. Hands-on labs to turn the theory into practice. Everyone can write a story in half an hour about successful AI products. We’ll show you how to build the same products yourself.

Join us at our next EmPower Event to get a behind the scene look at how data and AI products are being built and used today, pushing the frontlines of businesses into a more successful future. 

‍