Pre-Built Machine Learning Workflows

Kepler is a platform that provides pre-built Machine Learning workflows. The platform is tailored to assist business analysts and non-technical professionals. It equips them with the same resources and methods used by data scientists.

Director of UI / UX

There is a trend in the business analyst field where they are bridging the gap between their positions and the tasks of a data scientist. Through the adoption of programming languages like Python and R, there’s a clear opportunity to productize some of the internal tools we’ve built for our internal product, the Pipeline Builder.

By discovering, predicting, and automating insights, we enabled users to quickly reveal trends in complex data sets, automate data processing, and leverage predictive analytics for strategic decisions. This not only significantly boosted efficiency by eliminating manual tasks, but also empowered organizations to influence outcomes and prioritize actions based on AI insights, thereby significantly enhancing the end-user experience.

In the initial stages of our project, we acknowledged the importance of understanding both the needs of business analysts and the methods used by our data science team. We wanted to bridge the gap between these two disciplines and create a tool that could cater to both.

Understanding of a business analyst’s tasks and objectives: Conducted several user interviews.

Platform used: Utilized UserInterviews.com for coordinating and conducting user research.

Outcome of approach: Gained valuable insights into the challenges that business analysts face and the tools they use.

In parallel, we also:

Conducted interviews with our internal data science team: Aimed to comprehend the internal tools they’ve developed.

Objective of interviews: Understand how the tools could be productized for more extensive usage.

Goal: Understand the technical aspects that could be simplified for business analysts without compromising the functionality needed by data scientists.

This research and discovery phase laid a solid foundation for our project, enabling us to identify the key features that our tool would need to support both business analysts and data scientists effectively.

Our design process for this project was intensive and collaborative, involving both business analysts and our internal data science team. Having gained valuable insights from our research and interviews, our objective was to develop a solution that automated machine learning workflows, while requiring minimal user inputs.

Concept Creation: Started with brainstorming ideas based on the feedback and needs identified during the research phase.

Creating Wireframes: Moved onto creating wireframes for the design, which acted as the blueprint of the solution.

Wireframes Purpose: These wireframes laid out the structure, hierarchy, and relationship between various elements of the tool.

We then developed prototypes of our design, bringing our wireframes to life. These prototypes provided a tangible way to test out our ideas and evaluate the user experience. During this stage, we went through multiple iterations, refining our design based on feedback from both business analysts and our data science team.

Iterative Design Process: Allowed us to constantly test and improve our solution, ensuring it met the needs of our users.

Collaboration: By working closely with both business analysts and our data scientists, we bridged the gap between these two disciplines.

Outcome: Created a tool that effectively caters to both groups.

One of the major challenges we encountered during this project was the time-consuming nature of training Machine Learning models, especially when dealing with larger data sets. The process could extend to several hours, which was a significant pain point.

Addressed Challenge: We addressed this challenge in two ways.

Enhanced Transparency: Firstly, we enhanced the transparency of the loading screen.

Clear Communication: We made sure every step of the process was clearly communicated to the user through the text presented on the loading screen.

User Understanding: This allowed the users to understand what was happening behind the scenes during the extended loading periods.

The second solution we implemented was a notification system. This system, both in-app and via email, informed the user when their model was done training. This was a significant improvement in the user experience, as it allowed them to carry on with other tasks without having to constantly check on the training progress.

Our primary solution was the development of a linear user experience for the Kepler application, designed to guide users seamlessly through the Machine Learning model creation process. This intuitive workflow enables users, even those with limited technical knowledge, to successfully navigate the stages of importing data, training, improving, and deploying a model.

Making Machine Learning Accessible: We focused on making the complex process of Machine Learning model creation accessible and comprehensible to all users, thereby democratizing access to this powerful tool.

Facilitating a Clear Path: By facilitating a clear path through each step, we ensured that users could not only complete the process but also understand each stage.

Enhancing Learning: This approach enhances their learning and increases their capacity to utilize Machine Learning models effectively.

This linear UX design played a crucial role in bridging the gap between technical experts and business analysts, making Kepler an inclusive platform that empowers users with varying levels of technical proficiency.

The Kepler platform equipped business analysts with advanced tools, empowering them to leverage machine learning in their analysis and decision-making processes.

The intuitive and user-friendly interface of the Kepler platform reduced project times dramatically, often from weeks to just a few days.

The streamlined and efficient workflows facilitated by Kepler resulted in improved productivity, faster insights, and more informed business decisions.

In conclusion, the Kepler project successfully delivered a user-friendly platform that democratizes access to machine learning tools. It bridges the gap between technical experts and business analysts, providing them with an efficient and intuitive workflow that simplifies the process of creating Machine Learning models. The impactful results of the project include dramatically reduced project times, improved productivity, faster insights, and more informed business decisions.