The Engine as a tool for teaching ML and data-intensive research

The AI & Analytics Engine enables educators to introduce machine learning to students and provide exposure to the complete ML development pipeline.


The education sector is seeing unprecedented demand for ML courses and ML-enabled research for data-intensive research areas.

A significant portion of students embark on this journey without any foundational coding background. Current software tools used to develop machine learning models in these settings are often complex, requiring coding skills, are hard to scale and manage for large student bodies, expensive to maintain and update, and often bereft of features aligned with learning and marking coursework.

These hurdles lead to a scenario where a vast group of students and researchers feel sidelined or without access to ML.

The recommendation is clear – software that enables educators to introduce the basics of ML, laying down the common themes and concepts with a hands-on teaching tool that makes it easy to follow the logic and get comfortable with the topic. This is where the AI & Analytics Engine excels.


Current challenges

Education providers are struggling to keep up with the growing demands to provide teaching aid software for ML-focused or enhanced subjects and research areas. The current approach is neglecting students without a coding background resulting in missed innovation opportunities, increased student attrition rates, and worsening the shortfall in graduates equipped with ML skills.


Solving the tooling issue with the AI & Analytics Engine

The AI & Analytics Engine (the Engine) is an end-to-end no-code ML development tool. It provides exposure to the complete ML development pipeline from data preparation and feature engineering to model training, explainability, and inference. The Engine represents software capable of overcoming the barriers of complexity, scalability, and manageability in current tools employed in education settings, putting the focus back on building a deep understanding of concepts and processes.

By tapping into its capabilities, there's an opportunity to facilitate the seamless transition of students into the ML domain, guaranteeing a productive and hands-on learning experience.


Key learning moments

The Engine can assist as a virtual teaching aid for a hands-on experience with:

  • Problem Identification and data ingestion

  • Detailed graphical data analysis 

  • Data wrangling, data cleaning, and feature engineering

  • Model selection based on model performance, training time & inference time. 

  • Model training with definable train and test splits

  • Model understandability and interpretability (what-if analysis and feature importance)

  • Model and feature set experimentation  

  • Model deployment and periodic prediction systems

Students are empowered to put their learning in class to the test by making decisions based on their understanding at each step of the ML pipeline creating an optimal learning environment. For researchers, the process of developing and testing their ideas is streamlined.


Benefits of using the Engine

In the evolving landscape of education and research, the Engine is primed to cater to the unique needs of students, course administrators, researchers, and academics alike. Its intuitive no-code environment ensures rapid onboarding, simplifying the learning process for both newcomers and experienced individuals.

Students benefit from a comprehensive ML pipeline, guiding them from raw data processing to insightful conclusions. For educators, the platform's scalable administration tools streamline the process of student enrollment, data set assignment, and progress tracking.

With these tools, the emphasis is squarely on learning, eliminating unnecessary complexities that often hinder the educational process. For enhanced integration, the platform offers an SDK & API designed for seamless compatibility with Learning Management Systems, catering to advanced courses and students.

The Engine is a versatile solution that adapts to various institutional needs. Used across industries, the Engine successfully translates learning and research efforts into practicable skills and innovation.


Interested in understanding how The Engine can be used as a tool for teaching ML? We're here to help!




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