Why AI and IoT Researchers Have Struggled With Tools

And How Favoriot Intelligence Removes a Long-Standing Barrier

For many years, AI and IoT researchers have faced the same paradox.

The volume of data keeps growing.
The questions are becoming more sophisticated.
Yet the tools meant to support research often slow it down.

This challenge does not come from a lack of expertise. It comes from fragmented systems that force researchers to spend more time managing pipelines than studying behaviour, patterns, and outcomes.

The recent launch of Favoriot Intelligence, the built-in Machine Learning capability within the Favoriot platform, directly addresses this issue. The functionality described in the Favoriot Tutorial v2 confirms that machine learning is now part of the same workflow as IoT data ingestion, storage, and visualisation.

This shift has important implications for both IoT researchers and AI researchers.

The Structural Problem in IoT Research

IoT research is grounded in real-world data. Sensors continuously generate readings of temperature, humidity, vibration, energy usage, and environmental conditions.

Despite this, many researchers face recurring constraints:

• Data lives inside an IoT platform, but analysis happens elsewhere
• Insights are produced after long export and preparation cycles
• Research outputs remain descriptive rather than actionable

The result is research that looks backwards instead of forward.

Favoriot Tutorial v2 validates that this separation is no longer necessary. Machine learning models can now be configured directly within the platform using existing IoT datasets. Researchers no longer need to move data to external environments to experiment with prediction or pattern learning.

The Parallel Struggle for AI Researchers

AI researchers face a complementary problem.

They are skilled in building models, tuning algorithms, and evaluating performance. What they often lack is reliable, continuous, and contextual IoT data.

Common obstacles include:

• Limited access to live sensor data
• Reliance on static or simulated datasets
• Complex steps to ingest IoT data into separate ML systems

Favoriot Tutorial v2 demonstrates that IoT data and machine learning now share the same pipeline. Models are trained using real operational data already stored in the platform, without the need for data export or duplication.

This directly supports applied AI research, where models must reflect real-world behaviour rather than idealised conditions.

What Favoriot Intelligence Changes in Practice

Favoriot Intelligence is not positioned as a research experiment. The tutorial documentation confirms that it is a usable, configurable feature within the platform.

From a research standpoint, this enables three critical shifts.

1. Data and Machine Learning Share the Same Context

IoT data retains its meaning when it enters a separate ML environment. Time series, device identifiers, and operational context remain intact throughout the learning process.

This reduces errors and improves reproducibility.

2. Faster Iteration Cycles

Researchers can define datasets, train models, and evaluate outputs without waiting for external data transfers or infrastructure setup. This makes exploratory research more practical.

3. Insights Can Drive Actions

Model outputs can be linked to alerts and system responses inside the same platform. Research is no longer limited to observation. It can study cause, effect, and intervention.

Validated Benefits for IoT Researchers

Based on the documented capabilities in the Favoriot Tutorial v2, IoT researchers gain:

• Predictive insight directly from sensor data
• Reduced dependency on external analytics tools
• A clearer path from monitoring to understanding system behavior
• The ability to study anomalies and trends as they emerge

This lowers the technical barrier to applying machine learning in IoT research while preserving methodological rigour.

Validated Benefits for AI Researchers

AI researchers benefit in equally concrete ways:

• Immediate access to structured IoT datasets
• Elimination of data export and re-ingestion steps
• Models trained on real, evolving data
• Easier collaboration with domain experts working on the same platform

This supports research that is grounded in operational reality rather than theoretical datasets alone.

A Shared Platform Encourages Shared Research

One of the most important outcomes of this integration is collaboration.

When IoT researchers and AI researchers work on different systems, collaboration becomes procedural and slow. When they work on the same platform, collaboration becomes natural.

They see the same data.
They interpret the same outputs.
They measure results using the same references.

Favoriot Intelligence enables this shared environment without requiring either group to abandon their expertise.

Favoriot’s Invitation to the Research Community

With the Machine Learning capabilities now validated and documented in Favoriot Tutorial v2, Favoriot welcomes collaboration with:

• Universities and research institutions
• AI and IoT research labs
• Postgraduate and doctoral researchers
• Industry-academic research initiatives

The objective is straightforward.

Spend less time assembling tools.
Spend more time producing knowledge, insight, and impact.

Closing Thought

The future of applied research lies at the intersection of data, intelligence, and action.

Favoriot Intelligence removes a barrier that has existed for too long by placing machine learning where the data already lives. For researchers, this is not about convenience. It is about focus.

Focus on questions that matter.
Focus on results that can be tested.
Focus on research that connects theory with reality.

That is where meaningful progress begins.

FAVORIOT Intelligence References

• Official Announcement -Favoriot Evolves into an AIoT Platform with Built-in Machine Learning
• Why Favoriot Chooses Machine Learning Over LLMs
• Machine Learning vs Deep Learning in IoT
• Traditional Machine Learning vs Large Language Models (LLMs)
• Why Favoriot’s Built-in Machine Learning Matters for AI Researchers and IoT Developers
• Why Favoriot’s ML Infrastructure Reduces Costs
• AI and ML in IoT, Explained Without the Jargon
• Start Anomaly Detection in IoT with Less Data
• AI and ML in IoT, Explained Without the Jargon
• The Key Differences: Favoriot’s Rule Engine 2.0 and AI Agents
• The Role of Machine Learning in IoT Systems
• Favoriot’s Rule Engine 2.0: A Structured Approach to IoT Automation
• A Quiet Shift Is Coming to the Favoriot Platform