Why Favoriot’s ML Infrastructure Reduces Costs

When people see the phrase “No external pipelines. No separate ML infrastructure.”, it can sound technical or abstract. In practice, it describes a tangible benefit that directly affects how quickly, reliably, and cost-effectively an IoT solution can be built and maintained. This is precisely what the Favoriot Machine Learning feature offered.

This article explains what those terms mean in simple language and why they matter for teams building smart IoT solutions with Favoriot.

The Traditional Way Machine Learning Is Added to IoT

In many IoT projects, machine learning is not part of the IoT platform itself. It sits outside, as a separate system.

A typical setup often looks like this:

1. Devices send data to an IoT platform
2. Data is exported to another service or cloud environment
3. A custom pipeline cleans and prepares the data
4. Models are trained in a separate ML system
5. Predictions are sent back to the IoT platform
6. Rules or alerts are triggered based on those results

Each step depends on integrations, scripts, credentials, and ongoing maintenance.

These connections are what we call external pipelines.

What “No External Pipelines” Means

When Favoriot says “no external pipelines,” it means the machine learning process runs on the same platform that already handles IoT data.

Data does not need to be:

• Exported to another system
• Transformed by custom scripts
• Sent back through APIs

Instead, the data stays where it is collected. Models learn directly from stored IoT data, and the results are available immediately.

This removes unnecessary handoffs and reduces complexity.

What “No Separate ML Infrastructure” Means

Machine learning usually requires its own infrastructure, such as:

• Compute resources for training
• Storage for datasets and models
• Jobs to retrain models on schedules
• Systems to run predictions

In many projects, teams must set up and manage all of this themselves.

With Favoriot, this infrastructure is already built into the platform. Users do not need to:

• Provision training servers
• Manage inference services
• Maintain ML pipelines

They configure the model, and the platform handles the rest.

Why This Matters in Real Projects

Simpler System Architecture

Fewer systems mean fewer points of failure. When data, learning, and actions live in one platform, the system is easier to understand, test, and support.

This leads to more stable deployments.

Faster Development and Deployment

Without external pipelines:

• There is no data export step
• There is no integration layer to maintain
• There are fewer things to troubleshoot

Developers can move faster from idea to working solution.

Lower Ongoing Cost

Separate ML systems often introduce hidden costs over time. These include infrastructure, monitoring, and specialist skills.

An integrated approach helps control these costs by keeping everything in one place.

Better Fit for Most IoT Teams

Most IoT teams are focused on delivering solutions, not managing complex AI infrastructure.

By removing the need for separate ML systems, Favoriot allows teams to use machine learning without becoming ML operations experts.

Easier Automation and Action

Because machine learning results are already inside the platform:

• Predictions can be shown directly on dashboards
• Models can be used inside rule engines
• Alerts and actions can be triggered immediately

There is no extra integration step between insight and action.

A Simple Way to Think About It

Imagine running a factory.

Traditional approach
Raw materials are sent to another factory for processing, then shipped back to you for use.

Integrated approach with Favoriot
Processing happens in the same factory where the materials arrive.

The second approach is faster, simpler, and easier to manage.

Final Thoughts

When Favoriot states “No external pipelines. No separate ML infrastructure.”, it is highlighting a design choice that reduces complexity and increases practicality.

It means:

• Less setup work
• Less maintenance
• Faster results
• More reliable smart systems

Machine learning becomes a natural part of the IoT platform, not an additional system to manage.