Getting started

Overview

MathFi.ai is a predictive AI toolkit that allows you to easily turn your tabular, labelled data into actionable predictions on new, unseen data. For that, the following is needed:

CSV file with tabular, labelled data
CSV file with unseen data, same format of labelled data

From there, getting your first predictions is only few steps away:

Access the platform
Create a dataset by uploading your CSV file with labelled data
Train a model from the created dataset
Create a prediction that uses the model by uploading your unseen data CSV file
Download your CSV unseen data file populated with results

In this page you can:

Follow a step by step explanation with sample dataset
Watch a video walkthrough
Find out next steps to get the maximum from MathFi.ai platform

Both UI dashboard and API (curl commands) are provided. Consult the glossary for key metrics and terms used.

Examine sample CSV data

To fully understand how CSV should be prepared for this platform, follow the input CSV creation guide. But for convenience, a couple pre-created labelled and blind CSV files are provided. This sample data represents a set of readings from IoT devices present in a oil plant, aimed at predicting failure in key infrastructure.

CSV file with labelled data

Blind CSV with unseen and unlabeled data

Predictions may return NaN probability values when the number of buckets is too low. To fix this, create a new dataset with more buckets and retrain. If NaN values persist, your training data may be insufficient. For this oil/gas plant example, using the default 20 buckets produces NaN values — increase to 65-100 buckets to resolve.

Key considerations:

The timestamp acts as the unique ID
The relevant features are the columns temperature, flow_rate, vibration_level, valve_position, motor_speed, chemical_concentration
The outcome (binary classification for this example) is coded in the anomaly_label column (0=no anomaly, 1=anomaly)

More data and use cases can be explored in depth in the Use cases section.

Access the platform

First of all, request access to MathFi.ai.

Once the form is submitted, you should shortly receive an email with your credentials. These credentials work for both the Dashboard and REST API.

Dashboard
API

The MathFi.ai Dashboard is a simple web application that allows you to upload your CSV with sample data, train a model and run predictions on new, unseen data.Use the link and provided credentials to log into the platform

After successful login, the following page should appear:

To access via API, use the provided links and credentials to login. The following are curl commands that use the endpoints fully described in the API Reference.To login:

curl --location 'https://{baseUrl}/api/login' \
--header 'Content-Type: application/json' \
--data-raw '{ "username": "[email protected]", "password": "<yourpassword>"}'

the response should look like this:

{
    "access_token": "<token>",
    "token_type": "Bearer",
    "expires_in": 3600,
    "username": "<your-email>"
}

The returned access_token has 1h expiration and needs to be passed into every request. More details in API Reference.

Create your first dataset

To get started, let’s create a dataset from the sample labelled CSV file

Dashboard
API

Select Datasets from the side menu, then Create button on right hand side. Then populate the form:

Dataset name: set a descriptive name, must be unique across the platform
Number of buckets: set it as 10 for this dataset. This is one of the hyperparameters that can be later modified to enhance model performance. More details on Hyperparameter tuning.

Click Save and wait for the dataset to finish processing.

Once the dataset is in COMPLETED status, it’s ready for training.

Creating a Dataset using the API is done as 2-step process:

Create an empty Dataset
Use the signed URL returned from the previous step to add the labelled CSV data

Create an empty dataset

To create an empty dataset using the API:

curl --location 'https://{baseUrl}/api/v1/datasets' \
--header 'Content-Type: application/json' \
--header 'Accept: application/json' \
--header 'Authorization: Bearer {token}' \
--data '{
  "datasetName": "OilPlantAnomalyV1",
  "numberOfBuckets": 10
}'

the response should look like this:

{
    "datasetKey": "4223df90-0c31-4abf-a0e8-339b5c79f7c7",
    "numberOfBuckets": 10,
    "status": "PENDING",
    "datasetCreationProgressUrl": "https://{baseUrl}/api/v1/datasets/4223df90-0c31-4abf-a0e8-339b5c79f7c7",
    "datasetUploadInfo": {
        "uploadUrl": "https://storage.googleapis.com/...",
        "extraHeaders": "X-Goog-Content-Length-Range:10,534773760"
    }
}

The important parts of the response for the next step are:

datasetUploadInfo, uploadUrl and extraHeaders to craft the HTTP upload request
- This uploadUrl is valid for 1h before it expires
datasetCreationProgressUrl: to poll the dataset creation for progress

Add labelled CSV data

In order to upload the CSV data against the newly created dataset, a curl command like this can be used:

curl -i -XPUT '{uploadUrl}' \
--header 'X-Goog-Content-Length-Range: 10,534773760' \
--header 'Content-Type: text/csv' \
--header 'Authorization: Bearer {token}' \
--data-binary '@/path/to/dataset-anomaly-gas-oil-plant.csv'

Finally, check processing progress for the newly created dataset by polling the datasetCreationProgressUrl:

curl --location 'https://{baseUrl}/api/v1/datasets/4223df90-0c31-4abf-a0e8-339b5c79f7c7' \
--header 'Accept: application/json' \
--header 'Authorization: Bearer {token}'

{
    "datasetKey": "8223df90-0c31-4abf-a0e8-339b5c79f7c7",
    "datasetName": "OilPlantAnomalyV100012",
    "status": "COMPLETED",
    "createdOn": "2025-09-28T18:47:24Z",
    "numberOfBuckets": 10
}

For detailed information on these commands explore our API recipes.

Train a model

Once the dataset has finished processing (reached COMPLETED status), it’s time to train the first model from it.

Dashboard
API

Choose Trainings from the sidebar, then click Create button. Fill the form with the following:

Scaling Factor: set it to 19. This is one of the key training hyperparameters. Full details are present in the Training guide and Hyperparameter tuning guide.
Performance threshold: set it to 0.99. This is another training hyperparameter, representing the desired prediction accuracy (99%)
Dataset: Select the newly created dataset

The training process starts, showing the real time performance of the 4 proprietary training algorithms of MathFi.ai:

It should take no more than 10 minutes for this dataset training to complete and get the initial Champion model:

Training via the API can be started with the following command:

curl --location 'https://{baseUrl}/api/v1/training/datasets/8223df90-0c31-4abf-a0e8-339b5c79f7c7' \
--header 'Content-Type: application/json' \
--header 'Accept: application/json' \
--header 'Authorization: Bearer {token}' \
--data '{
  "performanceThreshold": 0.99,
  "scalingFactor": 19
}'

{
    "trainingJobKey": "3f856c87-ceb2-4988-ad2b-60719741c38b",
    "datasetKey": "8223df90-0c31-4abf-a0e8-339b5c79f7c7",
    "status": "PENDING",
    "trainingJobProgressUrl": "https://{baseUrl}/api/v1/training/3f856c87-ceb2-4988-ad2b-60719741c38b"
}

A training job has been created. Poll the trainingJobProgressUrl for basic progress, or append /progress for a detailed per-algorithm view:

curl --location 'https://{baseUrl}/api/v1/training/3f856c87-ceb2-4988-ad2b-60719741c38b/progress' \
--header 'Accept: application/json' \
--header 'Authorization: Bearer {token}'

{
    "status": "RUNNING",
    "jobs": [
        {
            "trainingJobKey": "07e98a02-d052-4f99-a021-5f6d7e9c3977",
            "algorithm": "BSEV02",
            "status": "COMPLETED",
            "latestPerformance": 0.9922231,
            "recentPerformances": [0.9682258, 0.9402289, 0.98822355, 0.98622376, 0.9922231]
        },
        {
            "trainingJobKey": "3928598b-abf3-427c-9453-802518a78637",
            "algorithm": "BSEV01",
            "status": "COMPLETED",
            "latestPerformance": 0.9900011,
            "recentPerformances": [0.98600155, 0.98800135, 0.98800135, 0.98800135, 0.9900011]
        },
        {
            "trainingJobKey": "8464ce1c-cd47-47d0-ba70-b2f6ae71baf1",
            "algorithm": "BFIF01",
            "status": "RUNNING",
            "latestPerformance": 0.90577775,
            "recentPerformances": [0.89955556, 0.9035556, 0.90555555, 0.90566665, 0.90577775]
        },
        {
            "trainingJobKey": "b3ea8b00-b9d6-4855-b365-e62e29e12d7f",
            "algorithm": "BSIX01",
            "status": "COMPLETED",
            "latestPerformance": 0.9937771,
            "recentPerformances": [0.97977555, 0.97777534, 0.97977555, 0.97977555, 0.9937771]
        }
    ]
}

Eventually, the COMPLETED status should be reached, showing final performances and the created model modelKey:

{
    "trainingJobKey": "3f856c87-ceb2-4988-ad2b-60719741c38b",
    "datasetKey": "4223df90-0c31-4abf-a0e8-339b5c79f7c7",
    "status": "COMPLETED",
    "scalingFactor": 19,
    "targetPerformance": 0.99,
    "achievedPerformance": 0.98727316,
    "trainingPerformance": 0.9937771,
    "testPerformance": 0.9807692,
    "modelKey": "4eea9572-66aa-446e-8b67-328409a56f8f"
}

At this point, a model exists with the following training metrics:

Overall achieved performance: 0.997219
Training performance: 0.9934427
Test performance: 1.0000

Each new training will override this model as long as achievedPerformance is greater than the existing one. This model can now be used to create any number of Predictions on unseen data. Check the Training guide for more details on the process.

Obtain a prediction

Once a model with the desired performance has been successfully created from training, it can be used to run predictions on unseen and unlabelled data (inference) as many times as needed. For the sake of this guide this blind CSV data is being used to try out prediction creation. This is going to be a batch prediction, in which each row represents a single, individual inference.

Dashboard
API

Select Predictions on the sidebar, then Create:

Select the original dataset
Upload the blind CSV

After few moments (depending on size), the prediction completes and the resulting CSV can be downloaded using the Download link:

To create a prediction using the API you’ll need to provide the modelKey to use, the command looks like:

curl --location 'https://{baseUrl}/api/v1/predictions/models/4eea9572-66aa-446e-8b67-328409a56f8f' \
--header 'Content-Type: text/csv' \
--header 'Accept: application/json' \
--header 'Authorization: Bearer {token}' \
--form '_file=@"/path/to/blind-anomaly-gas-oil-plant.csv"'

{
    "predictionKey": "02588573-1d4f-4fcc-8985-f8aaabf09171",
    "status": "PENDING",
    "predictionCreationProgressUrl": "https://{baseUrl}/api/v1/predictions/02588573-1d4f-4fcc-8985-f8aaabf09171"
}

Check progress using predictionCreationProgressUrl:

curl --location 'https://{baseUrl}/api/v1/predictions/02588573-1d4f-4fcc-8985-f8aaabf09171/progress' \
--header 'Accept: application/json' \
--header 'Authorization: {token}'

{
    "status": "COMPLETED"
}

Download the result using the predictionKey and redirecting the output to a CSV file:

curl --location 'https://{baseUrl}/api/v1/predictions/02588573-1d4f-4fcc-8985-f8aaabf09171/download' \
--header 'Content-Type: text/csv' \
--header 'Authorization: {token}' > predictionResult.csv

This is how the prediction results look like:

The IDs on the original blind file have been populated with a predicted label. Overall, this predicted label would be correct in 99% of the cases.

Video walkthrough for Parkinson diagnosis data

This is a video walkthrough outlining the step by step process to analyse Parkinson diagnosis data within MathFi.ai platform.

Where to go from here

This guide has explored the key workflows within MathFi.ai platform and to get a first prediction. In a nutshell, that’s what the platform is about: CSV with data —> training —> prediction on unseen data. For binary predictions, the MathFi.ai Platform assigns a probability that reflects the model’s confidence. A label is chosen if its probability is above (1) or below (0) 0.5. Values closer to 0 or 1 indicate higher certainty. The probability appears as a column in the prediction results file. Next steps:

Get in depth understanding of the dataset creation and training processes via the Training guide
Improve gradually the performance of the trained model via Hyperparameter tuning
Explore real life use cases in the Use cases section
Integrate MathFi.ai in your existing workflow using the API

Guides

Use Cases

Getting started

Overview

Examine sample CSV data

Access the platform

Create your first dataset

Create an empty dataset

Add labelled CSV data

Train a model

Obtain a prediction

Video walkthrough for Parkinson diagnosis data

Where to go from here

Getting Started

Guides

Use Cases

Documentation Index

​Overview

​Examine sample CSV data

​Access the platform

​Create your first dataset

​Create an empty dataset

​Add labelled CSV data

​Train a model

​Obtain a prediction

​Video walkthrough for Parkinson diagnosis data

​Where to go from here

Overview

Examine sample CSV data

Access the platform

Create your first dataset

Create an empty dataset

Add labelled CSV data

Train a model

Obtain a prediction

Video walkthrough for Parkinson diagnosis data

Where to go from here