Accurate & Fast
Get the best results and retention rate fastly
We can manage variables and millions of data with powerful learning dataset
We can ease to use your millions of data just a few clicks
Data Sources
Before the data uploads just select the category of problem you want to choose. Upload the dataset you want to train a model on. These dataset sources are; SQL, Cloud Storage and NoSQL
Core Features

Trendify AutoML applies cleaning, determining properties and transforming to your data. Also AutoML chooses the most suitable model and performs hyper-parameter optimization for you.


Scale your business insight. Thanks to Trendify AutoML, you could just as easily see statistical analysis, dashboards, chart and graphs for your dataset.

You can use these insights to predict customer churn and customer segmentation among millions of other use cases.


You can access your business insights with API, Python Library and Web Application.

Export Data

You can export your business insights with Excel or CSV file. Also Import your SQL Databases or and Cloud Storage.

Our Competitive Advantage to Other Solutions

Deep Clustering Algorithm

If Segmented Data does not achieve sufficient depth and distribution ; AutoML Segments again thus, obtaining the segmentation result with the minumum variance

Hyperparameter Optimization

In order for the models to produce better results, hyperparameter optimization has been differentiated and made faster.

Auto Model Selection

Automatically selects the model that you can get the highest performance according to your data.

No Unbalancing Data

We make the unbalanced data suitable for distribution with the most appropriate algorithm.

AutoML Pipeline

Discover Need
Determine the data architecture
Determine the data models
Creating a big data environment or EDW
Collect your data
Run Trendify AutoML
Monitoring Results and Integrate your business

Frequently Asked Questions

Trendify is suitable for use in many different environments. However, we recommend using it in a cloud environment.

A stronger baseline we will use is an approach that in addition to selecting the learner, also sets its hyperparameters optimally from a predefined set. More precisely, this baseline performs an exhaustive search over a grid of hyperparameter settings for each of the base learners, discretizing numeric parameters into three points.

AutoML could help data scientists within a company to save time and spend it more on more important stuff under normal conditions, the work of a data scientist and data analyst, which takes weeks, maybe months, Trendify AutoML can produce solutions at the level of days.