Data science

Special for data science teams

Clappform can be used for any kind of machine learning. From classic machine learning models to deep learning, under supervision and without supervision. On this page we explain how Clappform can help your business in dealing with machine learning more efficiently, accurately and effectively.

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Similar difficulties

Forecasts based on machine learning can make your business applications smarter. However, this is still difficult in practice and many data scientist teams experience similar difficulties:

Translation

The translation of complex analyses into daily business operations.

Information

Stakeholders having different interests regarding information needs.

Sharing

Analyses from Data Science teams are difficult to share with other teams.

How does Clappform work?

Data Integration

Clappform is a cloud-based low code platform. It is easy to link different data sources to your own Clappform environment. This is possible in different ways. For instance, you can do this both manually and by means of an automatic coupling. The data is then stored in cloud storage.

For data import there are different types of automatic links:

REST API
SFTP import
Manual data upload: Excel, PDFs or CSV files.

The power of AI

Clappform allows you to directly link developer and scientist tools to your database, enabling you to directly apply your machine learning algorithms to your data.

Our most commonly used tools are:

Jupyter notebooks
R scripts
Azure Machine Learning
Machine Learning CLI
Or other open source frameworks such as: PyTorch, TensorFlow and Scikit-learn and much more.

Use these tools to build, train and learn from your machine learning scripts, through an end-to-end machine learning process.

Share insights

Eventually, users want to share the results of their models with their business through data vizualisation, process automation and process workflow. Clappform allows you to share the right information with the right people in your organisation.

How does this work?

Visualize your data
Link your data to an automatic process workflow.
Set up an end-to-end machine learning automation process.
It is possible to use different calculation sources in each step.
Compare the results of your data to those of other data sets and learn from your machine learning model (benchmarking, deep learning).