Data Labeling Webinar

Watch this recorded webinar on time series labeling and learn:

  • Why time series labeling is a key success factor for AI
  • Approaches to data labeling based on real-life use cases
  • Examples: Manual labeling of sensor data (incl. Python Integration), Automated labeling, Auditing labelled data
  • Relating data labeling and visual exploration

Moreover, we invite you to download Visplore Free and try labeling your time series data.

Try it in Visplore Free

Watch recorded webinar and see Visplore in action

Interactive data labeling
Boost your AI models by better data labels

All Visplore versions feature powerful interactive tools for data labeling. These tools will help you to select and label time series data without worrying about complex parameters.

  • Select and label large amounts of multivariate data
  • Define labels based on integrated formulas or scripts
  • Mark up parts of the data directly in the visualization
  • Import categorical data attributes for auditing or fine-tuning labels
  • Compare multiple labeling results in depth
  • Share label definitions easily with colleagues
  • Start labeling directly from Python, Matlab or R
  • Use pattern search to label all occurrences of selected patterns efficiently

Different existing data labels can easily be audited and cleared at any time
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Labeling rules can be applied to new data and patterns can easily be searched within seconds.
Click Image to enlarge

Labeling data?
No problem with Visplore

The quality of input data is one of the most important factors in AI, machine learning, and data mining.

Building high-quality predictive models requires data which truly and comprehensively reflects the phenomenon that should be modelled or mined. AI algorithms typically do not detect implausible data on their own, but rely on the quality of data preselection and the quality of their labels. For predictive maintenance, for example, it may be necessary to label data from industrial processes as “good”, “critical”, “anomalous”, and so on, before building classification models. In practice, however, selection and labeling of data can be very time-consuming, difficult to address by scripted logic, and may require many iterations with domain experts.

Visplore reduces those challenges to a minimum – and saves you valuable time while improving the labeling results.

Working with data labels

Data labeling is a critical step in building high-quality AI models. The purpose of data labeling is to make information about distinguished classes (such as machine states) explicit. This information is important to train an AI model for the automatic distinction of states. AI experts emphasize that optimizing the input data is at least as powerful for optimizing the prediction quality as fine-tuning model parameters. A special case of labeling is the selection of appropriate input data. For example, unsuitable input data such as interruptions of the machine operation should be excluded before training any model on the data.

Why our customers
prefer Visplore

Perfect for dirty sensor data. With Visplore, we give customers more insights for the same money.

When we need it fast, we use Visplore.

Visplore enabled us to gain significant new insights from massive data about the impact of production process parameters on product quality.

Thanks to its ease of use and high performance even with millions of measurements, Visplore has established itself as our standard tool for quality management in plants from China to South America.

Checking and cleaning data from energy systems used to be very time-consuming and kept us from our core tasks. With Visplore, data preparation has become much faster, and the quality of the resulting data has improved.

The good usability and the comprehensive visualizations enabled us to gain a deeper understanding of routinely collected data and their relationships. For example, this increased the quality of forecast models.

Visplore is recognised and in use as a central strategic software for data analytics throughout the company.

By analyzing process data from a large foundry with Visplore, complex dependencies were detected that we didn’t find with other software. As a result, our customer’s heat energy was reduced by around 5% and mold wear by around 4%.

For our thermodynamical simulations, Visplore helps us to identify suitable data, eliminate outliers, and analyze physical processes very effectively.

The visualization is fast and simple enough for live collaboration with the customers, on site. By this, Visplore supports a thorough understanding of correlations, outliers, and anomalies.

Visplore makes our analysis of sustainable energy systems much more efficient.
In addition to the performance for large data, its integration with scripting environments is a big plus.

Visplore allows for a much easier investigation of relevant patterns and structures. Our downstream analysis becomes more efficient and we gain more confidence in the results.

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mondi
GF
Verbund
APG
AIT - Austrian Institute of Technology

Label your data like never before.