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Do the next step and choose your preferred way  to use aivis and gain access!

Gain access to aivis

There are multiple ways how to access and use aivis. Just choose the option that suits you best.

Insights App

Use the aivis Insights app to gain access to aivis fast and simple. The intuitive web UI helps you on every step to get you started smooth and efficient.

  • Basic version is free
  • Fast & simple
  • Pay per use

Project

Your challenges are rather complex and you prefer our aivis experts to take care of it? No problem, just contact us and get to know us in our free initial web meeting.

  • Free initial web meeting
  • Support by aivis experts
  • Fully customized

SDKs (coming soon)

You are used to work with jupyter notebooks or similar? You prefer direct access to aivis from within your code rather than using our web UI? Then get access to our aivis SDKs!

  • Use within your code
  • Python
  • Java

IIoT Platforms

Why import your data if you are already connected to one of our IIoT platform partners. Make use of a direct data connection and start using aivis in no time.

  • Voith OnCumulus
  • OSI (in progress)
  • More coming soon

How to use aivis

Independently of which option you choose to gain access to aivis, the following steps are always the same.

Step 1: Import your data

Simply import you time-series or tabular process data (historian) using the aivis CSV format .

Direct platform connections usually don’t require a data import.

  • Unfiltered & uncleaned

No prefiltering of supposedly irrelevant data or data cleaning required.

  • Unsynchronized

For time-series data, no prior timestamps synchronization is necessary.

  • Any amount

Any dataset size and complexity from few Kilobytes (KB) to Terrabytes (TB) with thousands of signals.

  • With data availability

Optional differentiation of record timestamp and record availability.

Step 2: Start AutoML

Simply define your goal and start aivis. No data science expertise required.

  • Unsupervised

Unsupervised model building and training requiring no model selection or hyperparameter-tuning.

  • Powerful

Native handling of up to thousands of parameters and huge data amounts with results usually within few minutes up to 1 hour at max.

  • Autonomous

Autonomous distinction between relevant and irrelevant data per use case and defined goal.

  • Predefined use cases

Selecting goals based on predefined, real-world use cases.

Step 3: Use the Report

Consume the ease-to-understand report, unveiling root causes, correlations & relationships to better understand and improve your processes.

  • Explainable

Each report can be quickly understood by a domain expert.

  • Actionable

The reports are great for decision-making and deriving direct actions.

  • Reliable

Each report provides precise information about the quality and reliability of the results.

  • Scientific

Each report follows strict scientific guidelines and requirements.

Step 4: Deploy the Model

Deploying the independent, fast and light-weight model within various environments.

  • Transparent

Model accompanied by an report explaining the model.

  • Excellent quality

Continuous benchmarks proof top quality of aivis models.

  • No overfitting

Ground-breaking methods prevents models from overfitting.

  • Durable & reliable

Models stay accurate over a long period of time.

FAQ

You have questions? Maybe our FAQ can clearify things up for you. If not, just use the contact form to ask us directly.

General
When should I use aivis?

You should use aivis if you have any process data that you are allowed to use with external services like aivis. If you think that the data might contain interesting or useful information that can help to improve your processes, you should give it a try.

When sould I not use aivis?

You should not use aivis if your data contains critical content or personal information, and you do not have the right to use it with data analytics services like aivis.

Am I qualified to use aivis?

You are qualified to use aivis, if you have a general understanding of your dataset and are able to convert it into the target format for aivis, e.g. with this useful tool.

In particular, you do not need any data science expertise, since all required expertise is already built into aivis. All the questions you need to answer to start an aivis calculation, can be answered with domain knowledge about your dataset. The same goes for interpreting the results.

Is aivis for process engineers?

Yes! aivis was particulary designed for process engineers, since we believe, that those being closed to the data should be able to work with it as well without the need to talk to data scientists first.

Is aivis for data analysts?

Yes! aivis is an excellent tool for data analysts since it requires almost no data preparation compared to other AutoML services and provides incredible speed and quality and exceeding the complexity others can handle by far.

Is aivis for data scientists?

Yes! aivis is an excellent tool for data analysts since it requires almost no data preparation compared to other AutoML services and provides incredible speed and quality and exceeding the complexity others can handle by far.

Can I try aivis without my own data?

Yes, you have two options: First, you can use datasets offered via the public team in the Insights app. Those datasets can be cloned into your own team to create insights with. Your second option are any published datasets you find e.g. on Kaggle. Those can easily be converted, e.g. with this useful tool, and uploaded to aivis.

Data
What does ‘analyzing historical data’ mean?

You use historical data to learn from past behavior and build and train models for e.g. prediction models, virtual sensors, anomaly detectors, or similar.

What does ‘working on live data’ mean?

A trained and ready-to-go model can be deployed to consume live data streams. By doing so, they can live predict or warn if abnormal behavior was detected.

Which data do I need?

To find hidden dependencies, relations, root causes, etc. you need historic time-series or tabular data. For deployments of models, you need live time-series or tabular data.

What is time-series data?

Time-series data contains data records that usually come from a sensor or similar and are associated with a timestamp, which indicates when the associated value was measured:

timestamp,Sensor_1
2021/08/31 01:32:31,0.23
2021/08/31 01:32:32,0.22
2021/08/31 01:32:33,0.24
2021/08/31 01:32:34,0.25

There might also be a second timestamp indicating when the record became available after the measurement. See also the aivis time-series data specifications.

What is tabular data?

Tabular data entries are usually created when an event or state is logged. It may contain timestamps or be time-independent. One record can contain countless columns, each associated with this event.

speed,torque,pressure,temperature,variable_1
31,0.22,4.1,293,42
34,0.23,4.2,293,42
38,0.24,4.4,293,43
45,0.26,4.5,293,43
349,0.28,4.7,293,43

What is the difference between time-series and tabular data?

Compared to time-series data, where a value is taken, e.g., every second, tabular data is created more event or state-oriented. It might also describe not a point in time but time frames.

See also the aivis time-series & tabular data specifications.

How do I have to prepare my data?

The only required data preparation is the conversion into the aivis CSV format. You might consider using this helpful tool to do so. You do not need to clean or filter your data; no records should be removed or interpolated. You do also not need to synchronize your time-series data since aivis takes care of that, too.

Of course, if you use aivis via one of our IIoT platform partners, there might be no data preparation necessary at all.

How do I import my data?

This depends on the option you choose, how to use aivis. Via an IIoT platform partner, there is no need for an extra data import. The insights app, on the other side, provides a guided wizard to upload your data to an AWS S3 storage connected to the aivis app. Alternatively, you can use your own AWS S3 storage and grant access to aivis.

Will may data stay under my control?

Yes! Every dataset uploaded to aivis is strictly confidential, not saved permanently, not duplicated, remains entirely under your control, and will be used for your purposes only. If required, an NDA can be set into place.

How much data can I use?

The amount of data can be anything between a few kilo bytes (kb) and multiple terra bytes (tb).

Deployment
What is an aivis model?

aivis models are independent, light-weight pieces of software, that are executable and deployable independently of aivis.

Which requirements do aivis models have to run?

aivis models have no special requirements at all. We strive, to make aivis model as small and light-weight as possible, so they can be deployed in any environment – on-edge, on-cloud or on-premise.

Who deploys aivis models?

aivis models are usually deployed by your IT.

Performance
What is the difference between aivis and other AutoML providers?

Working fully automated, aivis requires almost no data preparation and provides clear and actionable results with unseen speed and quality.

Compared to others, it is faster, provides better results, requires significantly less data preparation, is easier to use, and always provides clear and explainable results.

Why is aivis so much better?

The first step aivis performs on the data is always data segmentation: We use Stochastic Differential Geometry to represent the data so that all dependencies and relationships become apparent. Based on that, we use our proprietary universal cluster algorithm to cluster similar geometries. The result is a segmentation of the data where each segment pools similar relationships and importances of the features.

For each segment, we then determine the metric that best describes its dependencies. In a way, this can be interpreted as calculating the hyperparameters, which eliminates the need to manually choosing hyperparameters.

Next, aivis builds and trains a model for each segment using methods from Quantum Field Theory: We interpret the label as the vacuum states of a scalar quantum field and the hyperparameters as vacuum expectation values of background fields (e.g. Higgs field). Finally, we combine the models to a strong ensemble predictor.

In summary, the preceding segmentation brings us two vital benefits: First of all, it is relatively easy to train a model on the segmented data since aivis has already revealed all significant importances and relationships. Secondly, the results are quite transparent and interpretable since it is clear which segment contributes to the current prediction and to which extent it does so.

Other AutoML platforms usually act more like a search engine for suitable approaches to solve a particular problem. They iterate through countless conventional and generic methods and try to pick the most promising one per dataset and problem.

In contrast, aivis already is the perfect match for industrial problems and needs. It provides easily understandable results and clearly identifying root causes. Additionally, superior handling of input data is expressed by the ability to handle the extent of training data that is to be expected in industrial applications, as well as very forgiving requirements in regards to the necessary data structure.

Is aivis using any external libraries?

No. aivis is completely independent of any external libraries.

Is there a limit on how many signals can be handled?

aivis can handle thousands of signals. We have not yet found a limit on the number of signals.

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