Low-Code Enablement of Data Comprehension For Complex Datasets,
which should be the first step in ANY Ai/ML software development process

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Ai systems tend to operate in the darkness of black boxes. Input data is transformed into decisions without much human-readable justification. The journey from data to insights requires a crucial data comprehension step, so the direction of an Ai/ML model is fully explainable. This is particularly challenging for high dimensional, multivariate datasets. Omitting this step can produce a result that may not be actionable, causing the Ai project to dead-end.

Ai-Rover is a low-code suite of Ai algorithms that is the full implementation of an explainable AI algorithm on a virtualized notebook, in concert with Zeblok's GPU-powered data lake, Edge connectivity and other components.  Ai-Rover discovers and visually explains patterns in complex datasets as well as the causal relationships underlying those patterns, supporting data analysts in the construction of trust-able decision-making AI models. Zeblok's accelerated Ai-Data Lake enables rapid querying of multiple disparate data sources. Ai-Rover enables the crucial data comprehension step to properly target the direction of an Ai project.

Say goodbye to tedious data exploration and instead gain quick access to the hidden information in your datasets within an easy to understand low-code visual interface that enables users to intuitively derive subpopulations of data attributes, leading to better predictive insights.


About Ai-WorkStation &

The Zeblok Ai-WorkStation is a one stop shop. Whether you are looking to start a multi-user data science project using various open-source AI frameworks, or you are looking for a high-performance computing (HPC) environment to distribute your jobs across a large cluster of computational power, we have you covered. 

  • Powerful Machine Learning / Artificial Intelligence Notebook

  • Model Training, with ML/DL Pipelines

  • Out-of-the-box CUDA Optimization on GPU for AI- Frameworks

  • Supports popular data science language bindings such as R, Scala, Docker and Python

  • We embrace open-source frameworks and tools

About Ai-Data Lake

The Zeblok Ai-Data Lake: A high-performance data store that allows you to import, filter, and instantly analyze objects. Our solution is designed for performance, scales up with your data, and can provide industry-grade SSL security and data-redundancy for high availability of data at SSD speeds. 

The Zeblok Ai-Data Lake  handles the tasks of data pipe-lining, analysis, and propagation so that you can focus on what matters most. Easily automate your imports, with large files using various methods, such as the Zeblok Data Browser Portal, Zeblok magic commands, inbuilt S3 REST API, Ai-Data Lake Desktop App, and HTTP source. Whether you are ingesting images or video data files coming from IoT sensors at Edge locations through 4G/5G low latency network or uploading large csv files, we've got you covered.

How Ai-Rover™ For High Dimensional, Multivariate  Data Works

A common headache for data analysts is how to pick a manageable set of meaningful attributes and features from a list of hundreds or more. Our new approach to data analysis puts an end to this guessing game. Our Explainable AI software discovers and visually explains interesting patterns and causal relations in complex data, supporting data analysts in the construction of trustable decision-making Ai models.

Pattern Miner

Pattern Miner automatically decomposes the data into a manageable set of statistically robust data patterns, each of which can be concisely described with just a few attributes. 

Causal Analyst

 Visual Causal Analyst distills correlations that exist among these patterns into a terse set of causal relationships. This process eliminates all spurious correlations and makes it easy to discern true interactions within the data. This is illustrated in Step 3 below.

Data Context Map

The Data Context Map visualizes both patterns and relationships in an integrated and intuitive fashion. Analysis can freely interact with this visual layout to explore new patterns and relationships, creating new subpopulations. Simplifies data analysis by combating information overload.

How It All Comes Together

Step 1: Data Ingestion 

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Enterprise users login through a web browser and access their pre-configured Ai-Rover™ Notebook, which includes Zeblok's Ai-WorkStation and our exclusive Explainable Ai algorithm.

Users then ingest their data into the Ai-Data Lake using their choice of:

  • Data browser portal

  • ZBL magic commands


  • Data Lake Desktop App

  • HTTP Source


Step 2: Start the Automated Analysis and See Data Patterns in the Context of Their Relevant Attributes

Our software decomposes the high-dimensional input data into a set of independent data patterns.  Each pattern consists of data items that behave similarly in terms of a given target variable and are succinctly defined by just a small set of attributes, making them easy to understand. This analysis typically only takes a few minutes, or less. Once analyzed, the patterns and their features can then be visually explored in a dedicated pattern browser interface.

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After the analyst has selected data patterns that appear interesting, the system produces an easy-to-read visual layout of these patterns in the context of their relevant attributes.  In the image below, the green and red disks are patterns with unusually high or low values in the target attribute (disk radius encodes pattern size) and the triangles nearby are the attributes that define them. 

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Step 3: Add the Causal Interactions Among the Data Patterns

Many data analytics packages compute correlations among attributes to indicate relationships. But correlation does not necessarily imply causation. Our causal inference engine establishes true causal relationships, which imply a direction. For example, smoking causes cancer but not vice versa. Our visualization interface conveys the intricate web of causal relationships in a single intuitive map.

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Step 4: Use the Identified Patterns & Relationships in Your Application

Our software is available as a Jupyter Notebook plug-in and can be easily integrated into an existing data analyst’s workflow. Simply select one (or more) data pattern in the visualization interface and export it as a data frame into your downstream application, such as training a neural network, decision tree, etc. 

More Analytical Tools

See How Patterns Change Over Time to Aid in Predictive Analysis,

Form Hypotheses Using Our Pattern, and Feature Engineering Suite

Patterns often change over time. Our interactive visualization interface allows analysts to observe these changes and gain valuable insights for predictive analysis. This interface is further boosted into our temporal causal analysis engine. 

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Use our interactive pattern sculpting interface to view, edit and specify new patterns of interest. Then quickly test the effectiveness of new attributes, and features derived from them by examining their ability to form new patterns and causal relationships. 

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