Unlock machine learning for the new speed and scale of business



In today’s data-driven world, creating a competitive advantage depends on your ability to transform massive volumes of data into meaningful insights.

Companies that use advanced analytics and machine learning are twice as likely to be top quartile financial performers, and three times more likely to execute effective decisions*.

Built into Vertica’s core — with no need to download and install separate packages — in-database machine learning transforms the way data scientists and analysts across industries interact with data; removing barriers and accelerating time to value on predictive analytics projects.

Announcing Vertica 9.x Training: Predictive Analytics Using Machine Learning

Want to know the latest in machine learning model development, data preparation, regression algorithms, clustering algorithms and more? Check out the newly launched Digital Learning course: Predictive Analytics Using Machine Learning. This 4-hour course, consisting of 6 self-paced modules, is designed with both new and experienced users in mind. 

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Predictive Analytics is changing the way companies across every industry operate, grow and stay competitive

Financial Services

Discover fraud, detect investment opportunities, identify clients with high-risk profiles and determine the probability of an applicant defaulting on a loan.

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Analyze network performance, predict capacity constraints and ensure quality of service delivery to end customers.

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Optimize audience targeting, analyze visitor behavior through A/B and multivariate testing, and predict user engagement patterns.

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Identify product defects, predict equipment maintenance needs, optimize supply chain planning and forecast demand.

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 Vertica In-database Machine Learning

Vertica’s in-database machine learning supports the entire predictive analytics process with massively parallel processing and a familiar SQL interface, allowing data scientists and analysts to embrace the power of Big Data and accelerate business outcomes with no limits and no compromises.

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End-to-end Machine Learning Management

From data prep to deployment, Vertica supports the entire machine learning process:
  • Prepare data with functions for normalization, outlier detection, sampling, imbalanced data processing, missing value imputation and more
  • Create, train and test advanced machine learning models on massive data sets
  • Evaluate model-level statistics including ROC tables and confusion matrices
  • Revert back to previous model iterations using model management and version control features

Massively Parallel Processing (MPP) Architecture

Build and deploy models at Petabyte-scale with extreme speed and performance:
  • Leverage high scalability on clusters with no name node or other single point of failure
  • Boost query performance with 10-50x faster results than legacy data warehouses
  • Lower costly I/O with columnar storage and advanced data compression


Simple SQL Execution

Democratize predictive analytics with user-friendly, SQL-based machine learning functions:
  • Manage and deploy machine learning models using simple SQL calls
  • Empower data analysts to build and operationalize predictive analytics projects
  • Access advanced SQL-based analytics including; pattern matching, geospatial, time series and more
Simple SQL Execution


Familiar Programming Languages


Familiar Programming Languages

Develop user-defined extensions (UDx) with C++, Java, Python or R:
  • Increase the power and flexibility of procedural code by bringing it close to the data
  • Analyze data quickly by executing algorithms in parallel on each node in the cluster
  • Create and deploy C++, Java, Python or R libraries directly in Vertica

Vertica’s built-in machine learning algorithms support classification, clustering and predictive applications with functions for model training, scoring and evaluation.


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Linear Regression

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Logistic Regression

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Naive Bayes

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Support Vector Machines

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Random Forest

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The problem with traditional tools is that the growing volume and velocity of data has increased the complexity of creating and deploying machine learning models – requiring more time and resources to bring predictive analytics projects to fruition.



Scale-out MPP architecture handles massive volumes of data with blazing fast speed



End-to-end process reduces time spent preparing, normalizing and moving data



Familiar SQL interface means no learning new techniques and languages
*“Creating Value through Advanced Analytics.” Bain & Company, February 2015