Get projects into production to deliver on the promise of machine learning

Get the Power to Operationalize Machine Learning – at Scale

Because data scientists are forced to down-sample, move data, and settle for sluggish computation, the promise of predictive analytics at scale with complete accuracy has only been a dream. Until now. Watch this video to learn how Vertica’s in-database machine learning empowers teams to put machine learning into operation – whether your data scientists want to build models in Python, or data analysts and engineers prefer a SQL-based approach.

 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.

Read the White Paper

Vertica Training: Machine Learning & Predictive Analytics

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 six self-paced modules, is designed with both new and experienced users. Improve your machine learning know-how, and put those algorithms to the test!

Learn More

Put the full power of Vertica behind Python data science

The VerticaPy library gives Python scale and performance

Python is the most popular language for machine learning workflows, but it can only work within a computer’s memory. To get the most accurate models, you need to prepare and train on data sets far beyond that limitation.

With VerticaPy, you can use full data sets without downsampling, do data preparation in a fraction of the time, and use every feature that contributes to accuracy with Vertica’s blazing-fast performance.

Learn More

Machine Learning and 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.

Predictive Analytics for Financial Services


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

More on Telecom Data Analytics


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

Powering AdTech with Data Analytics and Machine Learning


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

Learn More

End-to-end Machine Learning Management

From data prep to deployment, Vertica supports the entire machine learning process:
  • Explore and prepare data with functions for normalization, correlation, outlier detection, sampling, imbalanced data processing, and more
  • Join disparate time series data sets and impute missing values
  • Use geospatial functions to cluster, geo-fence, and analyze by location
  • Evaluate model-level statistics including ROC tables and confusion matrices
  • Revert back to previous model iterations using model management and version control

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
  • Train our built-in distributed algorithms on massive data sets rapidly
  • Boost query performance with 10-50x faster results than legacy data warehouses
  • Lower costly I/O with columnar storage and advanced data compression
  • Use subclusters to dedicate compute to machine learning to use clean data warehouse data without bogging down BI


Simple SQL Execution

Democratize predictive analytics with user-friendly, SQL-based machine learning functions:
  • Train, manage and deploy machine learning models using simple SQL calls
  • Increase productivity of machine learning teams with fast data preparation and shorter development cycles
  • Access advanced analytics with SQL: pattern matching, regression, geospatial, time series, clustering, 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
  • Import and put to work PMML for TensorFlow, Python, and more

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


View the Documentation

Linear Regression

Learn More

Logistic Regression

Learn More


Learn More

Engineering Distributed Machine Learning

Are you interested in a technical deep-dive on Vertica’s built-in machine learning? Here is a paper, written by Vertica’s Chief Architect and Engineering leaders, that describes our distributed machine learning subsystem within the Vertica database. It includes Vertica’s current SQL machine learning functionalities that cover a complete data science workflow as well as model management.

Read the Paper

Naive Bayes

Learn More

Support Vector Machines

Learn More

Random Forest

Learn More

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 less coding, higher productivity