Machine Learning
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.
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.
Telecommunications
Analyze network performance, predict capacity constraints and ensure quality of service delivery to end customers.
AdTech
Optimize audience targeting, analyze visitor behavior through A/B and multivariate testing, and predict user engagement patterns.
Manufacturing
Identify product defects, predict equipment maintenance needs, optimize supply chain planning and forecast demand.
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.
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
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.
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.
Vertica’s in-database machine learning is designed to address common barriers preventing predictive analytics projects from getting off the ground:
Barrier 1: Legacy tools can’t handle the scale of today’s data volumes
- Result: data scientists are forced to down sample, compromising the accuracy of machine learning models
Barrier 2: Multiple platforms are required for data storage, SQL analytics, data preparation, and statistical analysis
- Result: Moving data across platforms takes up valuable time and adds cost to predictive analytics projects
Barrier 3: Resource constraints and skills gaps prevent companies from taking full advantage of their data
- Result: Missed opportunities for new revenue models, increased operational efficiencies, personalized customer service and more
What we're seeing today is the ability that we now have to query billions of data points and summarize those billions of data points, or even execute machine learning algorithms on those data points directly in the database, to an extent which was simply impossible with any other technology we've seen.
- - Michael Schuldenfrei, Corporate Technology Fellow, Optimal Plus
Scalability
Scale-out MPP architecture handles massive volumes of data with blazing fast speed
Speed
End-to-end process reduces time spent preparing, normalizing and moving data
Simplicity
