Machine Learning
Reaching the competitive advantage of becoming a data-driven company depends on putting meaningful insights to work no matter how much data you have.
In-database machine learning is built into Vertica’s core. From data exploration through data enhancement, feature filtering through algorithm training and management – the entire workflow can be done with parallel distributed performance while keeping data secure and avoiding the costs of data movement. Increase accuracy by training on full data sets. Turn predictive analytics projects from experiments to profit boosters faster with SQL productivity and an easy path to production.
Vertica 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.
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:
- 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
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.
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.
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
