Pentaho Orchestration streamlines the machine learning workflow
Pentaho orchestration capabilities that streamline the entire machine learning workflow and enable teams of data scientists, engineers and analysts to train, tune, test and deploy predictive models. Pentaho’s Data Integration and analytics platform ends the ‘gridlock’ associated with machine learning by enabling smooth team collaboration, maximizing limited data science resources and putting predictive models to work on big data faster – regardless of use case, industry, or language–whether models were built in R, Python, Scala or Weka.With Pentaho’s machine learning orchestration, the process of building and deploying advanced analytics models maximizes efficiency. Most enterprises struggle to put predictive models to work because data professionals often operate in silos and the workflow – from data preparation to updating models – create bottlenecks. Pentaho’s platform enables collaboration and removes bottlenecks in four key areas:
Data and feature engineering – Pentaho helps data scientists and engineers easily prepare and blend traditional sources like ERP, EAM and big data sources like sensors and social media. Pentaho also accelerates the notoriously difficult and costly task of feature engineering by automating data onboarding, data transformation and data validation in an easy-to-use drag and drop environment.
Model training, tuning and testing – Data scientists often apply trial and error to strike the right balance of complexity, performance and accuracy in their models. With integrations for languages like R and Python, and for machine learning packages like Spark MLlib and Weka, Pentaho allows data scientists to seamlessly train, tune, build and test models faster.
Model deployment and operationalization – a completely trained, tuned and tested machine learning model still needs to be deployed. Pentaho allows data professionals to easily embed models developed by the data scientist directly in a data workflow. They can leverage existing data and feature engineering efforts, significantly reducing time-to-deployment. With embeddable APIs, organizations can also include the full power of Pentaho within existing applications.
Update models regularly – According to Ventana Research, less than a third (31%) of organizations use an automated process to update their models. With Pentaho, data engineers and scientists can re-train existing models with new data sets or make feature updates using custom execution steps for R, Python, Spark MLlib and Weka. Pre-built workflows can automatically update models and archive existing ones.
Hitachi Rail uses Pentaho with Hitachi’s Hyper Scale-Out Platform to fulfil its pioneering “Trains-as-a-Service” concept, applying advanced IoT technology in three event horizons: real-time (monitoring, fault alerting), medium-term (predictive maintenance) and long-term (big data trend analysis). With each train carrying thousands of sensors generating huge amounts of data per day, the project’s data engineers and scientists face many challenges associated with big data and machine learning. Although the project is not yet operational, Pentaho is already helping to deliver productivity improvements across the business.
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More Information on Predictive Analysis Process
For more information of predictive analytics process, please review the overview of each components in the predictive analytics process: data collection (data mining), data analysis, statistical analysis, predictive modeling and predictive model deployment.