BIOVIA Pipeline Pilot
Versatile platform for automating and optimizing scientific data workflows.
Empower science-based industries with automated data workflows, accelerating research, and fostering efficient collaboration with BIOVIA Pipeline Pilot
Data, diverse in forms, demands profound knowledge of data science for efficient insight extraction. BIOVIA Pipeline Pilot streamlines complex data workflows with a graphically based, code-optional environment for science, engineering, and AI/Machine Learning (ML), enabling rapid development and easy interpretation. Enhance efficiency and decision-making by automating scientific data workflows and foster streamlining processes and integration.
What can you do with BIOVIA Pipeline Pilot
Scalable Data Science Framework
Access Data
Automate Data Preparation
Analyse Data
Visualization and Reporting
Deploy Protocols
Streamline Model Creation (AI, Machine Learning, Statistics)
Integration
BIOVIA Pipeline Pilot Feature Overview
- Source data when it’s needed, no migration required
- Read/write data from internal databases, Hadoop warehouses, cloud applications, flat files and more
- Connect to external data sources via an extensive library of APIs
- Out-of-the-box connections to various 3rd party databases
- Automate data preparation and handling
- Clean and blend data
- Schedule tasks
- Share complete protocols to minimize the time spent setting up analysis
- Operationalize discipline-specific capabilities and analyses
- Democratize powerful machine learning and predictive analytics
- Deploy protocols as web services, APIs or deploy to our built-in web interface (Web Port)
- Create and combine interactive charts and visuals for key stakeholder
Explore the “Creating a Scalable Framework for Data Science” whitepaper for valuable perspectives and optimal approaches in the following areas:
- Enhancing the utilization of current resources and enticing new talent.
- Understanding how a data science strategy can enhance R&D efficiency, reduce expenses, and accelerate time to market.
- Democratize scientifically-aware workflows
- Aggregate and get immediate access to volumes of disparate research data locked in silos
- Automate the scientific data analysis and rapidly explore, visualize and report results
- Out-of-the-box machine learning architectures and integration with Python, R, and more for custom scripts
- Cheminformatics, Bioinformatics, Imaging, Text, Data Modeling
- Provides powerful dynamic visualization
- Integrates property calculations properties, predictive models and analytics within the same simple UI
- Provides projects views and data that can be shared and worked on jointly by team members
- Insight for Excel provides the core capabilities of BIOVIA Insight within the familiar Microsoft Excel interface
- Transforms scientific data into actionable insight
- Offers a suite of applications to find, share, and reuse enterprise knowledge
- Builds up relationships between disparate bodies of information
- Provides easy access to scientific enterprise content
- Allows users to identify subject matter experts to drive collaboration
- Creates a dynamic, intelligently managed knowledge network
Simplify Enterprise Science and Engineering
Data is now ubiquitous in our lives, but many scientific and engineering organizations face challenges in realizing its full potential. They grapple with various tools and methods for accessing, cleaning, modeling, and sharing data, resulting in outcomes that often lack the technical depth needed for true innovation. This fragmented and sometimes overly generic approach to data science hinders effective solutions, collaboration, and trust in results.
To fully leverage the power of data science, organizations need to embrace a comprehensive, end-to-end strategy for utilizing their data resources in all their scientific and engineering endeavors.
Pipeline Pilot simplifies data scientists’ work by streamlining model training with a few clicks, facilitating performance comparisons across different models, and preserving trained models for future use. It also offers advanced users the flexibility to integrate custom scripts from Python, Perl, or R for broader applicability within the organization.
Importantly, Pipeline Pilot maintains transparency by associating each model with a defined protocol, revealing insights into data sources, cleaning processes, and the responsible model. This transparency builds confidence in predictions and empowers end users to enhance their scientific efforts with the latest machine learning techniques.
In essence, Pipeline Pilot aims to unlock the full potential of AI and machine learning, making their benefits accessible to everyone.
