实施数据驱动型和基于人工智能的工作流程
数据已变得无处不在。然而，许多科学和工程企业仍然难以有效利用他们所掌握的数据。团队使用不同的工具和流程来访问数据、清理数据、建立模型并交付结果，但这些结果通常缺乏所需的领域深度来推动创新。在分析科学和工程数据并提供深度见解时，如果使用这种不连贯且往往过于通用的方法，会降低结果可信度、阻碍进展和协作。为了充分利用潜在的数据科学信息，企业需要一种端到端的方法来利用整个科学和工程企业中的数据。
利用 BIOVIA Pipeline Pilot 及其系列产品，用户可在 Pipeline Pilot 中创作数据管道或协议，以提供一体化的数据驱动型解决方案，并可能将其与其他 BIOVIA 应用相结合，以增强开箱即用的功能。
主要优势：
- 普及数据 – 为所有人最大限度地发挥人工智能和机器学习的价值。
- 利用科学知识和专业技术 – 捕获最佳实践标准实践并将其转化为可分发、模块化、可共享的协议。
- 部署数据驱动型研发操作 – 帮助您的团队更智能地工作，而不是更辛苦地工作。
- 支持端到端数据科学工作流程 – 随时随地部署服务。所有这些都在一个工作流程中完成。
BIOVIA Pipeline Pilot（特别是其系列产品）提供了开箱即用的纵向和横向特定领域功能，支持用户解决从化学信息学到序列分析、从图像分析到文档和文本搜索、从实验室信息学到机器学习和分析等各方面的挑战。
x64 | File Size: 10.03 GB
Description
Leveraging BIOVIA Pipeline Pilot and its Collections, users author data pipelines, or protocols in Pipeline Pilot, to deliver integrated, data-driven solutions, and potentially combine them with other BIOVIA applications to augment out-of-the-box capabilities.
Operationalize Data-driven and AI-based Workflows
Data has become ubiquitous. However, many scientific and engineering-based organizations still struggle to effectively utilize the data at their disposal. Teams use different tools and processes to access data, clean it, model it, and deliver results, but these results often lack the domain depth needed to drive innovation. This disjointed, and often too generic approach, to analyze scientific and engineering data and deliver insights lowers trust in results, obstructs progress, and stifles collaboration. To fully benefit from the potential data science offers, organizations need an end-to-end approach to leverage their data across the science and engineering enterprise.
Key Benefits
-Democratize Data – Maximize the value of AI and machine learning for everyone.
-Capitalize Scientific Knowledge and Know-How – Capture best practices standard practices as distributable, modular, shareable protocols.
-Deploy Data-driven R&D Operations – Help your teams work smarter, not harder.
-Support End-to-End Data Science Workflows – Deploy services where they are needed, when they are needed. All in a single workflow.
Purpose-built Solutions for Science and Engineering
Scientists and engineers face different challenges. BIOVIA Pipeline Pilot, and specifically its Collections, offer vertical and horizontal domain-specific capabilities out-of-the-box, supporting users to solve their challenges from cheminformatics to sequence analysis, from image analytics to document and text searching, from lab informatics to machine learning and analytics. Explore our Collections below.
Simplify Your Data Science Workflow
Data comes in all shapes and sizes, yet unlocking actionable insight efficiently requires deep knowledge of data science techniques. BIOVIA Pipeline Pilot Machine Learning and Analytics Collection provides a comprehensive set of machine learning and data modeling capabilities to streamline your data science initiatives.
Analyze data, train and retrain models, and deploy your automated solution to useful enterprise applications.
Developing machine learning solutions often requires complex software architectures and deep statistical knowledge. With BIOVIA Pipeline Pilot Analytics and Machine Learning Collection, developers and end users alike can incorporate the latest machine learning techniques to their workflows with just a few clicks. No coding required.
Key capabilities
-Merge, join, characterize, and clean your data sets
-Apply any of 15+ machine learning (ML) methods to your scientific and engineering data
-Use R-based ML methods such as support vector machines, neural networks, and XGBoost without writing R scripts
-Use Python ML libraries including scikit-learn and TensorFlow
-Rapidly apply statistical analyses
-Use regression and classification model evaluation viewers to assess and compare model test set performance
-Build fast, scalable Bayesian classification models
-Use the GFA method’s genetic algorithm for variable selection and building regression ensemble models
-Build accurate, easy-to-use RP Forest regression and classification models
-Curate model performance
-Deploy model applicability domain (MAD) methods and cross-validation
-Employ the ML framework for cross-validation, hyperparameter tuning, and variable importance assessment for any type of model
-Work flexibly
-Support for 3rd party statistic platforms and tools such as Jupyter Notebook, R, JMP and SAS
-Read in discipline-specific data
-Purpose-built to support various numerical, chemical, biological, textual, and image data types
-Use built-in applicability domain measures and error models to assess sample-specific prediction confidence
-Optimize predictions
-Train multiple trial models in parallel to identify top performers or combine multiple models into a single ensemble model
-Simplify multi-objective optimization
-Employ methods such as Pareto optimization to multi-objective optimization problems
-Visualize results in workflow
-Generate interactive reports with ROC plots, enrichment plots and other visualization techniques
-Perform exploratory analysis, including PCA, clustering, and multi-dimensional data visualization
System Requirements
OS:Windows 10/Windows Server 2019/Windows Server 2016
CPU:Intel-compatible x86_64 architecture
RAM:4 GB per core
Space:70 to 80 GB
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