Seeq Corporation是制造和工业物联网（IIOT）高级分析软件的领导者，宣布将其将机器学习算法集成到SEEQ应用程序中的努力扩展。这些改进将使组织能够将其数据科学投资及其开源和第三方机器学习算法进行操作，以便于一线员工访问。
SEEQ客户包括油气，药品，化学，能源，采矿，食品和饮料以及其他工艺行业的公司。迄今已筹集了超过1亿美元的Seeq投资者，包括Insight Ventures，Saudi Aramco Energy Ventures，Altira Group，Chevron Technology Ventures和Cisco Investments。
Seeq’s strategy for enabling machine learning innovation provides end user access to algorithms from a variety of sources, rather than forcing users to rely on a single machine learning vendor or platform. This addresses the diversity and types of algorithms available to organizations, including:
- 开源算法和其他公共资源。例如，本周Seeq将向GitHub发布两个SEEQ附加组件，包括算法和工作流程，用于相关性和聚类分析，用户可以根据其需求修改和改进它们。
- Customer-developed algorithms in Seeq Data Lab—or machine learning operations platforms such as Microsoft Azure Machine Learning, Amazon SageMaker, Anaconda, and others—as part of data science or digital transformation initiatives.
- Third-party algorithms provided by software vendors, partners, and academic institutions. AWS’s Lookout for Equipment, Microsoft Azure AutoML, BKO Services’ Pump Prediction, and Brigham Young University’s open-source offerings are examples of the emerging marketplace for industry and vertical market specific algorithms.
The Seeq initiative also address the critical ‘last mile’ challenge of scaling and deploying algorithms in manufacturing organization by putting data science innovation in the hands of plant employees in easy-to-use applications: Seeq Workbench for advanced analytics, Organizer for publishing insights, and Seeq Data Lab for ad hoc Python scripting.
This is in addition to Seeq support for the foundational elements of success with machine learning. This includes access to all manufacturing data sources—historian, contextual, and manufacturing applications—for data cleansing and modeling, support for employee collaboration and knowledge capture, quick iteration, and performance-based continuous improvement workflows. “Data science innovation in manufacturing organizations has the potential to deliver a step change in plant sustainability, productivity, and availability metrics,” says Kevin Prouty, VP Industrials, IDC Corporation. “But to land this opportunity, companies must be able to deploy data science innovation to frontline engineers with the expertise, data, and plant context to make decisions on insights provided by these new algorithms.”
使用SEEQ应用程序访问和集成数据科学创新的客户的示例包括一家石油和天然气公司，该公司部署了基于深度学习的排放预测算法，这是一家使用无监督的学习算法的制药公司化学公司使用模式学习来识别过程不稳定性的根本原因并延长周期时间。
“Seeq provides a bridge between data science teams and their algorithms to front-line employees in hundreds of plants around the world,” says Brian Parsonnet, CTO at Seeq Corporation. “Deploying algorithms is now as simple as registering them in Seeq, and then defining which employees have access to each algorithm in their Seeq applications.”
Seeq first shipped machine learning features in 2017 in Seeq Workbench, and then in 2020 introduced Seeq Data Lab for Python scripting and access to any machine learning algorithm. This support for multiple audiences—with point-and-click features for process engineers, low code scripting, and a programming environment for data scientists engaged in feature engineering and data reduction efforts—delivers an end-to-end solution for organizations with all levels of analytics sophistication.
SEEQ可通过全球系统集成商的全球合作伙伴网络获得，该网络除了在北美和欧洲的直接销售组织外，还为SEEQ提供了培训和转售支持。
要了解有关Seeq访问的更多信息seeq.com
