使用自动调整功能根据您的建模目标自动找到最佳的机器学习超参数或属性集。利用内置的优化求解器在最短的时间内建立优化模型。SAS 机器学习的自动化功能通过让 SAS 为您处理数字问题，让您花费更多时间收集有意义的洞察。
利用面向 SAS® 和 Python 程序员的强大的协作编码环境。
除了使用 SAS 语言外，您还可以使用 Python 从 Jupyter Notebook 访问 SAS 算法。SAS 机器学习为生成模型、评估输出和获取洞察提供统一的体验。您可以通过 SAS® Studio 中的 SAS 编程访问相同的机器学习算法和数据。
探索 SAS® 机器学习和其他产品的更多信息
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- White Paper
- Analyst Report Bloor InBrief: SAS Event Stream ProcessingLearn why Bloor considers SAS a major contender in the market for streaming analytics platforms due to SAS’ differentiating analytic capabilities, notable performance and continuous improvement of in-stream models using machine learning.
- White Paper Heavy Reading: Advanced Predictive Network AnalyticsHeavy Reading reveals how predictive analytics gives service providers real-time visibility into their networks – boosting revenue potential and ensuring more satisfied customers, better targeted marketing, fine-tuned capacity planning and top-notch service assurance.
Data Mining From A to Z
This paper shows how you can use predictive analytics and data mining to reveal new insights from data, as well as get all the pieces and parts moving together for maximum value.
- E-Book Discovering new possibilities with SAS® Viya® on Microsoft AzureOrganizations need to quickly solve complex problems and make decisions. That’s exactly what SAS Viya on Microsoft Azure was designed to do. You get powerful, easy to use analytics that incorporate open source and SAS algorithms on a scalable, easy to deploy environment.
- Article A guide to machine learning algorithms and their applicationsDo you know the difference between supervised and unsupervised learning? How about the difference between decision trees and forests? Or when to use a support vector algorithm? Get all the answers here.
Artificial Intelligence for Executives
This paper outlines the SAS approach to AI and explains key concepts. It also provides process and implementation tips if you are considering adding AI technologies to your business and analytical strategies.
- Article Fraud detection and machine learning: What you need to knowMachine learning and fraud analytics are critical components of a fraud detection toolkit. Here’s what you’ll need to get started – from integrating supervised and unsupervised machine learning in operations to maintaining customer service while defending against fraud.
- White Paper Delivering SAS® Expertise to Your DoorSAS Remote Managed Services fulfills customers’ application management needs when they require or prefer that the solution and data remain on-site. Learn how this offering can help your organization deliver insights from your analytics program.
- Analyst Report IDC MarketScape: Worldwide Advanced Machine Learning Platforms 2020 Vendor AssessmentSAS is a Leader in the IDC MarketScape: Worldwide Advanced Machine Learning Platforms 2020 Vendor Assessment.
- Article 5 ways to measure beehive health with analytics and hive-streaming dataThis analytical approach to understanding bee hive health can automatically alert beekeepers to changes in hive weights, temperatures, flight activity and more.
- Article Analytics: A must-have tool for leading the fight on prescription and illicit drug addictionStates and MFCUs now have the analytics tools they need to change the trajectory of the opioid crisis by analyzing data and predicting trouble spots – whether in patients, prescribers, distributors or manufacturers. The OIG Toolkit with free SAS® programming code makes that possible.
- Video How machine learning can provide customers with more relevant offersIn this video, industry influencer Daniel Newman offers his perspective on ways machine learning can be used to enhance customer experiences.
- Article Five AI TechnologiesDo you know the difference between artificial intelligence and machine learning? And can you explain why computer vision is an AI technology? Find out in this short explainer.
- Article Detect and prevent banking application fraudSince credit fraud often starts with a falsified application, it makes sense to have analytics-driven tools in place to detect fraud from the earliest point and across the life of the account.
- Article Discover a secret resource for working with cloud providersAre you overwhelmed by the hundreds of options and offers in the cloud? Are you finding it hard to select the best cloud services from the different cloud providers? Why not ask a neutral and informed third party for help?
Hosted Managed Services for SAS® Technology
SAS provides hosted managed services designed to provide faster and easier deployment options for SAS technologies. This white paper reviews the fundamentals of this capability.
Analytics Across the Student Life Cycle
Why higher education institutions need to implement data integration, data visualization and analytics solutions. It then shares best practices for applying these technologies across the student life cycle and examples of how schools are using these technologies to improve decision making.
- White Paper Advanced Analytics: Moving Toward AI, Machine Learning, and Natural Language Processingadvanced analytics, artificial intelligence, machine learning, natural language processing
- Analyst Report Gartner positions SAS as a Leader in the Magic Quadrant for Data Science and Machine Learning Platforms, Q1 2021Gartner positions SAS as a Leader in the Magic Quadrant for Data Science and Machine Learning Platforms for the eighth consecutive year.
- Article How to drill a better hole with analyticsFrom drilling holes to preventing health care fraud, learn about some of the new technologies SAS has patented with IoT and machine learning technologies.
- Article Exploring the sun with big dataResearchers working for NASA are using automatic, exploratory and visual analysis of big data to help understand the mysteries of our universe.
How to Do Deep Learning With SAS®
Get an introduction to deep learning techniques and applications, and learn how SAS supports the creation of deep neural network models.
BI, Analytics, and the Cloud: Strategies for Business Agility
This TDWI Research report examines organizations’ experiences with, and plans for, cloud BI and analytics, new cloud models, and what should be considered when moving to the cloud.