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비정형 텍스트 데이터는 인류가 생성하는 가장 큰 데이터입니다. 더 나은 비즈니스 결정을 내리고, 제품 전략을 알리고, 고객 경험 개선에 도움이 되는 유용한 정보가 바로 이 데이터에 포함되어 있습니다. 비정형 텍스트 데이터의 잠재력을 최대한 활용해야 하는 이유입니다. 본 시리즈에서는 텍스트 데이터에서 인사이트를 얻는 주요 방법과 이를 위한 SAS 솔루션을 살펴봅니다. 전
I believe the most important part of the analytics lifecycle is defining the business question being asked.
Most New Yorkers like to fantasize about owning and living in a classic New York City brownstone, complete with high ceilings, large windows and a front stoop. You can picture yourself sitting there alongside the iron railing with a cup of coffee while you watch the city wake up and
Conversational AI can offer a way to provide that always-on 24/7, fast, convenient experience that can go anywhere (phone, computer smart speakers, even your car). It can provide a human-like experience through real-time, personalized interaction with AI running in the background. This technology is being applied across many industries for a variety of use cases (both customer-facing and for internal use).
지난 딥러닝 시리즈에서는 SAS Visual Data Mining and Machine Learning을 활용한 딥 러닝 모델 생성에 대한 내용 중 <기본 심층 신경망(DNN) 모델 아키텍처와 배치 정규화를 사용한 DNN 모델 구축>에 대해 소개해 드렸습니다. 이번 시리즈에서는 딥 러닝 성능을 개선할 수 있는 하이퍼파라미터를 조정에 대해 소개해 드립니다. 일정 기간에 걸쳐 성능이 향상되고
딥 러닝은 인공 지능과 함께 유비쿼터스가 된 머신 러닝의 한 영역입니다. 딥 러닝 모델의 복잡하고 뇌와 유사한 구조는 대량의 데이터에서 복잡한 패턴을 찾는 데 사용됩니다. 이러한 모델은 일반 지도 학습 모델, 시계열, 음성 인식, 객체 탐지 및 분류, 감성 분석의 성능을 크게 향상시켰습니다. 사전 정의된 방정식을 실행하도록 데이터를 구성하는 대신
The first principle of analytics is about bringing the right analytics technology to the right place at the right time. Whether your data are on-premises, in the cloud, or at the edges of the network – analytics needs to be there with it. Being true to this principle means we
SAS' Véronique Van Vlasselaer reveals why managing model performance is as important as putting them into production.
Discovery is an important part of setting up your analysis for success – essentially it prevents you from plunging into a haystack to try to find that elusive needle, and rather, helps you organize the haystack into neater, compact organized bales that you can navigate with ease. Proper discovery can help you more efficiently find patterns in your data set.
What does the AI enterprise of the future look like? That’s a tough question that I’ve been asked to consider, along with a distinguished panel at Valley ML AI Expo 2020. The title of the panel is, “Life, the Universe and the AI Enterprise of the Future.” Based on an initial chat with panel chair Gautam Khera, I’ve written up some possible topics we’ll be covering on the panel. Consider
Depending on who you talk to, you'll get varying definitions and opinions regarding demand sensing. Anything from sensing short-range replenishment based on sales orders, to the manual blending of point-of-sales (POS) data and shipments. But a key component for retailers and CPG companies is accurately forecasting short-term consumer demand to
A evolução do analytics e da ciência de dados gera constantes atualizações e transformações nas plataformas de análises. Este artigo tem o propósito de apresentar como o SAS tem acompanhado essa evolução. Ambiente Integrado: uma única plataforma, diversas tarefas O SAS oferece recursos que permitem acessar, explorar, transformar, analisar e
La ciencia de datos nos está ayudando a entender y, sobre todo, a proponer soluciones viables para los problemas más complejos que como sociedad e industria enfrentamos actualmente. Sin importar el perfil o sector de las organizaciones, las áreas de TI han encontrado en la ciencia de datos una alternativa
Everything happens somewhere, and much of our customer data includes location information. Websites include x, y coordinates in semi-structured click streams, and the mobile apps your prospects depend on frequently support device location to provide a personalized, targeted experience. As my SAS peer Robby Powell said: "Human brains are hardwired
When the automatic time-series techniques can't produce adequately forecasts, a tool should be equipped with multiple machine learning techniques.
According to the SAS Experience 2030 global study, by the year 2030 67% of in-person customer engagements (think sales assistance and information queries) will be completed by smart machines rather than humans. And while it may seem a bit ironic, the most personalized customer experiences could involve no people at
The Text Investigation Framework is a flexible solution for addressing text challenges across several domains. It was designed to create a process for turning unstructured text data into a decisioning system.
One of the wonderful aspects about my client-facing role at SAS is the breadth of audiences that I get to work with. No matter where you fall on this list: Data engineer. Business or marketing analyst. Citizen data scientist. Data scientist. Statistician. Executive. One topic is certain: We all love
We will combine three separate SAS Viya capabilities to create an application that can manage multiple models, interpret model outputs, and replace the production model if necessary
Everyone knows that SAS has been helping programmers and coders build complex machine learning models and solve complex business problems for many years, but did you know that you can also now build machines learning models without a single line of code using SAS Viya? SAS has been helping programmers
Over the last 20+ years, global society has adopted digital devices at scale, and consumer interaction behaviors continually evolve and mature. As analysts, we're uniquely positioned to notice worldwide trends, country-specific nuances and localized market behaviors that can have significant impact on our brand's business goals. This global scope is
최근 SAS는 클라우드에서 AI와 분석의 미래를 더욱 구체화하기 위해 마이크로소프트와 새로운 전략적 파트너십을 체결했습니다. 이번 파트너십으로 SAS 고객은 클라우드 환경에서 훨씬 수월하게 분석을 수행할 수 있습니다. 또한 SAS의 AI 및 분석 솔루션과 마이크로소프트 애저(Azure)의 긴밀한 통합으로 수백만 명의 애저 고객은 SAS 기술을 편리하게 활용하여 분석 성능을 한층 개선할 수 있습니다.
Os algoritmos de mineração de dados podem ser divididos em 4 grupos, a saber: aprendizado supervisionado, aprendizado não-supervisionado, aprendizado semissupervisionado e aprendizado por reforço. Embora os dois primeiros sejam vastamente conhecidos e implementados, os dois últimos não possuem a mesma popularidade. Mas, como veremos a seguir, isso não se deve
The Text Investigation Framework utilizes several technologies built on SAS Viya, including SAS Visual Text Analytics, SAS Visual Data Mining and Machine Learning, and SAS Visual Investigator. SAS Visual Investigator acts as the orchestrator to surface the results. With its broad set of capabilities, SAS Visual Investigator can perform scenario authoring, alert generation and disposition, and comprehensive workflow to gather vital outcomes and feedback.
Unlocking the potential of your unstructured text data can lead to great business outcomes but the prospect of starting a new or enhancing your existing Natural Language Processing (NLP) program can feel overwhelming because of the inherently unique (and sometimes messy) nature of human language. Text data doesn’t fit neatly into rows or columns the way that structured data does, which can make it seem more complex to work with. Conversations and written language range from objective statements to subjective perspectives and opinions. The same sentence, depending on its intent and the nuances in how it's said, can have a positive, negative, or neutral sentiment. To get us started, we'll share different types of NLP models used to analyze unstructured data with a focus on the hybrid approach.
There's no question that we're all increasingly, and often exclusively, interacting with brands digitally. Consumers are now online through countless mechanisms – from laptops and mobile apps to AI-enabled voice assistants and sensor-based wearables. Engagement is diversifying in fascinating new ways. And when organizations can't see their customers interacting in
Customer data platforms (CDPs), data management platforms (DMPs), people-based marketing, identity graphs, and more overlapping topics represent an important ingredient of any martech brainstorming session in 2020. As your brand spreads out across touchpoints — from web to mobile applications, as well as call centers, email and direct mail —
En el mundo de negocios actual, las empresas necesitan operar con gran agilidad, innovar y ser resilientes, así como contar con un entorno de TI capaz de adecuarse a las demandas del mercado y del propio crecimiento del negocio. Dicha capacidad de respuesta puede verse un tanto frenada si una
전 세계 모든 산업의 변화를 주도하고 있는 주인공은 단연 AI(Artificial Intelligence, 인공지능)입니다. 운송, 금융, 엔터테인먼트, 헬스케어, 공공서비스, 에너지, 통신, 교육 등 모든 분야에서 AI는 비즈니스와 일상을 혁신하며 새로운 미래를 열고 있지요. SAS도 2019년 향후 3년간 AI 분야에 총 10억 달러 투자 계획을 발표한 바 있습니다. AI는 데이터라는 경험을 통해 배우고, 이
Decision trees are a fundamental machine learning technique that every data scientist should know. Luckily, the construction and implementation of decision trees in SAS is straightforward and easy to produce. There are simply three sections to review for the development of decision trees: Data Tree development Model evaluation Data The
