Chemical companies are sitting on data gold mines waiting to be exploited. Yet most of them struggle to capitalize on the full potential and insights of their data. How can you make advanced analytics work for you and gain competitive advantage? For the past couple of years, chemical companies have
지난 글에서는 분석 모델을 배포하기까지 많은 시간이 소요되는 이유, 이를 극복하기 위한 방법으로서 운영계에 적용하는 ModelOps의 개념과 효과를 소개해드렸습니다. 하지만 통상적으로 기업의 의사결정이 분석의 결과만으로 이뤄지지는 않습니다. 분석 인사이트를 기반으로 하되 기업에서 설정한 비즈니스 룰을 확인해야 하며, 기업 안팎의 상황에 대한 검토도 필요합니다. 금융권을 예로 들면, 고객의 신용대출 요청에 따른
기업에서는 하루에도 여러 차례 비즈니스에 중요한 의사결정을 내리고 있습니다. 최선의 선택을 하기 위해 많은 기업이 강력한 분석 모델을 개발하여 의사결정 프로세스에 분석 결과를 통합하고 있습니다. 하지만 의사결정에 결정적인 역할을 하는 대부분의 분석 모델은 빛을 보지 못합니다. 데이터 중심의 의사결정을 위한 실용화의 마지막 관문을 넘지 못하기 때문입니다. 본 글에서 데이터 중심의
Zorlu Son Aşama: Model İmplementasyonu Günümüzde neredeyse tüm organizasyonların iş kararları vermek için, veriden faydalanarak gerçek zamanlı içgörüler elde etmeye çalıştığı bir dijital yolculuk içerisinde olduklarını görüyoruz. Sınırlarını hayalgücümüzün ve yeteneklerimizin belirlediği veri analitiği bizlere sonsuz bir potansiyel sunuyor. 2019 yılında analitik yazılımlara 190 milyar Dolar yatırım yapılması da şirketlerin
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.
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.
El gobierno de los modelos analíticos es una función cada vez más estratégica para las empresas. Para analizar su importancia hemos llevado a cabo un evento digital junto al Club Chief Data Officer (CDO) Spain & Latam. Aprovechando la ocasión entrevistamos a Silvina Arce, Co-Founder del Club Chief Data Officer
Jim Harris explains the relevance of DevOps, DataOps and ModelOps for data analytics practitioners.
Most model assessment metrics, such as Lift, AUC, KS, ASE, require the presence of the target/label to be in the data. This is always the case at the time of model training. But how can I ensure that the developed model can be applied to new data for prediction?
This post describes a fully automated validation pipeline for analytical models as part of an analytical platform, which has been set up recently as part of a customer project.
Let us now take a look at a well-known metaphor for test case development in the software industry. We are referring to the idea of the “test pyramid."
In total, there are four posts in this blog series, this is the first post describing some basic principles of the DevOps (or ModelOps) approach.
Everyone is talking about artificial intelligence (AI) and how it affects our lives -- there are even AI toothbrushes! But how do businesses use AI to help them compete in the market? According to Gartner research, only half of all AI projects are deployed and 90% take more than three
A business glossary improves data quality – one of the top five ways it makes analytics better.
Getting value from analytics is becoming top of mind for businesses. Organizations have invested millions of dollars in data, people and technology and are looking for a return on their investment. That requires operationalizing analytics so that it can be used for strategic decision making -- often referred to as
How do you deploy your model so that business processes can make use of it? This post explores how SAS Viya applications can directly add models to a model repository, and specifically focuses on how to deploy them with SAS Model Manager to Hadoop.
This is the fourth post in my series of 10 machine learning best practices. It’s common to build models on historical training data and then apply the model to new data to make decisions. This process is called model deployment or scoring. I often hear data scientists say, “It took
The increasing use of predictive analytics in mission-critical business decisions and operations brings new challenges to the forefront for many of our customers. Throughout the last year I spoke to many customers about their use of predictive analytics and where they see areas of improvement to achieve even more success
In part 1 of my thoughts about analytics maturity, I deferred talking about issues related to the actual assessment of your organization’s level. Today I intend to detail some of the ways my peers and I are thinking about analytical maturity, comment on scales in use today, and address some
