AI/ML 모델 개발 상의 어려움과 이를 해결하기 위한 접근법으로서 ModelOps의 필요성이 대두되고 있습니다. (참조 : AI/ML 기반 모델 개발, 과제와 해결방안은?) 이번 글에서는 ModelOps가 구체적으로 어떤 제품인지, 어떤 장점을 제공하며 구현방법은 어떠한지 등에 대해 설명드리도록 하겠습니다. 이에 앞서 ModelOps의 구현에 중요한 역할을 하는 ‘모델 거버넌스’에 대해 잠깐 짚어보도록 하겠습니다. 모델
Want to deploy digital twins as part of your predictive maintenance strategy? You should, for countless reasons that have been identified elsewhere. But like anything else worth doing in business, it’s a process – you need to know where your organization lands on the maturity curve, and where it’s going.
기업내에 AI/ML를 적용하기 위해, 업무 관점에서 시민 데이터 사이언티스트(Citizen Data Scientist, 이하 CDS)와 그 필요 역량인 데이터 문해력(Data Literacy)의 중요성이 높아지고 있습니다.(참고 : 데이터 문해력과 시민 데이터 사이언티스트의 필요 역량) 이와 연결하여, 데이터를 기반으로 신속하게 개발한 예측 모델을 업무 시스템에 통합 또는 활용하기 위해 IT 관점에서 해결해야할 과제와 접근 방안에 대해
SAS Model Manager and the sasctl packages aim to create a seamless ModelOps and MLOps process for Python and R models. Python and R models are not second-class citizens within SAS Model Manager. SAS, Python, and R models can be easily managed using our no-code/low-code interface. This is an interface that can be extended to support a variety of use cases.
"For those not knee-deep in the ModelOps process, the process may seem simple," says Ankit Sinha, Director of Product Management at Experian: "You build the model, deploy the model and reap the benefits." But the process starts to become very complex when you're using multiple database systems and data sources
You can start learning about ModelOps and SAS Model Manager now. Compare the various educational resources provided by SAS according to your learning preference.
Between DevOps, DataOps, MLOps and ModelOps, there are different "Ops" based on different environments. "Ops" generally is the shortened version of Operations. Check out some of the different ones in our current technological world. How many do you know? Learning about DevOps DevOps or Developer Operations refers to applying agile
DataOps increases the productivity of AI practitioners by automating data analytics pipelines and speeding up the process of moving from ideas to innovations. DataOps best practices make raw data polished and useful for building AI models. Models need to work on the data that is introduced, as well as on
Whether working as a business analyst, data scientist or machine learning engineer, one thing remains the same – making an impact with data and AI is what really matters. Pre-processing and exploring data, building and deploying models and turning those scoring values into an actionable insight can be overwhelming. A
Attend this session during the SAS Explore event on Sept 27-29 or view the recording at your convenience. We will showcase the use of SAS Intelligent Decisioning, SAS Model Manager, and SAS Visual Analytics on the SAS Viya platform for a solution that helps mitigate inequitable credit decisions.
SAS is pleased to announce a new ModelOps certification. Recognizing the growing need in this emerging area, this new credential will help create a standard of knowledge within the area of ModelOps.
You’ve probably heard of DevOps, but do you know about DataOps? It builds on the DevOps approach to provide huge benefits in unlocking business value from data. Many people have heard of DevOps, even if they don’t know precisely what it means. It is an agile approach to software development,
As promised in this latest blog about the Gartner Data & Analytics Summit in London, here’s an update from the second day at the SAS booth. To make a long story short, each day the SAS booth team posed a question to attendees visiting the booth. They could submit three
Just getting started with this series? Make sure to explore the earlier posts Part 1, Part 2 and Part 3. Up until now, you have seen how ModelOps can solve your biggest machine learning challenges and that SAS and Microsoft, together, can help you deploy, govern and monitor your models
Just getting started with this series? Make sure to explore Part 1 and Part 2. There are different ways you can use these two tools to accelerate model building, deployment and monitoring. Figure 1 summarizes best practices for conducting ModelOps using SAS Model Manager and Azure Machine Learning. Best practice
Just getting started with this series? Make sure to read part 1: How ModelOps addresses your biggest Machine Learning challenges. SAS and Microsoft make it easier for companies to address the challenges of machine learning model deployment, monitoring and governance. Specifically, SAS and Microsoft have built integrations between SAS® Model
Predictive models are a critical component for automated and augmented decision making. As this deployment pattern becomes more widely adopted, two competing priorities emerge. How can we deliver more models faster while being certain of accurate and consistent performance? The key to solving this dilemma is in the automated testing
We kunnen er niet meer omheen, data en analytics zijn overal aanwezig in ons dagelijks leven. In organisaties groot en klein wordt de behoefte om eindelijk eens iets met de data te gaan doen alsmaar sterker. We kunnen zo enkele vraagstukken in onze samenleving opnoemen die we met data en
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
ModelOps is NOT a one-size-fits-all approach. It's important to identify the appropriate level of sophistication based on both the organization’s current readiness and its current and future business needs. My previous articles on Medium talked about why organizations should choose ModelOps, and how to implement ModelOps using a holistic approach
The model management process, which is part of ModelOps, consists of registration, deployment, monitoring and retraining. This post is part of a series examining the model management process, orchestrated through the Model Manager (MM) APIs. The focus of part one is on model registration, specifically on using the APIs from
Kim Kaluba explains how connecting data to decisions helps create resiliency.
Does your bank manager know who you are? Unless your net worth is unusually high, the answer is probably no, and that’s been the case for many years. Banks have been using statistical models to inform credit-related decisions since at least the 1970s, and today almost every aspect of operational
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.
