6 machine learning pitfalls to avoid
Data science expert Andy Pulkstenis describes how to skip common machine learning mistakes.
Tag: analytical models
ModelOps: Operasyonel Analitik
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
Risk modeling during a crisis requires new data
Katherine Taylor explains how and why new data improves risk models.
5 ways to evolve the modeling process after COVID-19
Move from rapid response to long-term resiliency based on these 5 tips.
The problem with analytics
At SAS, we use terms like “machine learning,” “predictive modeling” and, of course, “analytics” quite a bit in our day-to-day business. Not surprising, given that we're the largest analytical software vendor out there. But have you noticed that these terms are popping up more frequently in news articles and blogs?
Big: Data, model, quality and variety
The “big” part of big data is about enabling insights that were previously indiscernible. It's about uncovering small differences that make a big difference in domains as widespread as health care, public health, marketing and business process optimization, law enforcement and cybersecurity – and even the detection of new subatomic particles.