Machine learning, data science and AI meet IoT
Capturing business value from IoT data
From smart watches to smart cars and smart cities, we can put sensors on virtually everything around us. What will we do with all that IoT data? In this video, Kirk Borne and Michele Null discuss how artificial intelligence, machine learning and data science can help you capture more value from IoT data – to drive efficiency, differentiate services and open the door for entirely new business models.
Recommended reading
-
Article
Mit synthetischen Daten AI-Durchbrüche ermöglichenIn diesem Artikel beleuchten wir die entscheidende Rolle von synthetischen Daten in unseren daten-hungrigen AI-Initiativen, wie Unternehmen Wachstum mit synthetischen Daten generieren können, und welche ethischen Fragen noch nicht geklärt sind.
-
Article
Was ist ein Data Lake und warum ist er wichtig?Ein Data Lake kann große Mengen an Rohdaten speichern. Er erlaubt den direkten Zugriff sowie eine einfache Darstellung/Analyse der Daten. SAS DE
-
Article
frtb: a wait and see strategy could be riskyFRTB, fundamental review of the trading book, is a regulation that changes how banks analyze market risk in the trading book to address systemic challenges.
-
Article
ModelOps: How to operationalize the model life cycleModelOps is where analytical models are cycled from the data science team to the IT production team in a regular cadence of deployment and updates. In the race to realizing value from AI models, it’s a winning ingredient that only a few companies are using.
Ready to subscribe to Insights now?
Subscribe to Insights newsletter