Industrialize Machine Learning to Minimize Technical Debt
MLOps is not something you need in the future as you scale – you need it today. Build for speed and agility now, while minimizing the technical debt that will otherwise slow your organization down to a crawl later.
Kong Applies Machine Learning to Microservice ManagementThenewstack
Refactoring Is Not Bad, Until It IsThenewstack
Accessibility as an Essential Part of the Inclusive Developer ExperienceThenewstack
AIOps, Blockchain and Other BuzzwordsThenewstack
Run Stateful Containerized Workloads with Rancher Kubernetes Engine and PortworxThenewstack
FireHydrant: Managing Incidents Without the ChaosThenewstack
DevSecOps: Why Security Shouldn’t be Sacrificed for SpeedThenewstack
Lessons Swift Designer Chris Lattner Has Learned about LeadershipThenewstack
Summer Data Engineering RoadmapMotherduck
Capturing Logs in Docker DesktopDocker
How To Create and Use Avatars in eLearningHeygen
From Hawking to Siri: The Evolution of Speech SynthesisDeepgram
How Sky Italia Digitizes Learning Paths to Accelerate New Product Launches by 4xSynthesia
How EPOS achieved a 90% learning completition rate with AI video.Synthesia
How Mondelez Accelerates Supply Chain Training Across 150+ Global Manufacturing PlantsSynthesia
Training: Hard Work Pays off for Data/AI ProsThenewstack
Breakdown: The Kubernetes-Run AI Video Generation Pipeline for NIUS.TVThenewstack
Cerebras Architecture Deep Dive: First Look Inside the HW/SW Co-Design for Deep Learning [Updated]Cerebras
Cirrascale Cloud Services® and Cerebras Systems Announce Availability of Cerebras Cloud @ CirrascaleCerebras
DockerCon Workshops: Getting Started with Docker, AI/ML, Secure DevelopmentDocker
