Operations | Monitoring | ITSM | DevOps | Cloud

Running ML/LLM models on Kubernetes Across Major Cloud Providers with Abhishek Choudhary

Abhishek, co-founder and CTO of @truefoundry, explores the complexities of building a machine learning platform on Kubernetes. Discover solutions to challenges like handling diverse hardware, managing large Docker images, and optimizing costs. Learn how True Foundry uses tools like Argo CD, Keda, and Istio to create efficient abstractions for data scientists and streamline ML operations.

JFrog & Qwak: Accelerating Models Into Production - The DevOps Way

We are collectively thrilled to share some exciting news: Qwak will be joining the JFrog family! Nearly four years ago, Qwak was founded with the vision to empower Machine Learning (ML) engineers to drive real impact with their ML-based products and achieve meaningful business results. Our mission has always been to accelerate, scale, and secure the delivery of ML applications.
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How AI and ML Are Revolutionizing Incident Management in IT Ops

In today’s digital landscape, IT operations face unique challenges and pressures unlike those of the past. Currently, the cost of a service failure for medium and large enterprises is estimated to exceed $100,000 per hour. At present high incident management costs, coupled with the impact on customer satisfaction, present significant challenges for enterprises. To resolve this challenge AI and ML assists in enhancing the overall management of incidents and reducing response times.

Top 5 reasons to use Ubuntu for your AI/ML projects

For 20 years, Ubuntu has been at the cutting edge of technology. Pioneers looking to innovate new technologies and ideas choose Ubuntu as the medium to do it, whether they’re building devices for space, deploying a fleet of robots or building up financial infrastructure. The rise of machine learning is no exception and has encouraged people to develop their models on Ubuntu at different scales.

Accelerating Innovation with MLOps Mastery

Machine Learning Operations (MLOps) is a methodology that combines machine learning (ML) with the principles of DevOps to streamline the development, deployment, and management of ML models. It addresses the unique challenges associated with operationalising ML, such as model versioning, reproducibility, and scalability.

Effective Observability for MLOps Pipelines at Scale with Rishit Dagli

Join Rishit Dagli as he explores effective observability for ML pipelines at scale. Learn about the critical differences between observability and monitoring in ML applications, common challenges like distribution shifts, and feedback loops. Rishit demonstrates practical methods for logging and interpreting various metrics to maintain model performance and reliability.

The strata of data: Accessing the gold in human information

I married into a family of geologists and rock and gem enthusiasts, and that bit of serendipity has added immensely to my life. Whenever we go on family hike excursions, I learn so much more about the landscape than I could have ever hoped to from my own educational path, and as a bonus my home gets adorned with tastefully and expertly chosen specimens from around the world.