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Conference: KubeCon + CloudNativeCon Europe 2022
Authors: Saiyam Pathak, Uma Mukkara, Udit Gaurav
2022-05-19
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Cloud native chaos engineering is becoming more democratic and important for maintaining resilience in complex dynamic deployment environments.
- Chaos engineering involves injecting failures into an environment to test the resilience of services and prevent sub-optimal behavior.
- Cloud native chaos engineering is open source and community-collaborated.
- Observability is important for customizing chaos engineering to an organization's needs.
- Cloud native chaos engineering is becoming more democratic and involves a larger set of personas, including devops engineers and cloud native developers.
- Cloud native chaos engineering is important for maintaining resilience in complex dynamic deployment environments with many moving parts.
Conference: KubeCon + CloudNativeCon North America 2021
Authors: Liz Rice
2021-10-14
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eBPF is a powerful platform for building cloud native observability, networking, and security tools that allow for collecting performance and behavioral insights from across an entire system, relating observed data to Kubernetes objects, and reliably instrumenting workloads without making any changes to apps or configurations.
- eBPF allows for dynamically running custom programs in the kernel
- eBPF programs can be attached to events in the kernel, such as network packets arriving or user space applications making system calls
- eBPF-based tools enable cloud native observability, networking, and security
- eBPF-based tools do not require changes to application code and provide instant insight and control over cloud native applications running in the cluster
- eBPF is being created on Windows, extending its powerful tooling capability from Linux to Windows
Conference: Transform X 2021
Authors: Justin Basilico
2021-10-07
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Recent trends in improving personalization at Netflix using deep learning, causality, bandits, and objectives
- Netflix faces a variety of challenges in personalization, including improving diversity, freshness, and fairness of recommendations
- Recent trends in improving personalization include using deep learning for recommendations, causality to understand the impact of recommendations, bandits to optimize recommendations, and objectives to measure success
- Deep learning involves learning embeddings for user and item IDs and using them to make predictions
- Causality involves understanding the impact of recommendations on user behavior using randomized experiments
- Bandits involve optimizing recommendations by balancing exploration and exploitation
- Objectives involve measuring success using metrics that capture user satisfaction and fairness
- Netflix is hiring for research and summer intern positions in personalization