Jan 31, 2023

We learnt a lot about Kafka on the way to 1 trillion messages, and built some interesting internal tools to ease adoption that will be explored in this blog post.

Understanding and developing large language models.

Whenever I transition to a new opportunity, I think about how to “start well.” How can I ramp up as effectively as possible? How do I balance the urge to “show value” immediately with making the right decisions?

We’ve made architecture changes to Meta’s event driven asynchronous computing platform that have  enabled easy integration with multiple event-sources.  We’re sharing our learnings from handling va…

SQL indexing and tuning tutorial for developers. No unnecessary database details—just what developers need to know. Covers all major SQL databases.

This is the Rust course used by the Android team at Google. It provides you the material to quickly teach Rust. - google/comprehensive-rust

The first-ever course on data-centric AI. Learn how you can train better ML models by improving the data.

Linux, Jenkins, AWS, SRE, Prometheus, Docker, Python, Ansible, Git, Kubernetes, Terraform, OpenStack, SQL, NoSQL, Azure, GCP, DNS, Elastic, Network, Virtualization. DevOps Interview Questions - bre...

This course introduces the foundations of neuroscience, from the biochemistry of neurotransmitters, the electrical basis of action potentials, to the computations performed by neural circuitry and the social implications of atypical neural function. This…

How to use the Kubernetes memory requests and limits in combination with JVM Heap and stay out of trouble.

We present a comprehensive evaluation of GPT-4, a state-of-the-art LLM, on medical competency examinations and benchmark datasets. Our results show that GPT-4, without any specialized prompt crafting, exceeds the passing score on USMLE by over 20 points and…

Twitter aims to deliver you the best of what’s happening in the world right now. This blog is an introduction to how the algorithm selects Tweets for your timeline.

This free course will teach you some of the intuition, math, and best practices for effective ML observability. Learn common metrics in model monitoring.