Spotify, a human's digital jukebox, has been a data-driven company since day one, using data for various purposes including payments and experimentation. Managing the vast amount of data required a more streamlined approach, leading to the development of their internal data platform.
**Event Delivery System:**
- **On-Premises Setup:** Initially, Spotify used on-premises solutions like Kafka and HDFS. Event data from clients was captured, timestamped, and routed to a central Hadoop cluster.
- **Google Cloud Transition:** In 2015, Spotify moved to Google Cloud Platform (GCP) for better scalability and reliability. Key components include File Tailer, Event Delivery Service, Reliable Persistent Queue, and ETL jobs using Dataflow and BigQuery.
This is a hands-on guide with Python example code that walks through the deployment of an ML-based search API using a simple 3-step approach. The article provides a deployment strategy applicable to most machine learning solutions, and the example code is available on GitHub.
In this article, we explore how to deploy and manage machine learning models using Google Kubernetes Engine (GKE), Google AI Platform, and TensorFlow Serving. We will cover the steps to create a machine learning model and deploy it on a Kubernetes cluster for inference.
Launched in 2007, Chess.com is a premium platform for online chess and one of the largest of its kind. A Cloud SQL for MySQL shop, it transitioned to Cloud SQL Enterprise Plus edition, improving the user experience, cutting costs, and significantly reducing response times, decreasing p99 latency response from 14ms to 4ms. Read on to learn more.
llm-tool provides a command-line utility for running large language models locally. It includes scripts for pulling models from the internet, starting them, and managing them using various commands such as 'run', 'ps', 'kill', 'rm', and 'pull'. Additionally, it offers a Python script named 'querylocal.py' for querying these models. The repository also come