How do companies use Ray?

Companies leverage Ray, a distributed computing framework, for a variety of use cases due to its scalability and user-friendly interface. Key benefits and applications include:

1. Machine Learning (ML) and AI Tasks: Ray streamlines ML tasks such as reinforcement learning, hyperparameter optimization, and deep learning across numerous machines, enhancing scalability and reducing complexity. Major users include Uber, Shopify, and OpenAI 1.

2. Compute-Intensive AI Applications: It shines in handling compute-intensive workloads required for training and deploying models, processing large datasets, and running inference on a large scale. Companies like Uber and Pinterest rely on Ray for their deep learning and model training operations 2.

3. Parallel Processing: Ray simplifies parallel processing, making it easier to execute tasks like demand prediction, NLP on large document batches, and backtesting for financial models. This reduces development time and increases efficiency 1.

4. Scaling AI Workloads: Ray is valuable for businesses looking to scale their AI capabilities without building extensive internal infrastructure, making it ideal for startups and mature companies handling large-scale video data and diverse compute-intensive tasks 3.

   Exploring Ray's Value

   Waleed Kadous

   Demetrios Brinkmann

   Discover the three key aspects of Ray - a clean and cute Python interface for distributed computing, an intelligent cluster auto scheduler, and an ecosystem of libraries for machine learning tasks. Learn how companies like OpenAI, Uber, Instacart, Shopify, and Spotify utilize Ray for their ML infrastructure. Explore the flexibility and power of Ray for tasks such as demand prediction, NLP, backtesting, and more.

   

   MLOps.community

   ML Scalability Challenges // Waleed Kadous // MLOps Podcast # 154

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5. Flexibility and Open Source Benefits: With its broad ecosystem, Ray supports various AI and ML frameworks, making it easier for companies to adopt and integrate into their existing workflows. Its open-source nature attracts a wide array of contributions and innovations 4.

In essence, Ray enables companies to efficiently scale and manage their AI and ML workloads, providing significant reductions in complexity and operational costs.