English | Size: 2.63 GB
Genre: eLearning
Machine learning has grown significantly, and with it the footprint of ML models—which can make training, deploying, and monitoring difficult and expensive. What if you could make your ML models and systems more efficient, whether in the form of cost, compute, storage, latency, or carbon footprint? Join us for this Superstream where experts dive into techniques for using fewer resources and delivering better quality.
What you’ll learn and how you can apply it
Understand hardware and software resources required for deep learning
Learn how to optimize ML models and workloads
Discover how to build robust and scalable machine learning systems
Explore AI efficiencies that combat climate change
You’re an ML engineer or data practitioner who wants to use more-efficient algorithms and improve ML model efficiency.
You’re a data team leader or CDO who wants to proactively reduce the cost and resource use of ML systems and pipelines.
You’re a product stakeholder who wants to learn more about how ML efficiencies align with business goals.
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