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Five tips and tricks to improve your AI workloads
- Gemini Code Assist, a free AI coding assistant, is now available for startups and individual developers, making advanced technology more accessible.
- Optimizing large AI workloads is crucial due to the potential high costs involved despite increased accessibility.
- Five tips to optimize workflow on Google Cloud Platform include exploring different platforms for training/inference and utilizing services like Vertex AI.
- Improving startup time of inference containers by storing models externally, using additional boot disks for GKE, and leveraging image streaming feature for faster workload initiation.
- Storage considerations for AI workloads involve maximizing GPU/TPU utilization, using Filestore or Cloud Storage for different needs, and considering Parallelstore for lower latency requirements.
- Utilizing Dynamic Workload Scheduler and Future Reservations to secure resources and reduce delays in acquiring necessary hardware for AI workloads.
- Custom disk images can expedite VM setup for AI workloads, saving time by pre-configuring necessary software and enabling quick deployment of fully configured instances.
- Stay updated with Google Cloud developments by subscribing to channels like YouTube, the blog, newsletter, and joining programs like Google Cloud Innovators.
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