(For large-scale deployments, you can deploy a dedicated Inference Endpoint)
SYSTEMS
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Stanford MLSys Seminars: Essential Talks on Machine Learning Systems
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Really like this series of talks by Stanford MLSys Seminars on wide range of topics. All talks are great. A few examples: – FlashAttention: https://
youtube.com/watch?v=gMOAud
7hZg4
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– PaLM: https://
youtube.com/watch?v=CV_eBV
wzOaw
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– Building ML models like open-source software: https://
youtube.com/watch?v=0oGxT_
i7nk8
… All talk -
Automation enables higher abstraction and increased output quality
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Facts. If you benefit from automation, you can simply operate a higher level of abstraction, and do more to squeeze in even more quality on screen within the same amount of time.
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Condor Galaxy 1: Cerebras G42 Partnership Dramatically Reduces AI Training
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“Condor Galaxy 1 (CG-1), the result of the Cerebras and G42 partnership, dramatically reduces AI training time while eliminating the pain of distributed compute,” says our CEO, Andrew Feldman. pic.twitter.com/CEkomoyaMB
— Cerebras (@cerebras) 21 juillet 2023“Condor Galaxy 1 (CG-1), the result of the Cerebras and G42 partnership, dramatically reduces AI training time while eliminating the pain of distributed compute,” says our CEO, Andrew Feldman.
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Implementing AI: Scale, Team Size, and Outsourcing Considerations
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What about as a function of model size / dataset size, sophistication of your team, whether you have platform eng team or not, etc? Or just full stop it sucks I don’t wanna do it plz take my money to solve this for me? 🙂
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Multi-Cloud Strategy: Cost and Maintenance Challenges
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Using multiple clouds can be like owning several cars – sure, it gives more options, but the cost of maintaining and fueling them all can quickly add up! #ai #datascience #cloud #saschat
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Training Large Models Without Cluster Management Complexity
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anyone out there wanting to train large models but not have to deal with cluster management?
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Cerebras CG-1 vs Nvidia Israel-1: GPU Architecture Comparison
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Let's compare CG-1 with Nvidia's Israel-1. Israel-1 has 2048 H100 GPUs. But each GPU shows up as a unique device. It's your job to break apart your model and farm them out to each GPU. On Cerebras, 1 to 64 CS-2s show as one accelerator. Just implement mini-batches and train!
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Machine proliferation acceleration driving IoT cybersecurity challenges
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"The forces driving the proliferation of machines will continue to accelerate in 2023 and beyond." More in this report: https://
ow.ly/R0Vs50PhfUI #sponsored #keyfactor_ics #cybersecurity #icssecurity #cybercommunity #iotsecurity @IotCyber @GregorianCT1 @SecureIoTGuy via @fogoros -
Business Infrastructure Success with Generative AI
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Is your business infrastructure set up for success with #generativeAI? Three of our technical experts answer your most pressing questions to drive business value from your #AI investments and infrastructure. Learn more