@huggingface inference endpoint integration If you've deployed your Hugging Face model to an inference endpoint, you can use it easily in LangChain! s/o @EnoReyes for adding Inference Endpoint Docs: https://
huggingface.co/inference-endp
oints
… Code: https://
github.com/hwchase17/lang
chain/blob/master/langchain/llms/huggingface_endpoint.py
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CODE
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Hugging Face Inference Endpoint Integration in LangChain
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LearnOpenCV Launches New AI Course on Kickstarter for Valentine’s Day
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This Valentines Day, our latest course is launching on Kickstarter. Stay tuned! https://
bit.ly/3HG6gI7 #kickstarter #course #launch #ai #computervision #deeplearning #machinelearning #artificialintelligence -
Abacus AI: Complete MLOps Platform for End-to-End Solutions
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Sounds interesting but don't know where to start? Check out this end-to-end MLOps platform by @abacusai https://
abacus.ai -
MLOps Bridges Data Science and IT for Secure AI Deployment
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At the end of the day, MLOps practices can help organizations to achieve their AI goals more quickly and efficiently. By fostering collaboration between data science and IT teams, MLOps can help to ensure that AI is deployed in a way that is secure, reliable, and scalable.
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MLOps ensures secure reliable scalable model deployment
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This improved collaboration between data science and IT also helps to ensure that models are deployed in a way that is secure, reliable, and scalable. For ex, MLOps practices can help to prevent security breaches and ensure that models continue to perform well in production.
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MLOps Enables Efficient Collaboration Between Data Scientists IT
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One of the key benefits of MLOps is that it enables data scientists and IT professionals to work together more efficiently and effectively. For example, MLOps practices can help to ensure that data scientists have access to the right infrastructure and tools.
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MLOps: Bridging the Gap Between Data Science and IT Teams
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Do you ever feel like your data science and IT teams are speaking different languages? That's where #MLOps comes in! By standardizing workflows and processes, MLOps can bridge the gap between these two critical teams. (A thread)
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Transformers for Enterprise Use Cases Course Launch
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Very pleased to collaborate with @get_sphere and teach the "Transformers for Enterprise Use Cases" course to a new cohort! Kicking off on March 20th, learn from experts how to apply the Hugging Face Ecosystem to your business use cases!
Join us https://
getsphere.com/cohorts/transf
ormers-for-enterprise-use-cases?source=Instructor-TwitterPost-262023-hf_social
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Neural Attention Mechanism: The Key to Transformer Success
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The “neural attention mechanism” is the secret sauce that makes transformers successful on various tasks and datasets. Check out our latest blog post on vision transformers (ViTs).https://t.co/MvPPYKZ5nC#ai #computervision #visiontransformer pic.twitter.com/psxhbtaeYL
— Satya Mallick (@LearnOpenCV) 7 février 2023The “neural attention mechanism” is the secret sauce that makes transformers successful on various tasks and datasets. Check out our latest blog post on vision transformers (ViTs). https://
learnopencv.com/attention-mech
anism-in-transformer-neural-networks/
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Automated Plugin Registry Protocol: Convenience Versus Explicitness Trade-offs
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In my case it was an automated plugin registry; adopt a certain protocol and it would automagically be called for events, no need to manually register. Wouldn’t trade convenience + performance for being explicit again, but 12 years ago I thought it was cool.