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Sagify

@sagify_ml

Sagify is a MLOps for AWS tool by Kenza-AI team Docs: https://teapaw.org/t.co/WaD2hik2vS

 London, UK
sagifyml.com/
Joined January 2021
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Photo by Sagify (@sagify_ml)Photo by Sagify (@sagify_ml)Photo by Sagify (@sagify_ml)Photo by Sagify (@sagify_ml)Photo by Sagify (@sagify_ml)Photo by Sagify (@sagify_ml)Photo by Sagify (@sagify_ml)
3 weeks to go before #AWS Innovate, AI/ML edition, a free online event to learn about our latest #MachineLearning services and see them in action. From developers to business decision makers, there's something for everyone! Info and registration at aws.amazon.com/events/aws-in…
Image from Julien Simon (@julsimon)
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Useful lesson #1 for MLOps!
Pavlos Mitsoulis (@PavlosMitsoulis) profile picturePavlos Mitsoulis@PavlosMitsoulis19 Jan 2021
First step towards MLOps: - Implement a CLI tool (and/or library) #MLOps #sagify #sagemaker
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What youtube channels around ML or MLOps do you subscribe to?
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In 2021, what do you think is the single most important problem that the AI community should be working on?
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Our paper "Collaborative Classification from Noisy Labels" has been accepted to #AISTATS2021. It introduces a Bayesian model that fixes errors in metadata by taking advantage of user-item interactions. With @lucasmaystre, N Kumarappan & J Bütepage. Final version to come soon!
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Important different between ML in academia and applied ML
François Chollet (@fchollet) profile pictureFrançois Chollet
@fchollet24 Jan 2021
ML researchers work with fixed benchmark datasets, and spend all of their time searching over the knobs they do control: architecture & optimization. In applied ML, you're likely to spend most of your time on data collection and annotation -- where your investment will pay off.
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Woohoo 🙌 Sagify 0.20.8 supports AWS CLI Version 2 github.com/Kenza-AI/sagify #MLOps #sagemaker #AWS
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Agreed! If you can build it (and maintain it), build it! The tricky part is to figure out if you CAN indeed to build it!
TechCrunch (@TechCrunch) profile pictureTechCrunch
@TechCrunch15 Jan 2021
Twilio CEO Jeff Lawson says wisdom lies with your developers tcrn.ch/3nP54pK by @ron_miller
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A/B Testing Machine Learning Models buff.ly/2yDEPPK by @MLinProduction
Looking forward for the AWS Innovate AI/ML! #AWS #MachineLearning #MLOps
Julien Simon (@julsimon) profile pictureJulien Simon
@julsimon19 Jan 2021
Yes, it's that time of the year again: the AWS Innovate AI/ML online conference is back! Less slides, more demos. Everyone is welcome, from beginners to experts. See you there! aws.amazon.com/events/aws-in… #AWS #MachineLearning #SageMaker
Image from Julien Simon (@julsimon)
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Test-Driven Machine Learning Development buff.ly/3sl39g7 by @MLinProduction
Model Registries for ML Deployment buff.ly/3bAZMsc by @MLinProduction
Online Inference for ML Deployment buff.ly/35vxjUo by @MLinProduction
The Challenges of Online Inference buff.ly/3nzuKql by @MLinProduction
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Batch Inference for Machine Learning Deployment buff.ly/39tBXDz by @MLinProduction
Software Interfaces for Machine Learning Deployment buff.ly/39pRFzl by @MLinProduction
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What Does it Mean to Deploy a Machine Learning Model? buff.ly/2LJsFuC by @MLinProduction
What Does it Mean to Deploy a Machine Learning Model? (Deployment Series: Guide 01) - ML in...

What Does it Mean to Deploy a Machine Learning Model? (Deployment Series: Guide 01) - ML in...

Thinking about deployment as a software engineer rather than as a data scientist will dramatically simplify what it means to deploy a model. Learn more now.

mlinproduction.com
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6 myths about Machine Learning models in production by @chipro Really useful!
erwtokritos (@erwtokritos) profile pictureerwtokritos@erwtokritos12 Jan 2021
Principles of Good Machine Learning Systems Design by @chipro youtube.com/watch?v=c_AUuTuP… #ml
Stanford MLSys Seminar Episode 5: Chip Huyen

Stanford MLSys Seminar Episode 5: Chip Huyen

Episode 5 of the Stanford MLSys Seminar Series!Principles of Good...

youtube.com
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5/ Myth #5: Most ML engineers don't need to worry about scale
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6/ Myth #6: ML can magically change your business overnight
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