Predictive Maintenance & Monitoring using Machine Learning: Demo & Case study (Cloud Next '18)
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- čas přidán 23. 07. 2018
- Learn how to build advanced predictive maintenance solution. Learn what is predictive monitoring and new scenarios you can unlock for competitive advantage.
MLAI223
Event schedule → g.co/next18
Watch more Machine Learning & AI sessions here → bit.ly/2zGKfcg
Next ‘18 All Sessions playlist → bit.ly/Allsessions
Subscribe to the Google Cloud channel! → bit.ly/NextSub event: Google Cloud Next 2018; re_ty: Publish; product: Cloud - General; event: Google Cloud Next 2018; - Věda a technologie
Crystal clear presentation on ideas and method on machine learning for industrial use cases. Best video i have come across that discussed so in-depth in such a short time. Salute to the presenter.
The best presentation about machine learning and predictive maintenance I have watched so far.
Incredible talk! Clear narration, examples, summaries and use cases. Straight to the point. Thank you!
Very good use cases .Shows the power of ML/AI in problem solving & improving the business.
Good video, very informative!
Awesome session and sharing!! Thank you!
Very helpful video, my domain is predictive maintenance and now I get to know how ML can be implemented.
I have a big image of PdM clearly. Thank for informative talking
Informative and insightful talk , thanks 🙏
Brilliant!!
Great Presentation, Thanks for sharing.
Can someone identify with which framework this app at 22:10 was built plz ?
Great presentation.
Can perform PdM without IOT data? I have just the maintenance history data pls?
Nice surname "DEVADAS". You can DEVelop ADAS (Advanced driver-assistance systems)! Anyway you know ML which is widely used in ADAS.
Can we see the codes here somewhere?
what machine learning or deep learning algorithms are most relevant in predictive maintenance, like for mechanical machinery? I want to focus on them.
thank you in advance.
So the video did not answer this question? Maybe its not worth watching.
Well, it does. Check minute 30:00!