Alejandro Saucedo - Industrial Machine Learning Pipelines with Python & Airflow

author Alejandro Saucedo   1 мес. назад
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Subscribe here: https://goo.gl/9FS8uF Become a Patreon!: https://www.patreon.com/ColdFusion_TV Visual animal AI: https://www.youtube.com/watch?v=DgPaCWJL7XI Hi, welcome to ColdFusion (formally known as ColdfusTion). Experience the cutting edge of the world around us in a fun relaxed atmosphere. Sources: Why AlphaGo is NOT an "Expert System": https://googleblog.blogspot.com.au/2016/01/alphago-machine-learning-game-go.html “Inside DeepMind” Nature video: https://www.youtube.com/watch?v=xN1d3qHMIEQ “AlphaGo and the future of Artificial Intelligence” BBC Newsnight: https://www.youtube.com/watch?v=53YLZBSS0cc http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html http://www.ft.com/cms/s/2/063c1176-d29a-11e5-969e-9d801cf5e15b.html http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html#tables https://www.technologyreview.com/s/533741/best-of-2014-googles-secretive-deepmind-startup-unveils-a-neural-turing-machine/ https://medium.com/the-physics-arxiv-blog/the-last-ai-breakthrough-deepmind-made-before-google-bought-it-for-400m-7952031ee5e1 https://www.deepmind.com/ www.forbes.com/sites/privacynotice/2014/02/03/inside-googles-mysterious-ethics-board/#5dc388ee4674 https://medium.com/the-physics-arxiv-blog/the-last-ai-breakthrough-deepmind-made-before-google-bought-it-for-400m-7952031ee5e1#.4yt5o1e59 http://www.theverge.com/2016/3/10/11192774/demis-hassabis-interview-alphago-google-deepmind-ai https://en.wikipedia.org/wiki/Demis_Hassabis https://en.wikipedia.org/wiki/Google_DeepMind //Soundtrack// Disclosure - You & Me (Ft. Eliza Doolittle) (Bicep Remix) Stumbleine - Glacier Sundra - Drifting in the Sea of Dreams (Chapter 2) Dakent - Noon (Mindthings Rework) Hnrk - fjarlæg Dr Meaker - Don't Think It's Love (Real Connoisseur Remix) Sweetheart of Kairi - Last Summer Song (ft. CoMa) Hiatus - Nimbus KOAN Sound & Asa - This Time Around (feat. Koo) Burn Water - Hide » Google + | http://www.google.com/+coldfustion » Facebook | https://www.facebook.com/ColdFusionTV » My music | t.guarva.com.au/BurnWater http://burnwater.bandcamp.com or » http://www.soundcloud.com/burnwater » https://www.patreon.com/ColdFusion_TV » Collection of music used in videos: https://www.youtube.com/watch?v=YOrJJKW31OA Producer: Dagogo Altraide Editing website: www.cfnstudios.com Coldfusion Android Launcher: https://play.google.com/store/apps/details?id=nqr.coldfustion.com&hl=en » Twitter | @ColdFusion_TV

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How to create apps and land your first client with Python in less than 90 days even if you have never written a line of code... http://cleverprogrammer.to/first-client-with-python Enroll for exercises, tutorials, courses, and projects... http://cleverprogrammer.to/enroll Enroll in Learn Python™ course http://cleverprogrammer.to/enroll ================================================== Connect With Me! Website ► http://cleverprogrammer.to/enroll Facebook ► http://cleverprogrammer.to/facebook Twitter ► http://cleverprogrammer.to/twitter Instagram ► http://cleverprogrammer.to/instagram Snapchat ► Rafeh1 iTunes Podcast ► http://cleverprogrammer.to/podcast Google Podcast ► http://cleverprogrammer.to/google-podcast Support (Patreon) ► http://cleverprogrammer.to/patreon Youtube ► https://www.youtube.com/c/CleverProgrammer Github (Code) ► http://cleverprogrammer.to/github

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PLEASE READ: This video demonstrates an EXAMPLE of git branching and merging strategies. It's a decent template to get you started in different situations. It's not a "THIS IS HOW YOU SHOULD DO IT" video. These are ideas based upon my experience. Hopefully they will increase your understanding of git. In the end, you should UNDERSTAND your own process, how git works, and then create a branching and merging strategy that works for YOU and your situation. ---- Three similar branching and merging strategies in Git, with a common goal of keeping development history linear. PowerPoint: http://goo.gl/kQPwb NOTE 1: This example branching and merging strategy is intended for larger development teams that manage a formalized development cycle with distinct development, testing, and release phases, and require production maintenance of more than just the latest version of the product. I don't recommend these thoughts for small teams and simple development processes. NOTE 2: One thing to remember is that my advice is simply an example put together from my experiences. If you don't know where to start, the specific processes I've laid out are a good starting point, but be sure as you get more comfortable with things to adapt them to your specific needs or preferences. NOTE 3: Note 2 implies that you take the time to understand what I've laid out and not just emulate it and also to be in touch with what your development process needs are.

Industrial Machine Learning Pipelines with Python & Airflow
[EuroPython 2018 - Talk - 2018-07-26 - Fintry [PyData]]
[Edinburgh, UK]

By Alejandro Saucedo

Industrial Machine Learning

This talk will provide key insights on the learnings I have obtained throughout my career building & deploying machine learning systems in critical environments across several sectors. I will provide a deep dive on how to build scalable and distributed machine learning data pipelines using Airflow with a Celery backend. I will also compare Airflow with other technologies available out there and how it differentiates, such as Luigi, Chronos, Pinball, etc. If you attend the talk, you will obtain an understanding on the solid fundamentals of Airflow, together with its caveats and walk-arounds for more complex use-cases. As we proceed with the examples, I will cover the challenges that you will run into when scaling Machine Learning systems, and how Airflow can be used to address these using a manager-worker-queue architecture for distributed processing with Celery. By the end of this talk you will have the knowledge required to build your own industry-ready machine learning pipelines to process data at scale, and I will provide further reading resources so people are able to implement the knowledge obtained almost right away.

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