{"id":991,"date":"2019-10-04T08:00:28","date_gmt":"2019-10-04T06:00:28","guid":{"rendered":"http:\/\/cmagazine.test\/local-data-science-is-dead-ai-with-a-devop-mindset\/"},"modified":"2021-12-23T13:02:49","modified_gmt":"2021-12-23T12:02:49","slug":"local-data-science-is-dead-ai-with-a-devop-mindset","status":"publish","type":"post","link":"https:\/\/www.codemotion.com\/magazine\/ai-ml\/local-data-science-is-dead-ai-with-a-devop-mindset\/","title":{"rendered":"Local Data Science is dead: AI with a DevOp mindset"},"content":{"rendered":"

Thiago de Faria<\/strong>, Solution Engineer at Linkit, strongly believes that the entire data science (ML<\/abbr>, AI<\/abbr>) pipeline should be processed online. This is only one of the cultural limitations of a developing world torn between computer science<\/span> degrees and non-delivering AI Masters\/PhD.<\/p>\n

Why don\u2019t we create an AI devops cloud-native app on K8S using blockchain backed by IoT devices that the user will experience<\/span> with a VR headset? A truly disruptive digital transformation!<\/p>\n


\n\"\"
\nA marketing way to say absolutely nothing in an ICT-based slang.<\/small><\/center>This is a very humorous quote by Thiago de Faria to mock today\u2019s ICT-based slang used by marketing staff to sell more, if not wrong, solutions. We could say that this is, more generally, the way to communicate to all audiences, each with its own slang – politics for one. But let’s talk AI.<\/p>\n

There is a need to avoid all this if the goal is to be a state-of-the-art developer in machine learning<\/span> and artificial intelligence<\/span><\/b>.<\/p>\n

\u201cThis is the 50th and last time this speech<\/span> is to be given,\u201d starts the solution engineer at Linkit and editor at ITNEXT: it has been a valuable performance<\/span> on how to correctly approach machine learning<\/span> programming<\/span>.<\/p>\n

\u201cThiago wakes up every day with one goal: develop happy high-performing teams<\/span> to decrease time-to-market and build production-ready applications, always!\u201d, states his Linkedin profile. The brilliant Brazilian is full of surprises. \u201cI must say that not having a formal computer engineer formation was quite nice<\/b> because I could focus on the real things. I worked over theoretical math, differential geometry and the like\u201d, recalls the AI engineer. \u201cMy master studies were later on AI, but from a very theoretical standpoint, on neural networks executing support vector machine algorithms\u201d.<\/p>\n

From theory to developing, and then to engineering<\/span>. \u201cI understood that the problem was not the algorithm, not the calculation, but having the correct data<\/span>, at the correct time, in the correct data<\/span> store: that\u2019s why I moved to the backend\u201d. Needless to say, he\u2019s a very big advocate of open source tools.<\/p>\n


\n\"\"
\nThiago playing guitar at Codemotion Rome, 2019. His fingers are so fast to cloak the instrument. How fast can he code?<\/small><\/center><\/p>\n

Culture is everything<\/h2>\n

A path made of more important steps is important, because the biggest problem is always about culture, from the ability to picture the real world from different points of view.
\nAI engineers usually come from Masters\/PhD models for publications<\/b>. For them, it\u2019s ok having something of lower quality, even failing. Here is their cultural problem. They don\u2019t need to deliver, they don\u2019t compete in the real world. They only have a single, non-competitive point of view.<\/p>\n

Fighting the downwards spiral<\/h2>\n

There is a timeless conflict between designers<\/span>, developers and sellers. The fight between designers and developers seems simple. \u201cThe answer is this model\u201d, states a designer<\/span>, showing a physical new device he envisioned. \u201cThe solution is in my code!\u201d, counterattacks the developer. This endless fight is solved by the commercial side: they listen to both and when meet the client they listens to him, maybe takes notes, then says something ending with \u201cwe have this beautiful Powerpoint!<\/b>\u201d.<\/p>\n

Thiago\u2019s definition of ML:
\nMake machines find patterns
\nwithout explicitly programming them to do so.<\/i><\/p>\n


\n<\/i>Thiago\u2019s definition of AI:
\nMaking computers capable of doing things
\nthat when done by a human,
\nwould be thought to require intelligence.<\/i><\/p>\n

Local DS is dead. Don\u2019t use that laptop!<\/h2>\n

The conflict also arises in ML\/AI\/DS<\/abbr> projects. ML development is still in its infancy, making the problem even bigger than in the usual software development. It\u2019s hard to track ML experiments to identify the correct mix of parameters, data<\/span> and code leading to a good model. Different models can\u2019t be easily moved among different tools. Each platform supports a different set of different algorithms. You can\u2019t easily reproduce results, deploy it, standardise it. Observing software is hard. In ML this worsens; it is even worse in production.<\/p>\n

Clearly there is a need for ML-based development. It can only be in the cloud.<\/p>\n


\n\"\"
\nIt\u2019s official: local data science is dead.<\/small><\/center><\/p>\n

Mlflow is the answer<\/h2>\n

Thiago suggests writing AI projects on open-source platforms. This will make it easier to update the code, maintain the system and observe the executions.<\/p>\n

In ML development you never know the true results of the tool you are using before testing it. This means the ML developer needs to try as many tools as possible for each step.<\/p>\n

Mlflow<\/strong> by Databriks is an Open Machine Learning<\/span> Platform and Framework. Hundreds of ML tools have been created to cover each phase of the ML lifecycle. Thiago strongly believes in Mlflow.
\nAs for the version of code and data, Github<\/span> is his preferred tool. All other steps can be processed inside Mlflow: evaluation, packaging, deployment and model serving.<\/p>\n

Mlflow brings openness to both the interface and the code. It is designed to work with any ML library, algorithm, deployment tool or language. It\u2019s built around REST APIs and simple data formats that can be used from a variety of tools. You can easily add MLflow to your existing ML code and deploy it inside your team<\/span>. A model can be viewed as a Lambda function (in the serverless model) so you can immediately run it at a very small cost without having to take care of the needed hardware capacity.<\/p>\n

You can also share workflow steps across organisations. Even more options are coming.<\/p>\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Thiago de Faria, Solution Engineer at Linkit, strongly believes that the entire data science (ML, AI) pipeline should be processed online. This is only one of the cultural limitations of a developing world torn between computer science degrees and non-delivering AI Masters\/PhD. Why don\u2019t we create an AI devops cloud-native app on K8S using blockchain… Read more<\/a><\/p>\n","protected":false},"author":8,"featured_media":946,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_editorskit_title_hidden":false,"_editorskit_reading_time":0,"_editorskit_is_block_options_detached":false,"_editorskit_block_options_position":"{}","_uag_custom_page_level_css":"","_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":""},"categories":[46],"tags":[],"collections":[],"class_list":{"0":"post-991","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-ai-ml","8":"entry"},"yoast_head":"\nLocal Data Science is dead: AI with a DevOp mindset - Codemotion Magazine<\/title>\n<meta name=\"description\" content=\"In his talk, Thiago da Faria showed how the workflow of a data scientist can be on the real life: here we report on his speech.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.codemotion.com\/magazine\/ai-ml\/local-data-science-is-dead-ai-with-a-devop-mindset\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Local Data Science is dead: AI with a DevOp mindset\" \/>\n<meta property=\"og:description\" content=\"In his talk, Thiago da Faria showed how the workflow of a data scientist can be on the real life: here we report on his speech.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.codemotion.com\/magazine\/ai-ml\/local-data-science-is-dead-ai-with-a-devop-mindset\/\" \/>\n<meta property=\"og:site_name\" content=\"Codemotion Magazine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/Codemotion.Italy\/\" \/>\n<meta property=\"article:published_time\" content=\"2019-10-04T06:00:28+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2021-12-23T12:02:49+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2019\/10\/M3A9280.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1013\" \/>\n\t<meta property=\"og:image:height\" content=\"675\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Leo Sorge\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@CodemotionIT\" \/>\n<meta name=\"twitter:site\" content=\"@CodemotionIT\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Leo Sorge\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/www.codemotion.com\\\/magazine\\\/ai-ml\\\/local-data-science-is-dead-ai-with-a-devop-mindset\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.codemotion.com\\\/magazine\\\/ai-ml\\\/local-data-science-is-dead-ai-with-a-devop-mindset\\\/\"},\"author\":{\"name\":\"Leo Sorge\",\"@id\":\"https:\\\/\\\/www.codemotion.com\\\/magazine\\\/#\\\/schema\\\/person\\\/99fe84861e17797989e16094aac727d5\"},\"headline\":\"Local Data Science is dead: AI with a DevOp mindset\",\"datePublished\":\"2019-10-04T06:00:28+00:00\",\"dateModified\":\"2021-12-23T12:02:49+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/www.codemotion.com\\\/magazine\\\/ai-ml\\\/local-data-science-is-dead-ai-with-a-devop-mindset\\\/\"},\"wordCount\":928,\"publisher\":{\"@id\":\"https:\\\/\\\/www.codemotion.com\\\/magazine\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/www.codemotion.com\\\/magazine\\\/ai-ml\\\/local-data-science-is-dead-ai-with-a-devop-mindset\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.codemotion.com\\\/magazine\\\/wp-content\\\/uploads\\\/2019\\\/10\\\/M3A9280.jpg\",\"articleSection\":[\"AI\\\/ML\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.codemotion.com\\\/magazine\\\/ai-ml\\\/local-data-science-is-dead-ai-with-a-devop-mindset\\\/\",\"url\":\"https:\\\/\\\/www.codemotion.com\\\/magazine\\\/ai-ml\\\/local-data-science-is-dead-ai-with-a-devop-mindset\\\/\",\"name\":\"Local Data Science is dead: AI with a DevOp mindset - 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