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Ask the CEO: What Makes Machine Learning and AI so Powerful?

Ask the CEO: What Makes Machine Learning and AI so Powerful?

Transcription

Machine learning and AI are systems that are able to learn from a large number of dimensions.

If you're talking about a problem that has so many different features to it that we and our limitation are unable to understand because of our ability to handle depth. When you when you train a machine with multi-dimensionality algorithms that we have today on something that is simple.

Take for example, the weather. You train it on what the weather patterns were in, say, the 2010s and you take simple features like temperature clouds, wind speed and so on. You train that network with it and then you take that model that you've trained and you apply it to today and try to predict the weather tomorrow.

It is actually able to predict the weather tomorrow much better than the weather man without knowing anything about the weather. And that in itself is very powerful and actually helping us understand that maybe we were missing the dimensionality of the world around us. Maybe that is something that the machines are going to be capable of analyzing better than we do and provide us with some insights we we never were privy to in the first place. So I think there is a lot of hype around what we see as applications that there is also the flip side to that, that in some way machines could actually turn out to be a lot more intelligent than we are in that aspect.

20.11.2019

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DevFest West Coast 2020

DevFest West Coast 2020

Video: Machine Learning Engineering with Tensorflow Extended

Video: Machine Learning Engineering with Tensorflow Extended

Video: How to Build a Reproducible ML Pipeline

Video: How to Build a Reproducible ML Pipeline

Video: ML adventures with AutoML and TFHub

Video: ML adventures with AutoML and TFHub

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DevFest West Coast 2020

Watch videos of some of the world's top AI experts discuss everything from Tensorflow Extended to Kubernetes to AutoML to Coral.

Video: Machine Learning Engineering with Tensorflow Extended

In this talk, Hannes is providing insights into Machine Learning Engineering with TensorFlow Extended (TFX). He introduces how TFX for machine learning pipeline tasks and how to orchestrate entire ML pipelines with TFX. The audience learns how to run ML production pipelines with Kubeflow Pipelines, and therefore, free the data scientist's time from maintaining production machine learning models.

Video: How to Build a Reproducible ML Pipeline

Solving a data science problem usually requires multiple steps. These steps can include extracting and transforming data, training a model, and deploying the model into production. In this session, we'll discuss how to specify those steps with Python into an ML pipeline. We'll show how to create a Kubeflow Pipeline, a component of the Kubeflow open-source project. The audience will learn about how to integrate TensorFlow Extended components into the pipeline, and how to deploy the pipeline to the hosted Cloud AI Pipelines environment on Google Cloud. The key takeaway is how to improve reuse and reproducibility of the machine learning process.

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