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Manceps

OUR LATEST ARTICLES

Ask the CEO: What's Behind the AI Revolution?

Ask the CEO: What's Behind the AI Revolution?

 

Transcript:

Think about all the data we've been collecting. That has been a phenomenal wealth that we did not know how to use in the past.

Today, we are actually so much better at understanding data, processing it properly, and feature engineering it to extract meaningful insights.

In addition to the data revolution, of course, there was the hardware expansion — the new abilities we're seeing with better hardware everyday, and the better algorithms that are being developed every day.

But if you think about this: the cloud made something phenomenal happen. The cloud made everyone able to take that data with a credit card and process massive jobs of training and learning from that data with just a little bit of money. And that really helped a whole new breed of startups that use that those new AI technologies.

In addition to all that, the community developed this new concept over time called open source, and open source made things so much better.

We started building incrementally on top of the shoulders of giants so we learned from the past. We open source our products. We open source our code. We open source our technologies and somebody else improves a little bit on top of it and makes it a little bit better. And someone else makes that little bit better — incremental improvement. We are progressing so much faster than the past, almost exponentially.

And finally I think what's enabling this really is all the great work that is available for free by trainers that are helping millions of people learn the technology and get passionate about it and understand it better. A new generation is starting to learn about things that are significantly complex.


 

15.11.2019

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Our Most Recent Articles

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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50 AI Secrets: How Every Fortune 50 Company is Using AI Right Now

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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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