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OUR LATEST ARTICLES

Using Datastudio to Map the CoronaVirus

As misinformation and rumor continues to swirl around the spread of the Covid-19 pandemic, we wanted to create a series of charts to see if it would be possible to identify any previously-unnoticed trends. By combining health data from the WHO, climate data from NOAA, and World Development Indicators from the World Bank, we were able to discover some interesting correlations.

Some Interesting Discoveries —

• America has more coronavirus cases than any other country on earth. However, this is mainly due to the fact that America is larger. Our per-capita case numbers are roughly equivalent to other western countries in Europe and our Corona-related death rate is actually lower than those of developed European nations.

• Many have theorized that warmer temperatures will lead to a reduction in confirmed cases; however, by banding confirmed cases by average temperature, we were unable to identify a relationship between weather and new cases.

• The average mortality rate across all nations is roughly the same, regardless of each country's wealth. If anything, the world's poorest countries are having better health outcomes than wealthier nations. This may, in part, be skewed by the fact the wealthier nations are testing more of its citizens.

•Wealthier nations tend to travel more, which may partially explain why they are seeing higher cases of coronavirus. We can see this across multiple charts, including charts that correlate over-all life expectancy with corona mortality rates.

Explore the Coronavirus charts here. →

16.04.2020

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Our Most Recent Articles

Video: Machine Learning Engineering with Tensorflow Extended

Video: How to Build a Reproducible ML Pipeline

Video: ML adventures with AutoML and TFHub

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

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OUR LATEST ARTICLES

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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