Google and O’Reilly came together to present TensorFlow World 2019. TensorFlow World is the first conference for the TensorFlow community. For those who are not familiar with it, TensorFlow is an intensive computing framework designed to build, train and serve…Read More >
Pythian would like to announce Diamond sponsorship of the first cloud and machine learning (ML) DevFest in Montreal, September 27th, at École de Technologie Supérieure (ÉTS). More details can be found on the website: https://devfest.cloud-montreal.ca/. DevFests are large-scale community-driven events…Read More >
In this blog post, I will describe a few takeaways on how to deploy or submit Machine Learning (ML) tasks on Google Cloud Platform (GCP). If you have less experience as a ML engineer or if you are a solution…Read More >
The first step to kick off a Machine Learning (ML) project is to have a written proposition for the business problem, and second, to frame the ML problem. Before even discussing an ML method, it is necessary first to understand…Read More >
In this series of blog posts, we will recommend some best practices identified from our own failures and successes throughout our time implementing machine learning (ML) systems. We won’t discuss ML techniques here, but instead, provide an upper-level overview of…Read More >
This blog post is a compilation of suggestions for best practices drawn from my personal experience as a data scientist building Airflow DAGs and installing and maintaining Airflow. Let’s begin by explaining what Airflow is and what it is not….Read More >
The difference between data science consultants and a data science team at a company depending on machine learning (ML) solutions is significant. In the first instance, the consultants need to have a good understanding of both the business and technology….Read More >
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