Author: Carlos Timoteo

How to deploy machine learning on Google Cloud Platform

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…

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Making a business case for Machine Learning

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…

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An overview of best practices for implementing ML systems – Part 1

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…

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How to Implement Airflow Best Practices from a Data Scientist’s perspective – Part 1

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….

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Comparing Data Science at an AI Startup and a Consulting Company

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….

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