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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An Oracle-based approach to the “taxi fare” prediction problem – episode 2

This is the second part of the series on the Taxi Fare prediction problem from an Oracle perspective. You can read Episode 1 here. In this Episode, I will put our model to work. That is, I will show several…

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Analyzing BigQuery via Excel and Google Sheets

Both MS Excel and Google Sheets offer ways to connect directly to BQ data, to run queries, to pull data back to Excel/Sheets and allow further analysis via options such as pivot tables, charts and drilling up/down. MS Excel The…

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Azure Data Lake basics for the SQL Server DBA / developer and… for everyone!

The basics If you’re a Microsoft SQL Server DBA or developer and have not been introduced to the Microsoft Azure Data Lake and would like to understand what it’s all about and how to get started, this article is for YOU….

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Why column stores?

Introduction I’ve been working with data in many forms for my entire career. During this time, I have occasionally needed to build or query existing databases to get statistical data. Traditional databases are usually designed to query specific data from…

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Five ways to build a data-driven culture within your organization

In this post guest blogger Jim Donnelly introduces some of the concepts in our latest eBook, Analytics for Everyone, discussing what it takes to drive a data-first culture across your organization. Imagine that it’s been about a year since you…

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A comparative analysis of Amazon SageMaker and Google Datalab

Amazon SageMaker and Google Datalab have fully managed cloud Jupyter notebooks for designing and developing machine learning and deep learning models by leveraging serverless cloud engines. However, as much as they have in common, there are key differences between the…

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Exploring Amazon SageMaker

Amazon SageMaker is another cloud-based fully managed data analytics/ machine learning modeling platform for designing, building and deploying data models. The key selling point of Amazon SageMaker is “zero-setup”. The concept of “zero-setup” means data science teams can entirely focus…

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