Engineering

Mage is an open-source data pipeline tool for transforming and integrating data. Find out how Mage compares to ELT companies like Fivetran. We’ll break down the features, pricing, pros, cons, and more.

Data integration is synchronizing information between 1 system (e.g. Salesforce) and another (e.g Snowflake).

Accuracy tells overall correctness. Precision is specific to a category. Recall tells you successful detection of a specific category.

Technical design documents are detailed game plans for features or solutions to technical problems. They end up saving your team a lot of time and code.

A machine learning (ML) project requires collaboration across many roles. Here are high level steps in the end-to-end ML lifecycle for each role they play.

A process to create, review, and merge pull requests is a must when working as a team. Communication and collaboration effectively gets the job done.

Structure your interview process to emulate what it’s like to work together on your team and a typical day on the job.

We raised money. You might be thinking “so what, everyone else did as well”. I’ll try not to make this another fundraising announcement.

Having a continuous integration/continuous delivery pipeline greatly speeds up the process of testing, building, and deploying without sacrificing quality.

Without good performance, machine learning (ML) models won’t provide much value. Introducing, strategies to improve model performance metrics and runtime.