Home

Picture this: A data scientist spends hours configuring YAML files, debugging pipeline errors, and switching between five different tool UIs just to test a simple feature idea. Meanwhile, an analyst in Excel has already cleaned their data, built a model, and presented insights to stakeholders. Something’s wrong with this picture – and it’s not t...

When data infrastructure struggles to keep pace with business needs, the impact ripples throughout an organization. As Lead Machine Learning Engineer at Mindstrong, I encountered this firsthand when our analytics infrastructure began hampering executive decision-making capabilities. The Challenge: Data Delays Crippling Decision Making Our data...