Flask website template that includes a spot for blogs, dash applications, and Jupyter notebooks — I wanted to create my own website that would allow me to create a blog, showcase my dashboards, and allow me to run jupyter notebooks. I found out how to create the Dash application itself, but I was unable to find one that describes all three. …

Want consistent, reliable data? Hire more data engineers and implement data observation. I took a deep breath as I had to fix one more report on a Friday afternoon. Again. The sole data engineer at a small start-up, I supported the company and fixed its data problems. We often forget…

Platforms, databases, and languages you will most likely see in the future according to data developers Introduction Stack Overflow is an awesome developer community. There is no denying it. And the best part is that that we all use it one way or another. I was able to find some of…

Make your data pipeline orchestration future-proof with Prefect. — I just started a new position at a great company and we are re-evaluating our options. To provide some context, we are in Azure and are using Azure Data Factory. We are not too happy with this setup as we do not have extensive historical data and the monitoring has…

Simplifying your MLOps stack with MLFlow and BentoML — Developing machine learning models takes time. In Algorithimia’s 2021 Enterprise Trends In Machine Learning report, they observed that 64% of these organizations took at least a month to deploy their models.¹ Shrinking time to at least 50% will empower data scientists to make space for them, so they can create…

Training, predicting, and serving all in one easy process using BentoML Motivation Many data scientists know how to train and optimize their models. However, getting those models into production is a different story. These models must preprocess data, train, deploy, and then handle the scale of the requests. BentoML addresses these…

The third and fourth lessons might surprise you. Working at a startup is not for everyone. Ideas are pitched, teams are put together, and products are launched at the drop of a hat. I am so glad I had the opportunity to be a part of a couple of startups…

A guide on how you can solve your data quality problem using Python. — Bad data quality sucks. It causes our reports to be inaccurate and drives our data scientists and engineers to pull their hair out. But most importantly, it causes the trust between the data providers and consumers to deteriorate. When we finally get around to fixing at least some of the…