Roger Noble

2 months ago | towardsdatascience.com | Ari Joury |Roger Noble

Python has grown to dominate data science, and its package Pandas has become the go-to tool for data analysis. It is great for tabular data and supports data files of up to 1GB if you have a large RAM. Within these size limits, it is also good with time-series data because it comes with some in-built support. That being said, when it comes to larger datasets, Pandas alone might not be enough.

Apr 29, 2024 | towardsdatascience.com | Roger Noble

Fabric Madness part 5A Huge thanks to Martim Chaves who co-authored this post and developed the example scripts. So far in this series, we’ve looked at how to use Fabric for collecting data, feature engineering, and training models. But now that we have our shiny new models, what do we do with them? How do we keep track of them, and how do we use them to make predictions? This is where MLFlow’s Model Registry comes in, or what Fabric calls an ML Model.

Apr 29, 2024 | towardsdatascience.com | Roger Noble

Fabric Madness part 5A Huge thanks to Martim Chaves who co-authored this post and developed the example scripts. So far in this series, we’ve looked at how to use Fabric for collecting data, feature engineering, and training models. But now that we have our shiny new models, what do we do with them? How do we keep track of them, and how do we use them to make predictions? This is where MLFlow’s Model Registry comes in, or what Fabric calls an ML Model.

Apr 15, 2024 | towardsdatascience.com | Roger Noble

Fabric Madness part 3In the previous post, we discussed how to use Notebooks with PySpark for feature engineering. While spark offers a lot of flexibility and power, it can be quite complex and requires a lot of code to get started. Not everyone is comfortable with writing code or has the time to learn a new programming language, which is where Dataflow Gen2 comes in.

Apr 15, 2024 | towardsdatascience.com | Roger Noble

Fabric Madness part 3In the previous post, we discussed how to use Notebooks with PySpark for feature engineering. While spark offers a lot of flexibility and power, it can be quite complex and requires a lot of code to get started. Not everyone is comfortable with writing code or has the time to learn a new programming language, which is where Dataflow Gen2 comes in.