🚀 Want to write production-grade ML code that’s clean, modular & maintainable?

Here’s a simple CPO (components, pipelines, orchestrators) pattern example to structure your code.

Let's break this down with a training pipeline example👇
#MachineLearning #DataScience #MLOps

🚀 Many newbies jump straight to feature eng and training ML models... then find models underperform.

Why? They skip Exploratory Data Analysis (EDA).
EDA is an important step for performance and explainability.

Here’s how to start👇
#DataAnalytics #100DaysOfML #MachineLearning

On cloud platforms there are a number of different ways to store your data. Given that it can often be confusing to know what to know how best to organise your data.

Here's a couple of tips for working with blob storage, data lakes and sql servers:

#azure #dataengineering

Personally, I’m not a fan of building things with certain frameworks or patterns just because company x or developer y does it that way. Prefer to use as ideas to try, see the pros and cons and then use where it makes sense rather than following blindly.

The more I watch or read about successful people, the more I see that:

- They build things they want to use
- They are smart contrarians
- They don’t assume something will or won’t work, they experiment and adjust.

Also helps if you have rich people in your network😬