ODSC - Open Data Science

APRIL 6, 2023

For budding data scientists and data analysts, there are mountains of information about why you should learn R over Python and the other way around. Though both are great to learn, what gets left out of the conversation is a simple yet powerful programming language that everyone in the data science world can agree on, SQL.

Data Scientist 98

Data Scientist Data Analysis Data Science Python 98

AWS Machine Learning Blog

MARCH 1, 2023

In addition to the challenge of defining the features for the ML model, it’s critical to automate the feature generation process so that we can get ML features from the raw data for ML inference and model retraining. The ETL pipeline, MLOps pipeline, and ML inference should be rebuilt in a different AWS account.

Automation 87

Automation ETL Data Drift ML 87

AWS Machine Learning Blog

APRIL 16, 2024

Jupyter notebooks can differentiate between SQL and Python code using the %%sm_sql magic command, which must be placed at the top of any cell that contains SQL code. This command signals to JupyterLab that the following instructions are SQL commands rather than Python code. They can also be written to a pandas DataFrame.

Data Scientist 90

Data Scientist Generative AI ML Machine Learning 90

Marktechpost

JULY 23, 2023

Python and SQL are supported languages for machine learning research. It’s perfect for deriving real-time business intelligence from extensive data analysis. IBM Infosphere The good ETL tool IBM Infosphere carries out data integration tasks using graphical notations.

Machine Learning 52

Machine Learning ETL Big Data Data Integration 52

The MLOps Blog

OCTOBER 20, 2023

And that’s what we’re going to focus on in this article, which is the second in my series on Software Patterns for Data Science & ML Engineering. I’ll show you best practices for using Jupyter Notebooks for exploratory data analysis. When data science was sexy , notebooks weren’t a thing yet.

Data Scientist 52

Data Scientist Python Explainability ETL 52

The MLOps Blog

DECEMBER 7, 2022

May be useful Best Workflow and Pipeline Orchestration Tools: Machine Learning Guide Phase 1—Data pipeline: getting the house in order Once the dust was settled, we got the Architecture Canvas completed, and the plan was clear to everyone involved, the next step was to take a closer look at the architecture. What’s in the box?

ML 52

ML ETL Data Scientist Automation 52

The MLOps Blog

MAY 31, 2023

In this section, I will talk about best practices around building the Data Processing platform. The objective of this platform is to preprocess, prepare and transform the data so that it’s ready for model training. are present in the data. So, this platform can leverage the JupyterLab environment to write code and execute.

ML 59

ML Algorithm Data Drift Data Platform 59