ODSC - Open Data Science

MARCH 13, 2023

Machine learning practitioners are often working with data at the beginning and during the full stack of things, so they see a lot of workflow/pipeline development, data wrangling, and data preparation.

Machine Learning 94

Machine Learning Machine Learning Data Wrangling Data Science 94

Towards AI

JUNE 27, 2023

This article is part of the AWS SageMaker series for exploration of ’31 Questions that Shape Fortune 500 ML Strategy’. To prepare the data for models, a data scientist often needs to transform, clean, and enrich the dataset. This section will focus on running transformations on our transaction data.

AWS 52

AWS Data Scientist Data Wrangling Data Preparation 52

ODSC - Open Data Science

JANUARY 29, 2024

Open Source ML/DL Platforms: Pytorch, Tensorflow, and scikit-learn Hiring managers continue to favor the most popular open-source machine/deep learning platforms including Pytorch, Tensorflow, and scikit-learn. It’s a pre-trained model capable of various tasks like text classification, question answering, and sentiment analysis.

Data Science 52

Data Science Machine Learning Machine Learning Natural Language Processing 52

Snorkel AI

APRIL 13, 2023

The natural language interface enables a wide audience of both ML and non-ML experts to engage with the models. We can’t send private data such as medical records to an API, and therefore we need small open-source models to improve the feasibility of our proposal. We’re super excited by their potential.

Data Wrangling 52

Data Wrangling Machine Learning Machine Learning ML 52

Snorkel AI

APRIL 13, 2023

The natural language interface enables a wide audience of both ML and non-ML experts to engage with the models. We can’t send private data such as medical records to an API, and therefore we need small open-source models to improve the feasibility of our proposal. We’re super excited by their potential.

Data Wrangling 52

Data Wrangling Machine Learning Machine Learning ML 52

Becoming Human

MAY 15, 2023

There is a position called Data Analyst whose work is to analyze the historical data, and from that, they will derive some KPI s (Key Performance Indicators) for making any further calls. For Data Analysis you can focus on such topics as Feature Engineering , Data Wrangling , and EDA which is also known as Exploratory Data Analysis.

Data Science 75

Data Science Machine Learning Machine Learning Database 75

The MLOps Blog

OCTOBER 20, 2023

Nevertheless, many data scientists will agree that they can be really valuable – if used well. 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. in a pandas DataFrame) but in the company’s data warehouse (e.g., Redshift).

SQL 52

SQL Database Data Scientist Python 52