The MLOps Blog

JUNE 27, 2023

Learn more The Best Tools, Libraries, Frameworks and Methodologies that ML Teams Actually Use – Things We Learned from 41 ML Startups [ROUNDUP] Key use cases and/or user journeys Identify the main business problems and the data scientist’s needs that you want to solve with ML, and choose a tool that can handle them effectively.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

AWS Machine Learning Blog

JUNE 23, 2023

Amazon SageMaker Data Wrangler is a single visual interface that reduces the time required to prepare data and perform feature engineering from weeks to minutes with the ability to select and clean data, create features, and automate data preparation in machine learning (ML) workflows without writing any code.

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Auto-complete Auto-classification ML Data Quality 73

AWS Machine Learning Blog

MAY 3, 2023

Feature engineering refers to the process where relevant variables are identified, selected, and manipulated to transform the raw data into more useful and usable forms for use with the ML algorithm used to train a model and perform inference against it. The final outcome is an auto scaling, robust, and dynamically monitored solution.

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Auto-classification Auto-complete Machine Learning Metadata 67

AWS Machine Learning Blog

JULY 31, 2023

It also enables you to evaluate the models using advanced metrics as if you were a data scientist. In this post, we show how a business analyst can evaluate and understand a classification churn model created with SageMaker Canvas using the Advanced metrics tab.

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Auto-classification Machine Learning ML Auto-complete 77

Pickl AI

MARCH 10, 2023

Data Pre-processing is a necessary Data Science process because it helps improve the accuracy and reliability of data. Furthermore, it ensures that data is consistent while effectively increasing the readability of the data’s algorithm.

Data Scientist 52

Data Scientist Data Science Data Integration Auto-classification 52

Snorkel AI

FEBRUARY 27, 2023

So rather than just clicking and labeling one data point at a time, like playing 20,000 questions with a machine-learning model that then has to re-infer all that rich knowledge that was in your head, why not just express it directly to inject that domain knowledge? This could be something really simple.

Machine Learning 52

Machine Learning AI AI Large Language Models 52

Snorkel AI

FEBRUARY 27, 2023

So rather than just clicking and labeling one data point at a time, like playing 20,000 questions with a machine-learning model that then has to re-infer all that rich knowledge that was in your head, why not just express it directly to inject that domain knowledge? This could be something really simple.

Machine Learning 52

Machine Learning AI AI Large Language Models 52