Snorkel AI

JULY 5, 2023

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. The second is that it can be really hard to classify and catalog data assets for discovery.

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52

The MLOps Blog

APRIL 17, 2023

Building a tool for managing experiments can help your data scientists; 1 Keep track of experiments across different projects, 2 Save experiment-related metadata, 3 Reproduce and compare results over time, 4 Share results with teammates, 5 Or push experiment outputs to downstream systems.

Metadata 52

Metadata Data Scientist Explainability ML 52

Sebastian Ruder

NOVEMBER 15, 2023

Human-written examples are more expensive to obtain but are more high quality. Data quality:  Was any filtering done to improve the quality of the generated data? In most cases, filtering is based on simple heuristics or a pre-trained model, which can result in noisy data. Improving data quality.  To

NLP 52

NLP ChatGPT Data Quality OpenAI 52

Pickl AI

APRIL 14, 2023

With the exponential growth of data and increasing complexities of the ecosystem, organizations face the challenge of ensuring data security and compliance with regulations. Data Processes and Organizational Structure Data Governance access controls enable the end-users to see how data processing works inside an organization.

Data Platform 52

Data Platform Data Integration Data Ingestion Automation 52

AWS Machine Learning Blog

FEBRUARY 14, 2024

Labeling mistakes are important to identify and prevent because model performance for pose estimation models is heavily influenced by labeled data quality and data volume. This custom workflow helps streamline the labeling process and minimize labeling errors, thereby reducing the cost of obtaining high-quality pose labels.

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Python Computer Vision Data Scientist Machine Learning 122

The MLOps Blog

AUGUST 3, 2023

And then, we’re trying to boot out features of the platform and the open-source to be able to take Hamilton data flow definitions and help you auto-generate the Airflow tasks. To a junior data scientist, it doesn’t matter if you’re using Airflow, Prefect , Dexter. I term it as a feature definition store.

ML 52

ML Data Scientist Software Engineer Machine Learning 52

Mlearning.ai

DECEMBER 21, 2023

Things to Keep in Mind Ensure data quality by preprocessing it before determining the optimal chunk size. Examples include removing HTML tags or eliminating specific elements that contribute noise, particularly when data is sourced from the web. A word embedding is a vector representation of words. Precise Similarity Search.

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Large Language Models LLM OpenAI ChatGPT 94

The MLOps Blog

MARCH 15, 2023

Cost and resource requirements There are several cost-related constraints we had to consider when we ventured into the ML model deployment journey Data storage costs: Storing the data used to train and test the model, as well as any new data used for prediction, can add to the cost of deployment. S3 buckets.

ETL 52

ETL Data Drift Machine Learning ML 52

Sebastian Ruder

JUNE 6, 2022

Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets (Kreutzer et al.). It conducts a careful audit of large-scale multilingual datasets covering 70 languages and identifies many data quality issues that have previously gone unnoticed. I wrote about this paper when it first came out.

NLP 52

NLP Natural Language Processing Computational Linguistics Neural Network 52

The MLOps Blog

MARCH 21, 2023

To make that possible, your data scientists would need to store enough details about the environment the model was created in and the related metadata so that the model could be recreated with the same or similar outcomes. Your ML platform must have versioning in-built because code and data mostly make up the ML system.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69

Pickl AI

JUNE 11, 2024

They offer a focused selection of data, allowing for faster analysis tailored to departmental goals. Metadata This acts like the data dictionary, providing crucial information about the data itself. Metadata details the source of the data, its definition, and how it relates to other data points within the warehouse.

ETL 52

ETL Data Mining Actionable Intelligence Data Integration 52