ODSC - Open Data Science
OCTOBER 11, 2023
Machine learning models are only as good as the data they are trained on. Even with the most advanced neural network architectures, if the training data is flawed, the model will suffer. Data issues like label errors, outliers, duplicates, data drift, and low-quality examples significantly hamper model performance.
DataRobot Blog
MARCH 10, 2022
With Snowflake’s newest feature release, Snowpark , developers can now quickly build and scale data-driven pipelines and applications in their programming language of choice, taking full advantage of Snowflake’s highly performant and scalable processing engine that accelerates the traditional data engineering and machine learning life cycles.
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AWS Machine Learning Blog
MARCH 1, 2023
Challenges In this section, we discuss challenges around various data sources, data drift caused by internal or external events, and solution reusability. For example, Amazon Forecast supports related time series data like weather, prices, economic indicators, or promotions to reflect internal and external related events.
The MLOps Blog
JUNE 27, 2023
For example, if your team is proficient in Python and R, you may want an MLOps tool that supports open data formats like Parquet, JSON, CSV, etc., Some of its features include a data labeling workforce, annotation workflows, active learning and auto-labeling, scalability and infrastructure, and so on.
Topbots
JUNE 29, 2023
However, as of now, unleashing the full potential of organisational data is often a privilege of a handful of data scientists and analysts. Most employees don’t master the conventional data science toolkit (SQL, Python, R etc.). The manual collection of training data for Text2SQL is particularly tedious.
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