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.
AWS Machine Learning Blog
NOVEMBER 9, 2023
Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is essential for seamlessly bridging the gap between data science experimentation and deployment while meeting the requirements around model performance, security, and compliance.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
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., Can you see the complete model lineage with data/models/experiments used downstream? and Pandas or Apache Spark DataFrames. Is it fast and reliable enough for your workflow?
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.
Expert insights. Personalized for you.
52 
Let's personalize your content