Data Collection Modeling Statistics 
O'Reilly on Data
MARCH 24, 2020
In a world focused on buzzword-driven models and algorithms, you’d be forgiven for forgetting about the unreasonable importance of data preparation and quality: your models are only as good as the data you feed them. The model and the data specification become more important than the code.
Analytics Vidhya
JULY 9, 2022
This article was published as a part of the Data Science Blogathon. Introduction In order to build machine learning models that are highly generalizable to a wide range of test conditions, training models with high-quality data is essential.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
O'Reilly on Data
JUNE 18, 2019
There has been a significant increase in our ability to build complex AI models for predictions, classifications, and various analytics tasks, and there’s an abundance of (fairly easy-to-use) tools that allow data scientists and analysts to provision complex models within days. Data integration and cleaning.
O'Reilly on Data
NOVEMBER 13, 2018
Considerations for a world where ML models are becoming mission critical. In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in New York last September. As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations.
Rocket-Powered Data Science
MARCH 19, 2021
Focus on the strategies that aim these tools, talents, and technologies on reaching business mission and goals: e.g., data strategy, analytics strategy, observability strategy ( i.e., why and where are we deploying the data-streaming sensors, and what outcomes should they achieve?).
O'Reilly on Data
MARCH 31, 2020
All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data. After training, the system can make predictions (or deliver other results) based on data it hasn’t seen before. Machine learning adds uncertainty.
O'Reilly on Data
MARCH 18, 2020
Whether it’s controlling for common risk factors—bias in model development, missing or poorly conditioned data, the tendency of models to degrade in production—or instantiating formal processes to promote data governance, adopters will have their work cut out for them as they work to establish reliable AI production lines.
Expert insights. Personalized for you.
364 
Let's personalize your content