Measurement Modeling Predictive Modeling 
David Menninger's Analyst Perspectives
JANUARY 15, 2025
ISG Research asserts that by 2027, one-third of enterprises will incorporate comprehensive external measures to enable ML to support AI and predictive analytics and achieve more consistently performative planning models. A robust dataset is also valuable because predictions are almost always inaccurate.
O'Reilly on Data
MARCH 20, 2019
Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. This is like a denial-of-service (DOS) attack on your model itself.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
O'Reilly on Data
DECEMBER 12, 2019
Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]
O'Reilly on Data
SEPTEMBER 15, 2020
In my book, I introduce the Technical Maturity Model: I define technical maturity as a combination of three factors at a given point of time. Technical sophistication: Sophistication measures a team’s ability to use advanced tools and techniques (e.g., PyTorch, TensorFlow, reinforcement learning, self-supervised learning).
datapine
NOVEMBER 27, 2019
While we work on programs to avoid such inconvenience , AI and machine learning are revolutionizing the way we interact with our analytics and data management while increment in security measures must be taken into account. Any difference between predicted data and real value is used by the moving average (MA) part.
DataRobot Blog
JULY 22, 2022
Using AI-based models increases your organization’s revenue, improves operational efficiency, and enhances client relationships. You need to know where your deployed models are, what they do, the data they use, the results they produce, and who relies upon their results. That requires a good model governance framework.
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.
Expert insights. Personalized for you.
130 
Let's personalize your content