Rocket-Powered Data Science

FEBRUARY 15, 2023

While generative AI has been around for several years , the arrival of ChatGPT (a conversational AI tool for all business occasions, built and trained from large language models) has been like a brilliant torch brought into a dark room, illuminating many previously unseen opportunities.

Strategy 289

Strategy Experimentation Uncertainty Machine Learning 289

DataKitchen

OCTOBER 2, 2023

.’ What’s a Data Journey? Data Journeys track and monitor all levels of the data stack, from data to tools to code to tests across all critical dimensions. A Data Journey supplies real-time statuses and alerts on start times, processing durations, test results, and infrastructure events, among other metrics.

Data Quality 100

Data Quality Testing Uncertainty Data Enablement 100

O'Reilly on Data

MARCH 31, 2020

Instead of writing code with hard-coded algorithms and rules that always behave in a predictable manner, ML engineers collect a large number of examples of input and output pairs and use them as training data for their models. Machine learning adds uncertainty. Models also become stale and outdated over time.

Management 332

Management Machine Learning Experimentation Metrics 332

Cloudera

JULY 15, 2021

In this session we explored what firms are doing to approach the uncertainty with more predictability. Pandemic “Pressure” Testing. However, through this real-time “pressure test”, they identified areas of weakness, dependencies, and opportunities. Observe what the model has to offer even if not the intended output.

Risk 98

Risk Optimization Management Insurance 98

DataRobot

JULY 16, 2021

The first is trust in the performance of your AI/machine learning model. They all serve to answer the question, “How well can my model make predictions based on data?” So, we ask, what recommendations and assessments can you use to verify the origin and quality of the data used? How large is the data set?

Machine Learning 98

Machine Learning Uncertainty Modeling Measurement 98

DataRobot

JUNE 1, 2021

In an earlier post, I shared the four foundations of trusted performance in AI : data quality, accuracy, robustness and stability, and speed. Before you put a model into production, you may first need to clear compliance hurdles. Typically, these areas require the most attention: model development, implementation, and use.

Uncertainty 52

Uncertainty Machine Learning Advertising Risk 52

DataKitchen

SEPTEMBER 21, 2023

Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability In a world where 97% of data engineers report burnout and crisis mode seems to be the default setting for data teams, a Zen-like calm feels like an unattainable dream.

Testing 176

Testing Data Quality Predictive Modeling Metrics 176