Agile Culture Data Engineering Machine Learning 
InfoQ Culture Methods
FEBRUARY 25, 2021
Machine learning is a powerful new tool, but how does it fit in your agile development? Developing ML with agile has a few challenges that new teams coming up in the space need to be prepared for - from new roles like Data Scientists to concerns in reproducibility and dependency management. By Jay Palat.
CIO
APRIL 12, 2023
Real-time AI involves processing data for making decisions within a given time frame. Real-time AI brings together streaming data and machine learning algorithms to make fast and automated decisions; examples include recommendations, fraud detection, security monitoring, and chatbots. It isn’t easy.
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CIO
FEBRUARY 14, 2023
To succeed with real-time AI, data ecosystems need to excel at handling fast-moving streams of events, operational data, and machine learning models to leverage insights and automate decision-making. report they have established a data culture 26.5% report they have a data-driven organization 39.7%
InfoQ Culture Methods
JULY 17, 2020
To mix the power of the data and the importance of people to offer business intelligence is a key point nowadays. To be agile is to adapt to today's market. The result is not only the most imporant thing, the way you do it more important. By Alejandro Ruiz.
InfoQ Culture Methods
FEBRUARY 16, 2023
Have you ever wondered about systems based on machine learning? In those cases, testing takes a backseat. And even if testing is done, it’s done mostly by developers itself. A tester’s role is not clearly portrayed. Testers usually struggle to understand ML-based systems and explore what contributions they can make.
d2iq
FEBRUARY 19, 2021
Going from a prototype to production is perilous when it comes to machine learning: most initiatives fail , and for the few models that are ever deployed, it takes many months to do so. As little as 5% of the code of production machine learning systems is the model itself. Adapted from Sculley et al.
CIO
APRIL 3, 2023
The organization now has data engineers, data scientists, and is investing in cutting-edge technologies like quantum computing. “In In a lot of ways, we felt like outsiders within our own organization, but we knew that was going to be the case, that we could usher in this new culture and organization.”
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