Confluent
FEBRUARY 6, 2019
Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. It takes much more effort than just building an analytic model with Python and your favorite machine learning framework. Impedance mismatch between data scientists, data engineers and production engineers.
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. Cloud-native apps, microservices and mobile apps drive revenue with their real-time customer interactions.
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Altexsoft
MAY 27, 2020
Machine learning (ML) history can be traced back to the 1950s, when the first neural networks and ML algorithms appeared. Analysis of more than 16.000 papers on data science by MIT technologies shows the exponential growth of machine learning during the last 20 years pumped by big data and deep learning advancements.
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.
Confluent
JUNE 26, 2019
Microservices have a symbiotic relationship with domain-driven design (DDD)—a design approach where the business domain is carefully modeled in software and evolved over time, independently of the plumbing that makes the system work. In these projects, microservice architectures use Kafka as an event streaming platform. Microservices.
Altexsoft
MARCH 30, 2020
the fourth industrial revolution driven by automation, machine learning, real-time data, and interconnectivity. Similar to preventive maintenance, PdM is a proactive approach to servicing of machines. Analytical solution with machine learning capabilities.
OTS Solutions
AUGUST 16, 2023
Artificial Intelligence (AI) and Machine Learning (ML) have been at the forefront of app modernization, helping businesses to streamline workflows, enhance user experience, and improve app security measures. It can also help organizations better understand their data and make data-driven decisions.
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