integrating-machine-learning-models-into-your-big-data-pipelines-in-real-time-with-no-coding 
TechCrunch
MARCH 30, 2022
Building: A turnkey data warehouse for images and video. Eventual ingests, organizes and processes imaging data all on one platform. Also handles queries, simple integration with cloud providers and smart scheduling to save costs on compute. Includes real-time alerts, automated reports and photo-based quality control.
Apiumhub
MARCH 12, 2024
To summarize, it’s more or less when you store data and programs on a cloud instead of storing it on the hard drive of a computer. What do you need to access the data? Rather than relying solely on centralized data centers, edge computing distributes computational processes closer to the source of data generation.
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Xebia
JANUARY 19, 2023
The following is a review of the book Fundamentals of Data Engineering by Joe Reis and Matt Housley, published by O’Reilly in June of 2022, and some takeaway lessons. This book is as good for a project manager or any other non-technical role as it is for a computer science student or a data engineer.
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.
Altexsoft
AUGUST 25, 2021
Humans have been trying to make machines chat for decades. Today, we converse with virtual companions all the time. But despite failing to understand us in some instances, machines are extremely good in making sense of our talking and writing in other examples. Keep reading to learn: What problems NLP can help solve.
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. AI is everywhere.
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.
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