Architecture Machine Learning Scalability Transportation 
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. Therefore, the majority of machine learning/deep learning frameworks focus on Python APIs.
O'Reilly Media - Data
JULY 23, 2018
Interest in machine learning (ML) has been growing steadily , and many companies and organizations are aware of the potential impact these tools and technologies can have on their underlying operations and processes. Machine Learning in the enterprise". Scalable Machine Learning for Data Cleaning.
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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. It’s also used to deploy machine learning models, data streaming platforms, and databases. That’s not to say it’ll be easy.
CIO
JANUARY 4, 2023
Another key question is whether call centers are staffed and have scalable technologies to support a massive influx of calls and communications that often happen during a crisis. Is your organization learning from past failures? How quickly can you realign operations during a crisis?
CIO
FEBRUARY 13, 2023
CIOs anticipate an increased focus on cybersecurity (70%), data analysis (55%), data privacy (55%), AI/machine learning (55%), and customer experience (53%). Besides surgery, the hospital is also investing in robotics for the transportation and delivery of medications.
Cloudera
AUGUST 11, 2022
And while operations in the cyber-domain are more likely to make the evening news, there are a vast array of critical use cases that support the military’s need for a data architecture that collects, processes, and delivers any type of data, anywhere. . Universal Data Distribution Solves DoD Data Transport Challenges.
AWS Machine Learning - AI
JANUARY 26, 2024
Understanding and addressing LLM vulnerabilities, threats, and risks during the design and architecture phases helps teams focus on maximizing the economic and productivity benefits generative AI can bring. Many customers are looking for guidance on how to manage security, privacy, and compliance as they develop generative AI applications.
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