Cloudera
NOVEMBER 19, 2021
We are excited by the endless possibilities of machine learning (ML). We recognise that experimentation is an important component of any enterprise machine learning practice. Continuous Operations for Production Machine Learning (COPML) helps companies think about the entire life cycle of an ML model.
Cloudera
SEPTEMBER 22, 2022
To me, this means that by applying more data, analytics, and machine learning to reduce manual efforts helps you work smarter. Incorporating these new data sources into the underwriting process does not have to be a massive overhaul of infrastructure that takes years to roll out. Step two: expand machine learning and AI.
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Cloudera
MAY 5, 2022
Namely, agency leaders and staff lack the modernized data infrastructure and tooling needed to fight FWA with such massive amounts of money and data flowing through their agencies. These platforms become even more powerful when integrated with AI and machine learning, as well as other forms of automation.
IDC
JUNE 24, 2019
Automated Machine Learning. Automated machine learning (AutomML) is the automation of the end-to-end process of applying machine learning (ML) to real-world problems. Likewise, AI gets embedded in enterprise infrastructure for intelligence and self-management. Embedded AI.
Lacework
MARCH 16, 2022
Attackers know that gaming companies are reliant on a complex web of cloud infrastructure that must stay online in order to stay in business. By using a combination of AI and machine learning, Lacework continuously monitors for anomalous behavior, alerting to those activities unusual for your cloud environment. No rules, rules!
Palo Alto Networks
OCTOBER 19, 2023
These vulnerabilities range from adapting legacy security tools for cloud usage to potential misconfigurations in cloud infrastructure. The sudden surge of interest in generative AI and machine learning has validated the growing concern about novel technologies in the wrong hands.
DataRobot
NOVEMBER 15, 2022
Closing the value gap and reducing the overall AI cycle time means addressing the individual needs of each stakeholder group within the machine learning lifecycle. Machine Learning stacks are commonly fragmented and hard to manage across departments, creating complexity and cost that can inhibit scale and slow down progress.
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