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Make Your Models Matter: What It Takes to Maximize Business Value from Your Machine Learning Initiatives

Cloudera

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. Organizations need to usher their ML models out of the lab (i.e., Organizations need to usher their ML models out of the lab (i.e.,

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10 Steps to Achieve Enterprise Machine Learning Success

Cloudera

You’ve probably heard it more than once: Machine learning (ML) can take your digital transformation to another level. Unfortunately, most organizations run into trouble when it comes to bridging the gap that exists between experimentation and full-scale ML production. Step 6: Evolve your organization to embrace ML.

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The Ever-Growing Importance of Machine Learning Operations

DataRobot

AI and machine learning initiatives are the gifts that keep on giving, simultaneously increasing top-line revenue and decreasing bottom-line costs. But to meet this scale in demand, organizations have to navigate a myriad of new challenges, from IT governance and security, to data security, privacy, and tax regulatory compliance.

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Making the World a Better Place with Data

CIO

To achieve this vision, agencies must modernize and optimize how they collect, organize, manage, analyze, and act on that data—in an open manner that fosters trust and accountability with citizens, partners, and other stakeholders. You can learn more in our interactive ebook, “Data in Motion to Accelerate Your Mission.”.

Data 246
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10 Keys to AI Success in 2021

But it’s not always easy for organizations to do. In our 10 Keys to AI Success in 2021 eBook, we draw from the engaging conversations we’ve had with guests on our More Intelligent Tomorrow podcast series to show how organizations are overcoming hurdles and realizing the enormous rewards that AI can bring to any organization.

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Making Remarkable Energy Grids a Reality

CIO

These changes bring new challenges, but advancements in IT automation, artificial intelligence (AI) and machine learning (ML), and edge-computing capabilities will play a key role. Read our latest eBook and view our energy webpage to learn more about exciting advancements in energy.

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Not Getting the Most From Your Model Ops? Why Businesses Struggle With Data Science and Machine Learning

TIBCO - Connected Intelligence

Companies have begun to recognize the value of integrating data science (DS) and machine learning (ML) across their organization to reap the benefits of the advanced analytics they can provide. Why are so few organizations able to follow through with model ops adoption? Reading Time: 2 minutes. The Devil’s in the Data.

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5 Things a Data Scientist Can Do to Stay Current

With the number of available data science roles increasing by a staggering 650% since 2012, organizations are clearly looking for professionals who have the right combination of computer science, modeling, mathematics, and business skills. Fostering collaboration between DevOps and machine learning operations (MLOps) teams.

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Intelligent Process Automation: Boosting Bots with AI and Machine Learning

But in order to reap the rewards of Intelligent Process Automation, organizations must first educate themselves and prepare for the adoption of IPA. In Data Robot's new ebook, Intelligent Process Automation: Boosting Bots with AI and Machine Learning, we cover important issues related to IPA, including: What is RPA?

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ERP Migration: Why Data Quality Comes First

We are living through a fundamental transformation in the way we work, and the way that organizations function. Automation and machine learning are augmenting human intelligence, tasks, jobs, and changing the systems that organizations need in order not just to compete, but to function effectively and securely in the modern world.