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Whitepapers Datanami's white paper database contains reports from the leading thought-leaders and idea generators in the Datanami industry.

Responsible Machine Learning: Actionable Strategies for Mitigating Risks & Driving Adoption

Source: H2O
Release Date: Oct 22, 2020

Like other powerful technologies, AI and machine learning present significant opportunities. To reap the full benefits of ML, organizations must also mitigate the considerable risks it presents. This report outlines a set of actionable best practices for people, processes, and technology that can enable organizations to innovate with ML in a responsible manner. Read more…

Making Alternative Credit Scores the Norm: How to Create a New Scoring Model

Source: Explorium
Release Date: Oct 22, 2020

The way we currently measure potential borrowers’ and other customers’ creditworthiness is broken. The credit scores that have become the gold standard are narrow, opaque, and easily manipulated measures that reward financial risk-taking and ignore responsible behaviors. Read more…

Analyze-then-Store: The Journey to Continuous Intelligence

Source: Sponsored Content by Swim
Release Date: Oct 7, 2020

“Analyze-then-Store: The Journey to Continuous Intelligence” is a technical eBook intended for data architects and anyone else interested in learning how to design modern real-time data analytics and continuous intelligence solutions. Read more…

Guide to Maximum Data Lake Value

Source: Qlik
Release Date: Sep 28, 2020

In this whitepaper, Eckerson Group discusses how to get maximum value from data lakes and how Qlik’s Data Integration Platform helps businesses get the most value out of their data lakes quickly, accurately, and with the agility to respond to shifting business needs. Read more…

The Essential Guide to Feature Selection

Source: Explorium
Release Date: Sep 4, 2020

Feature selection is a key step in building powerful and interpretable machine learning models, but it’s also one of the easiest to get wrong. The wrong features will give you inaccurate answers and may impact your ML models’ efficiency in ways you can’t predict. Read more…

How to Improve Your Training Data for Vastly Better Machine Learning

Source: Explorium
Release Date: Aug 13, 2020

Your machine learning models are only as good as the data you’re using to train and test them. So, how can you improve your datasets? This guide breaks down simple strategies to acquire better data and quick approaches and methods to fine-tune and manipulate your existing data will get you better testing results and insights. Read more…

8 Key Considerations for AI in the Enterprise

Source: H2O
Release Date: Jul 9, 2020

If you’re developing or thinking of developing an AI strategy to transform your business, there’s a lot to consider, let us help. We’re the creator of the leading open source machine learning and artificial intelligence platform and our vision is to democratize AI for all and empower every company to be an AI company. Read more…

AI is Making BI Obsolete, and Machine Learning is Leading the Way

Source: Explorium
Release Date: Jul 2, 2020

BI has become a must-get for any company, and while it does offer some great value, what are you really getting from it? Although BI is great at visualizing your data and giving you digestible reports, it’s hard to make predictions and automate your insights to really optimize your operations. Read more…

Making the Most of Your Investment in Hadoop

Source: Sqream
Release Date: Jun 5, 2020

Hadoop is a popular enabler for big data. But with data volumes growing exponentially, analytics have become restricted and painfully slow, requiring arduous data preparation. Often, querying weeks, months, or years of data is simply infeasible, and organizations succeed in analyzing only a fraction of their data. Read more…

Top Cloud Data Warehouses for the Enterprise

Source: Qlik
Release Date: May 29, 2020

Modern cloud architectures combine three essentials: the power of data warehousing, flexibility of Big Data platforms, and elasticity of cloud at a fraction of the cost to traditional solution users. Read more…

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