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Data Digest: Using AI/ML in Hiring, Research, and Healthcare

Avoiding machine learning bias, improving machine learning documentation, and a case study for AI benefits in a hospital setting.

Finding Talent on the Periphery

Now is a great time to find data science talent by expanding your search from the mainstream to the periphery.

Self-Supervised Learning's Impact on AI and NLP

Although humans can learn from observing a few examples of a given task, ML algorithms cannot. They can, however, learn through self-supervised learning. We explain how self-supervised learning works.

Data Digest: Trends for Graph Databases, Data Science, and Data Quality

Graph database predictions, data science trends, and requirements for good data quality.

Data Digest: Improving Data Science Processes, Data Visibility, and Data Architecture

Strategy advice for data scientists, the importance of data observability, and the elements of a modernized data architecture.

Data Digest: Healthcare Advances with ML and AI

These articles explain recent advances in predicting zoonotic diseases, improving patient health, and targeting personalized care.

Data Digest: Pitfalls in Data Democratization, Data Access, Common Paradoxes

Why to carefully consider the speed of data democratization, tips for making data more accessible, and paradoxes for data scientists to beware of.

DataSecOps: Applying the DevSecOps Mindset to Data Security

Organizations that adapt quickly and develop a strong DataSecOps approach with clearly communicated processes can accelerate their data-driven innovation.

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