Remove 2020 Remove Data Science Remove Experimentation
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AI adoption in the enterprise 2020

O'Reilly on Data

It seems as if the experimental AI projects of 2019 have borne fruit. In 2020, as in 2019, a plurality of respondents—almost 22%—identified a lack of institutional support as the biggest problem. In both 2019 and 2020, the AI skills gap actually occupied the No. But what kind? Where AI projects are being used within companies.

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Top 10 Data Innovation Trends During 2020

Rocket-Powered Data Science

The year 2020 was remarkably different in many ways from previous years. In at least one way, it was not different, and that was in the continued development of innovations that are inspired by data. This steady march of data-driven innovation has been a consistent characteristic of each year for at least the past decade.

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9 IT resolutions for 2025

CIO Business Intelligence

Innovate Shane McDaniel, CIO for the City of Seguin, Texas, says his city has grown by about 35% since the 2020 census. One of them is Katherine Wetmur, CIO for cyber, data, risk, and resilience at Morgan Stanley. I firmly believe continuous learning and experimentation are essential for progress.

IT 140
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The DataOps Vendor Landscape, 2021

DataKitchen

Piperr.io — Pre-built data pipelines across enterprise stakeholders, from IT to analytics, tech, data science and LoBs. Prefect Technologies — Open-source data engineering platform that builds, tests, and runs data workflows. Genie — Distributed big data orchestration service by Netflix.

Testing 304
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6 Data And Analytics Trends To Prepare For In 2020

Smart Data Collective

Big data alone has become a modern staple of nearly every industry from retail to manufacturing, and for good reason. IDC predicts that if our digital universe or total data content were represented by tablets, then by 2020 they would stretch all the way to the moon over six times. Machine Learning Experience is a Must.

Analytics 111
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Your New Cloud for AI May Be Inside a Colo

CIO Business Intelligence

Many companies whose AI model training infrastructure is not proximal to their data lake incur steeper costs as the data sets grow larger and AI models become more complex. The cloud is great for experimentation when data sets are smaller and model complexity is light.

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How We Teach The Leaders of Tomorrow To Be Curious, Ask Questions and Not Be Afraid To Fail Fast To Learn Fast

Rocket-Powered Data Science

Be sure to listen to the full recording of our lively conversation, which covered Data Literacy, Data Strategy, Data Leadership, and more. The data age has been marked by numerous “hype cycles.” In my chat with Joe, we talked about many data concepts in the context of enterprise digital transformation.