Transforming Information Into Knowledge
Transforming Information Into Knowledge
Ignore the anecdotal stories you’re hearing about AI workloads driving a migration from the cloud to on-premises and private cloud environments. In 2024, only 27% of professional respondents in Anaconda’s latest “State of Data Science” report deploy most of their models to on-premises servers, which […]
Although the collaboration tools space is getting more crowded, machine learning and data practitioners still prefer GitHub to other tools, says the latest survey by Kaggle.
Once a use case is actually defined, it takes 66% of organizations more than a month to develop an ML model. For 64% of organizations, it takes at least another month to deploy that model.
Data engineers use cloud-based software and APIs as their primary tool to analyze data and are more likely to analyze data in the cloud.
Integrating unstructured data is the top data challenge encountered when developing AI according to 57% of the survey by MIT Technology Review Insights.
On average, 40% of companies said it takes more than a month to deploy an ML model into production, 28% do so in eight to 30 days, while only 14% could do so in seven days or less.
51% of data specialists that are doing ML said their models are created by an internal data science team.
No matter how you look at gender, the percentage of women in IT varies depending on the study. Yet, in some ways, it doesn’t matter if the figure is 5 percent or 25 percent, the statistic still obscure hard truths. For example, when women join a programming team, […]
This is the second of several blog posts that look at the use of analytics consultants and when consulting firms should use analytics consultants. The first article can be found here. Management consultancies like McKinsey have been rushing to get in on the analytics boom […]
This is the first of several blog posts that look at the use of analytics consultants and when consulting firms should use analytics consultants. Throughout the corporate world analytics is a necessary capability for a data-driven enterprise. But what types of analytics should be done […]
Bill Franks knew what he was talking about at last December’s Data Skeptic’s Meetup. I recommend him as a speaker. In his job at Teradata, I can easily see him successfully introducing robust new analytic projects to Teradata’s legacy data warehouse clients. While Franks had to cover […]
I was fascinated with the chart that shows how often self-identified data scientists with different skill sets work with different scales of data. It is notable that most data scientists weren’t using big data (see the chart below). I assume that in 2015 a much larger percentage of data scientists are working on a terabyte or petabyte scale.