Data Science teams are taking a more prominent leadership role in their organization by adopting new technologies such as machine learning and deep learning to drive change. Learn how Anaconda and Intel can help your organization harness the power of open source to gain the competitive advantage.
Read this paper to learn how data science leaders must step up and collaborate with IT and security leaders to take charge of their open-source data science and ML pipelines.
This paper walks you through the key considerations of the build-versus-buy conundrum and directs you toward the optimum solution for your organization.
This paper focuses on a common set of tools in the realm of data science and machine learning and also touches on a few tools to take ML and data science to the next level.
Learn how Anaconda Team Edition, a secure repository of more than 7,500 powerful open-source packages for data science and machine learning for build artifacts that you can trust, complete with the innovation and cost benefits of managed open-source technologies.
Read this paper to learn how a data scientist engaging in deep learning (DL) uses models to predict the future, reveal hidden information, identify structure in large data sets, and find unusual trends and patterns in data.
This paper will walk you through organizational readiness, differences between platform types, and key considerations to evaluate vendors in this spac and also includes a detailed interactive checklist to help your team through the evaluation process.
Learn how to accelerate your own scikit-learn workloads with daal4py and Intel AI Analytics Toolkit to see the performance improvements that users can see in shortened model development iteration cycles, reduced cost of training, and a smaller memory footprint.
Read this paper for a performance comparison of XGBoost 1.1 on CPU and on GPU and this paper also takes a closer look at the optimizations that were introduced for this release.
Learn how to train ridge regression models using a version of scikit-learn that is optimized for Intel CPUs, and to compare the performance and accuracy of these models trained with the vanilla scikit-learn library.
Learn how to significantly boost the performance of many classical ML algorithms, including K-Means clustering with Intel Distribution for Python (IDP), which includes the scikit-learn ML library which takes advantage of Intel DAAL.
Read this paper for a comparison of interference performance of 3 different Gradient Boosted Trees implementations and learn how to boost prediction quality and performance using the Intel data analytics acceleration library.
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