Data Science Workstations Benchmarks and Best Practice

Business has changed rapidly over the last decade, with the Internet of Things and its industrial counterpart producing vast quantities of data. Estimates from IDC suggest these will reach 175 zettabytes worldwide by 2025 – a tenfold increase on 2017.

This in turn, has resulted in an increased need for processing power, with concurrent advancements in algorithms, open source software and specialized hardware accelerators, driving an explosive adoption of Artificial Intelligence (AI).

Machine learning and deep learning each have their own unique use cases and challenges. Machine learning is less sophisticated and is generally used on structured data – such as tabular data – processed using well-known algorithms like linear regression, logistic regression, naïve Bayes and XGBoost.

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