Kubernetes is a great system for handling clusters of containers (whether on cloud or on-premise), but deploying and managing containerized applications for ML can be a challenging task.

Kubeflow is known as a machine learning toolkit for Kubernetes. It is an open source project used for making deployments of machine learning workflows on Kubernetes…

Scikit-learn (also known as sklearn) is a widely used free software machine learning library for the Python programming language. It has been adopted by many companies and universities as it features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, and k-means. SKlearn is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

Scikit-learn is largely written in Python…

A use case on Logistic regression training

Over the last few years there are several efforts for more powerful computing platforms to face the challenges imposed by emerging applications like machine learning. General purpose CPUs have been developed specialized ML modules, GPUs and FPGAs with specialized engines are…

Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more.

Due to its simplicity it has gained wide popularity among the data scientists and the HPC users that want to create and share applications that integrates the code, the…

Machine learning algorithms are extremely computationally intensive and time consuming when they must be trained on large amounts of data. Typical processors are not optimized for machine learning applications and therefore offer limited performance. Therefore, both academia an industry is focused on the development of specialized architectures for the efficient acceleration of machine learning applications.

FPGAs are programmable chips that can be configured with tailored-made…

Emerging applications like machine learning (ML), big data analytics, and artificial intelligence (AI) has created the need for many companies to hire highly skilled and experienced work force. Demand for data scientists, ML engineers and data engineers is booming and will only increase in the next years. The January report from Indeed, one of the top job sites, showed a 29% increase in demand for data scientists year over year and a 344% increase since 2013.

Salaries and…

Machine learning applications require powerful and scalable computing systems that can sustain the high computation complexity of these applications. Companies that are working on the domain of machine learning have to allocate a significant amount of their budget for the OpEx of machine learning applications whether this is done on cloud or on-prem.

Typical machine learning application…

Emerging cloud applications like machine learning, AI and big data analytics require high performance computing systems that can sustain the increased amount of data processing without consuming excessive power. Towards this end, many cloud operators have started adopting heterogeneous infrastructures deploying hardware accelerators, like FPGAs, to increase the performance of computational intensive tasks. However, most hardware accelerators lack…