End-to-end Enterprise Machine Learning Pipeline in Minutes with PaperSpace – Intel on AI Episode 55
Intel on AI18 Touko 2020

End-to-end Enterprise Machine Learning Pipeline in Minutes with PaperSpace – Intel on AI Episode 55

In this Intel on AI podcast episode: Enterprises are in a race to become more agile, nimble, and responsive to remain competitive in today's fast-changing marketplace. Turning to machine learning (ML) and data science is essential. Today companies can spend millions building their own internal ML pipelines that need ongoing support and maintenance. There are numerous tools that exist for developing traditional web services, but not many tools that enable teams to adopt ML and artificial intelligence (AI). Dillon Erb, CEO at PaperSpace, joins the Intel on AI podcast to talk about how their Gradient solution brings simplicity and flexibility of a traditional platform as a service (PaaS) for building ML models in the cloud. Grandient enables ML teams to deploy more models from research to production because of dramatically shorter development cycles when using the solution. Dillon describes how enterprises can now deploy a mature and robust PaaS within their data center to train and deploy models in a fraction of the time and costs that it previously required. He also discusses how PaperSpace has worked closely with Intel to make it easy for enterprises to use their existing CPU hardware infrastructures to build performant machine learning models with Gradient.

To learn more, visit:
paperspace.com

Visit Intel AI Builders at:
builders.intel.com/ai

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AI Powered Self-Healing 4G LTE Networks with Altran – Intel on AI – Episode 58

AI Powered Self-Healing 4G LTE Networks with Altran – Intel on AI – Episode 58

In this Intel on AI podcast episode: 4G/ LTE network is a preferred method of information transfer today and is becoming more and more crucial in our extremely connected lives. To keep up with the eve...

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Manufacturing Visual Inspection with SPECTRO From HACARUS – Intel on AI – Episode 57

Manufacturing Visual Inspection with SPECTRO From HACARUS – Intel on AI – Episode 57

In this Intel on AI podcast episode: Sparse modeling methods can improve the interpretability of predictive AI models and is widely used in academia today. Yet despite advances in the field, issues re...

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Safe Industrial Workspaces with Video Analytics and Flutura AI – Intel on AI – Episode 56

Safe Industrial Workspaces with Video Analytics and Flutura AI – Intel on AI – Episode 56

In this Intel on AI podcast episode: In industrial and manufacturing settings plant safety issues like oil spills, chemical spills, and PPE (personal protective equipment) violations can happen often....

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vPhrase Making Data Easier to Understand in the Enterprise – Intel on AI – Episode 54

vPhrase Making Data Easier to Understand in the Enterprise – Intel on AI – Episode 54

In this Intel on AI podcast episode: To remain competitive businesses need to utilize their data to the fullest and make data-driven decisions at all levels. Yet, collecting and analyzing data can be ...

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Gnani AI Enabled Voice Bots Empowering Enterprises at Scale – Intel on AI – Episode 53

Gnani AI Enabled Voice Bots Empowering Enterprises at Scale – Intel on AI – Episode 53

In this Intel on AI podcast episode: Every customer call is an opportunity to gain information about customer preferences and provide a positive experience for callers. Yet, call centers can be expens...

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Transforming Enterprise with AI and IoT, Combined – Intel on AI – Episode 52

Transforming Enterprise with AI and IoT, Combined – Intel on AI – Episode 52

In this Intel on AI podcast episode: The Internet of Things (IoT) is producing a tremendous amount of data. But companies need to make sense of the data and AI is a clear answer to analyze and act on ...

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Driving AI Model Training in Healthcare with Intel Xeon and Dell EMC – Intel on AI – Episode 51

Driving AI Model Training in Healthcare with Intel Xeon and Dell EMC – Intel on AI – Episode 51

In this Intel on AI podcast episode: Healthcare workloads, particularly in medical imaging, require more memory usage than other AI workloads because they often use higher resolution 3D images. Deep l...

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