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From Training to Model Serving with Red Hat OpenShift Data Science - Part 3 Kubeflow PytorchJob and Triton Inference Server Introduction In Part 1 , we saw how to train a model using CodeFlare and Ray cluster with multiple pods using GPUs. In Part 2 , we saw how to use the Multi...
From Training to Model Serving with Red Hat OpenShift Data Science - Part 2 MNIST handwritten digits, Fashion MNIST and CIFAR10 data sets Introduction In Part 1 , we saw the use of Codeflare/Ray to finetune a huggingface model with imdb for sentiment analysis using the @ray.remote...
From Training to Model Serving with Red Hat OpenShift Data Science - Part 1 IMDB Sentiment Analysis with Huggingface Introduction Red Hat OpenShift Data Science (RHODS) is a machine-learning-as-a-service platform built on Red Hat's Kubernetes-based OpenShift Container Platform, Ceph Object...
We're very glad to announce that last week IBMers Martha Cryan and Alex Bozarth were both officially added to the JupyterLab committer list! Open source is core to the ethos of IBM and how we build our own products, so we're exceptionally proud of Alex and Martha in their continued...
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MongoDB is a NoSQL JSON document based database. As an alternative to traditional SQL based methods, Mongo instead offers a vast amount of query operators packaged as the "MongoDB Query Language" (MQL). Developers can use MQL filtering, aggregation, and sorting operations to rapidly generate...
[Link] Code: https://github.com/airbnb/knowledge-repo As an organization grows, how do we make sure that an insight uncovered by one person effectively transfers beyond the targeted recipient? Airbnb solves this with its knowledge repository project: An open-source...
Here’s the second post in our “Monday Replay” series. Once again, this lightning talk came out of the February 12, 2020 SF Python Meetup where the IBM Data Science Community was happy to be the sponsor for video post-production. The presenter is Phillip K.S. Chu ( GitHub ). Enjoy! ...
Context The IBM Data Science Elite (DSE) is a team of data scientists and data engineers who work with a select group of IBM clients to test and implement data science solutions — at no additional cost to those clients. The DSE team is the result of a stark reality that companies...
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This blog is posted on IBM Big Data & Analytics Hub here: https://www.ibmbigdatahub.com/blog/bridging-data-science-skills-gap-collaboration ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ What good are notebooks? Bridging the data science skills gap with collaboration May 10, 2019 ...
Challenged using #Jupyter Notebooks at scale in your organization? Sam Charrington (@samcharrington) and Luciano Resende (@lresende1975) spent some time discussing these challenges and the role of open source projects, like Jupyter Hub (LINK) and Jupyter Enterprise Gatweay , in making this...