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SAS Analytics Pro – now available for on-site or containerized cloud-native deployment – providing your entry point into SAS Viya
Now, SAS Analytics Pro includes a new option for containerized cloud-native deployment. This makes SAS Analytics Pro a perfect entry point into SAS Viya.
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What Comes After HDF5? Seeking a Data Storage Format for Deep Learning
In this article we are discussing that HDF5 is one of the most popular and reliable formats for non-tabular, numerical data. But this format is not optimized for deep learning work. This article suggests what kind of ML native data format should be to truly serve the needs of modern data scientists.
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7 of The Coolest Machine Learning Topics of 2021 at ODSC West
At our upcoming event this November 16th-18th in San Francisco, ODSC West 2021 will feature a plethora of talks, workshops, and training sessions on machine learning topics, deep learning, NLP, MLOps, and so on. You can register now for 20% off all ticket types, or register for a free AI Expo Pass to see what some big names in AI are doing now.
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Visual Scoring Techniques for Classification Models
Read this article assessing a model performance in a broader context.
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Neural Networks from a Bayesian Perspective
This article looks at neural networks from a Bayesian perspective.
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Three reasons to self-host your product analytics
Want three reasons to avoid the cloud and host your own analytics platform? More data, more control, more secure.
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Is the Modern Data Stack Leaving You Behind?
The modern data stack narrative is largely dominated by analytics engineering. Where does that leave data engineers? Discover the difference between the MDS for data engineers & analytics engineers.
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Multivariate Time Series Analysis with an LSTM based RNN
Check out this codeless solution using the Keras integration.
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Analyze Python Code in Jupyter Notebooks
We present a new tool that integrates modern code analysis techniques with Jupyter notebooks and helps developers find bugs as they write code.
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 Machine Learning Model Development and Model Operations: Principles and Practices
The ML model management and the delivery of highly performing model is as important as the initial build of the model by choosing right dataset. The concepts around model retraining, model versioning, model deployment and model monitoring are the basis for machine learning operations (MLOps) that helps the data science teams deliver highly performing models.
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Machine Learning Model Development and Model Operations: Principles and Practices