If you want to run a spark job in windows that make use of the spark SQL pool, you need to set the HADOOP_HOME and hadoop.home.dir environment variables manually and there is a very quick and easy solution to do this.
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For me, Windows11 is the best shell of Linux. I´m using it in my home and work and for that reason I´m using WSL2 to run my Linux and dev station (python, scala,…), so there is a trick to avoid the problem of the Git credential store in WSL2. The trick is to configure the credential helper of the git inside WSL2 as the windows11 credential helper.
When you work with Azure Machine Learning, you are not required to work with Azure Machine Learning portal. Since Azure Machine Learning libraries supports to work connected with your workspace through the official library, you can benefit from containers to create an entire dev container to develop and deploy your code into Azure Machine Learning Service.
I had the pleasure and honor to be speaker in the past codemotion world conference talking about #DeepLearning and #Azure.
Tittle: Applied Deep Learning state-of-the-art
Subject: In this session we will see how to do transfer learning over a state-of-the-art pre-trained model like RoBERTa to achieve our own goal in Named Entity Recognition with our own DataSet. We will take and label new data to finally feed the network trying to achieve transfer learning. Then we will make use of the new fine tuned weights and use FastAPI to deploy our new fine tuned model to kubernetes to make inference.
Here is the video of my talk:
When you work with text data, you often want to split it into training and test sets. This is something very usual in machine learning and also in deep learning´s natural language processing. The problem is that you have to split the data in a way that is consistent with the training and test sets, but at the same time you want to keep your data consistent.
NOTE: img source