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You can also view the tutorial on [GitHub](https://github.com/mlrun/demos/blob/release/v0.6.x-latest/getting-started-tutorial/README.md).
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Start out by running the [getting-started tutorial](https://docs.mlrun.org/en/latest/tutorial/01-mlrun-basics.html) to familiarize yourself with the platform and experience firsthand some of its main capabilities.
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<aid="demos"></a>
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@@ -102,7 +98,7 @@ For full usage instructions, run the script with the `-h` or `--help` flag:
<img src="./assets/images/GitHub-Mark-32px.png"/><br>View on GitHub</a>
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<td>Demonstrates the feature store usage for fraud prevention: Data ingestion & preparation; Model training & testing; Model serving; Building An Automated ML Pipeline.
@@ -129,7 +125,7 @@ For full usage instructions, run the script with the `-h` or `--help` flag:
<a target="_blank" href="https://github.com/mlrun/demos/tree/1.1.x/news-article-nlp/"><img src="./assets/images/GitHub-Mark-32px.png"/><br>View on GitHub</a>
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<a target="_blank" href="https://github.com/mlrun/demos/tree/1.3.x-latest/news-article-nlp/"><img src="./assets/images/GitHub-Mark-32px.png"/><br>View on GitHub</a>
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<td>This demo creates an NLP pipeline that summarizes and extract keywords from a news article URL. We will be using state-of-the-art transformer models. such as BERT. to perform these NLP tasks.
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Additionally, we will use MLRun's real-time inference graphs to create the pipeline. This allows for easy containerization and deployment of the pipeline on top of a production-ready Kubernetes cluster.
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<atarget="_blank"href="https://github.com/mlrun/demos/tree/1.1.x/network-operations/"><imgsrc="./assets/images/GitHub-Mark-32px.png"/><br>View on GitHub</a>
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<atarget="_blank"href="https://github.com/mlrun/demos/tree/1.3.x-latest/network-operations/"><imgsrc="./assets/images/GitHub-Mark-32px.png"/><br>View on GitHub</a>
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<td>This demo demonstrates how to build an automated machine-learning (ML) pipeline for predicting network outages based on network-device telemetry, also known as Network Operations (NetOps).
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The demo implements feature engineering, model training, testing, inference, and model monitoring (with concept-drift detection).
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The demo uses a offline/real-time metrics simulator to generate semi-random network telemetry data that is used across the pipeline.
<atarget="_blank"href="https://github.com/mlrun/demos/tree/1.3.x-latest/stocks-prediction/"><imgsrc="./assets/images/GitHub-Mark-32px.png"/><br>View on GitHub</a>
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<td>This demo illustrates using Iguazio's latest technologies and methods for model serving, the platform feature store, and the MLRun frameworks (sub-modules for the most commonly
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used machine and deep learning frameworks, providing features such as automatic logging, model management, and distributed training). The demo predicts stock prices,
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and it creates a Grafana dashbord for model analysis.
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</td>
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@@ -167,7 +176,7 @@ The demo uses a offline/real-time metrics simulator to generate semi-random netw
<a target="_blank" href="https://github.com/mlrun/demos/tree/1.1.x/howto/converting-to-mlrun"><img src="./assets/images/GitHub-Mark-32px.png"/><br>View on GitHub</a>
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<a target="_blank" href="https://github.com/mlrun/demos/tree/1.3.x-latest/howto/converting-to-mlrun"><img src="./assets/images/GitHub-Mark-32px.png"/><br>View on GitHub</a>
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<td>Demonstrates how to convert existing ML code to an MLRun project.
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The demo implements an MLRun project for taxi ride-fare prediction based on a <a href="https://www.kaggle.com/jsylas/python-version-of-top-ten-rank-r-22-m-2-88">Kaggle notebook</a> with an ML Python script that uses data from the <a href="https://www.kaggle.com/c/new-york-city-taxi-fare-prediction">New York City Taxi Fare Prediction competition</a>.
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<a target="_blank" href="https://github.com/mlrun/demos/blob/1.1.x/howto/spark/spark-operator.ipynb"><img src="./assets/images/GitHub-Mark-32px.png"/><br>View on GitHub</a>
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<a target="_blank" href="https://github.com/mlrun/demos/blob/1.3.x-latest/howto/spark/spark-operator.ipynb"><img src="./assets/images/GitHub-Mark-32px.png"/><br>View on GitHub</a>
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<td>Demonstrates how to use <a target="_blank" href="https://github.com/GoogleCloudPlatform/spark-on-k8s-operator">Spark Operator</a> to run a Spark job over Kubernetes with MLRun.
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@@ -261,7 +270,7 @@ For details, see the [**update-tutorials.ipynb**](update-tutorials.ipynb) notebo
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<aid="v3io-dir"></a>
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### The v3io Directory
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The **v3io** directory that you see in the file browser of the Jupyter UI displays the contents of the `v3io` data mount for browsing the platform data containers. For information about the platform's data containers and how to reference data in these containers, see [Data Containers](https://www.iguazio.com/docs/latest-release/data-layer/containers/).
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The **v3io** directory that you see in the file browser of the Jupyter UI displays the contents of the `v3io` data mount for browsing the platform data containers. For information about the platform's data containers and how to reference data in these containers, see [Data Containers](https://www.iguazio.com/docs/latest-release/services/data-layer/containers/).
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