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Update welcome to align with new changes and ensure correct name is used
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‎README.md‎

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# Welcome to the Iguazio Data Science Platform
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# Welcome to the Iguazio MLOps Platform
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An initial introduction to the Iguazio Data Science Platform and the platform tutorials
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An initial introduction to the Iguazio MLOps Platform and the platform tutorials
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- [Platform Overview](#platform-overview)
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- [Data Science Workflow](#data-science-workflow)
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## Platform Overview
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The Iguazio Data Science Platform (**"the platform"**) is a fully integrated and secure data science platform as a service (PaaS), which simplifies development, accelerates performance, facilitates collaboration, and addresses operational challenges.
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The Iguazio MLOps Platform (**"the platform"**) is a fully integrated and secure data science platform as a service (PaaS), which simplifies development, accelerates performance, facilitates collaboration, and addresses operational challenges.
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The platform incorporates the following components:
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- A data science workbench that includes Jupyter Notebook, integrated analytics engines, and Python packages
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## Getting-Started Tutorial
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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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Start out by running the getting-started tutorial to familiarize yourself with the platform and experience firsthand some of its main capabilities.
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<a href="demos/getting-started-tutorial/README.ipynb"><img src="./assets/images/view-tutorial-button.png" alt="View tutorial"/></a>
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You can also view the tutorial on [GitHub](https://docs.mlrun.org/en/latest/tutorial/01-mlrun-basics.html).
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<a id="demos"></a>
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‎welcome.ipynb‎

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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Welcome to the Iguazio Data Science Platform\n",
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"# Welcome to the Iguazio MLOps Platform\n",
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"\n",
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"An initial introduction to the Iguazio Data Science Platform and the platform tutorials"
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"An initial introduction to the Iguazio MLOps Platform and the platform tutorials"
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]
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},
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{
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"source": [
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"## Platform Overview\n",
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"\n",
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"The Iguazio Data Science Platform (**\"the platform\"**) is a fully integrated and secure data science platform as a service (PaaS), which simplifies development, accelerates performance, facilitates collaboration, and addresses operational challenges.\n",
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"The Iguazio MLOps Platform (**\"the platform\"**) is a fully integrated and secure data science platform as a service (PaaS), which simplifies development, accelerates performance, facilitates collaboration, and addresses operational challenges.\n",
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"The platform incorporates the following components:\n",
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"\n",
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"- A data science workbench that includes Jupyter Notebook, integrated analytics engines, and Python packages\n",
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]
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},
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"<a href=\"demos/getting-started-tutorial/README.ipynb\"><img src=\"./assets/images/view-tutorial-button.png\" alt=\"View tutorial\"/></a>\n",
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"\n",
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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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"You can also view the tutorial on [GitHub](https://docs.mlrun.org/en/latest/tutorial/01-mlrun-basics.html)."
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]
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},
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]
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"attachments": {},
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"metadata": {},
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"source": [
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" <a href=\"demos/mask-detection/README.md\"><img src=\"./assets/images/Jupyter-Logo-32px.png\"/><br>Open locally</a>\n",
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" </td>\n",
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" <td align=\"center\", style=\"min-width:45px; padding: 10px;\">\n",
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" <a target=\"_blank\" href=\"https://github.com/mlrun/demos/tree/1.1.x/mask-detection/\">\n",
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" <a target=\"_blank\" href=\"https://github.com/mlrun/demos/tree/1.3.x-latest/mask-detection/\">\n",
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" <img src=\"./assets/images/GitHub-Mark-32px.png\"/><br>View on GitHub</a>\n",
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" </td>\n",
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" <td>This demo contains 3 notebooks where we:\n",
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" <a href=\"demos/fraud-prevention-feature-store/README.md\"><img src=\"./assets/images/Jupyter-Logo-32px.png\"/><br>Open locally</a>\n",
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" </td>\n",
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" <td align=\"center\", style=\"min-width:45px; padding: 10px;\">\n",
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" <a target=\"_blank\" href=\"https://github.com/mlrun/demos/blob/1.1.x/fraud-prevention-feature-store/\">\n",
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" <a target=\"_blank\" href=\"https://github.com/mlrun/demos/blob/1.3.x-latest/fraud-prevention-feature-store/\">\n",
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" <img src=\"./assets/images/GitHub-Mark-32px.png\"/><br>View on GitHub</a>\n",
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" </td>\n",
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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.\n",
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" <a href=\"demos/news-article-nlp/README.md\"><img src=\"./assets/images/Jupyter-Logo-32px.png\"/><br>Open locally</a>\n",
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" </td>\n",
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" <td align=\"center\", style=\"min-width:45px; padding: 10px;\">\n",
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" <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>\n",
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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>\n",
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" </td>\n",
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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.\n",
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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.\n",
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" <a href=\"demos/network-operations/README.md\"><img src=\"./assets/images/Jupyter-Logo-32px.png\"/><br>Open locally</a>\n",
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" </td>\n",
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" <td align=\"center\", style=\"min-width:45px; padding: 10px;\">\n",
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" <a target=\"_blank\" href=\"https://github.com/mlrun/demos/tree/1.1.x/network-operations/\"><img src=\"./assets/images/GitHub-Mark-32px.png\"/><br>View on GitHub</a>\n",
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" <a target=\"_blank\" href=\"https://github.com/mlrun/demos/tree/1.3.x-latest/network-operations/\"><img src=\"./assets/images/GitHub-Mark-32px.png\"/><br>View on GitHub</a>\n",
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" </td>\n",
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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).\n",
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"The demo implements feature engineering, model training, testing, inference, and model monitoring (with concept-drift detection).\n",
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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.\n",
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" </td>\n",
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" </tr>\n",
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"\t <tr>\n",
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" <td><b>Stocks Prediction</b></td>\n",
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" <td align=\"center\", style=\"min-width:45px; padding: 10px;\">\n",
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" <a href=\"demosstocke-prediction/README.md\"><img src=\"./assets/images/Jupyter-Logo-32px.png\"/><br>Open locally</a>\n",
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" </td>\n",
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" <td align=\"center\", style=\"min-width:45px; padding: 10px;\">\n",
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" <a target=\"_blank\" href=\"https://github.com/mlrun/demos/tree/1.3.x-latest/stocks-prediction/\"><img src=\"./assets/images/GitHub-Mark-32px.png\"/><br>View on GitHub</a>\n",
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" </td>\n",
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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 \n",
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"\t\tused machine and deep learning frameworks, providing features such as automatic logging, model management, and distributed training). The demo predicts stock prices, \n",
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"\t\tand it creates a Grafana dashbord for model analysis.\n",
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" </td>\n",
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" </tr>\n",
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"</table>"
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]
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},
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"attachments": {},
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"cell_type": "markdown",
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"source": [
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" <a href=\"demos/howto/converting-to-mlrun/mlrun-code.ipynb\"><img src=\"./assets/images/Jupyter-Logo-32px.png\"/><br>Open locally</a>\n",
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" </td>\n",
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" <td align=\"center\", style=\"min-width:45px; padding: 10px;\">\n",
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" <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>\n",
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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>\n",
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" </td>\n",
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" <td>Demonstrates how to convert existing ML code to an MLRun project.\n",
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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>.\n",
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" <a href=\"demos/howto/spark/spark-mlrun-read-csv.ipynb\"><img src=\"./assets/images/Jupyter-Logo-32px.png\"/><br>Open locally</a>\n",
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" </td>\n",
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" <td align=\"center\", style=\"min-width:45px; padding: 10px;\">\n",
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" <a target=\"_blank\" href=\"https://github.com/mlrun/demos/blob/1.1.x/howto/spark/spark-mlrun-read-csv.ipynb\"><img src=\"./assets/images/GitHub-Mark-32px.png\"/><br>View on GitHub</a>\n",
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" <a target=\"_blank\" href=\"https://github.com/mlrun/demos/blob/1.3.x-latest/howto/spark/spark-mlrun-read-csv.ipynb\"><img src=\"./assets/images/GitHub-Mark-32px.png\"/><br>View on GitHub</a>\n",
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" </td>\n",
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" <td>Demonstrates how to run a Spark job that reads a CSV file and logs the data set to an MLRun database.\n",
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" </td>\n",
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" <a href=\"demos/howto/spark/spark-mlrun-describe.ipynb\"><img src=\"./assets/images/Jupyter-Logo-32px.png\"/><br>Open locally</a>\n",
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" </td>\n",
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" <td align=\"center\", style=\"min-width:45px; padding: 10px;\">\n",
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" <a target=\"_blank\" href=\"https://github.com/mlrun/demos/blob/1.1.x/howto/spark/spark-mlrun-describe.ipynb\"><img src=\"./assets/images/GitHub-Mark-32px.png\"/><br>View on GitHub</a>\n",
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" <a target=\"_blank\" href=\"https://github.com/mlrun/demos/blob/1.3.x-latest/howto/spark/spark-mlrun-describe.ipynb\"><img src=\"./assets/images/GitHub-Mark-32px.png\"/><br>View on GitHub</a>\n",
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" <td>Demonstrates how to create and run a Spark job that generates a profile report from an Apache Spark DataFrame based on pandas profiling.\n",
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" </td>\n",
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" <a href=\"demos/howto/spark/spark-operator.ipynb\"><img src=\"./assets/images/Jupyter-Logo-32px.png\"/><br>Open locally</a>\n",
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" </td>\n",
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" <td align=\"center\", style=\"min-width:45px; padding: 10px;\">\n",
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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>\n",
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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>\n",
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" </td>\n",
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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.\n",
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" </td>\n",
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]
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"attachments": {},
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"cell_type": "markdown",
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"source": [
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"<a id=\"v3io-dir\"></a>\n",
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"### The v3io Directory\n",
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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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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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"version": "3.10.6 (main, Nov 14 2022, 16:10:14) [GCC 11.3.0]"
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"vscode": {
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"interpreter": {

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