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Update welcome to align with new changes and ensure correct name is used
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12 changes: 8 additions & 4 deletions README.md
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# Welcome to the Iguazio Data Science Platform
# Welcome to the Iguazio MLOps Platform

An initial introduction to the Iguazio Data Science Platform and the platform tutorials
An initial introduction to the Iguazio MLOps Platform and the platform tutorials

- [Platform Overview](#platform-overview)
- [Data Science Workflow](#data-science-workflow)
Expand All @@ -15,7 +15,7 @@ An initial introduction to the Iguazio Data Science Platform and the platform tu

## Platform Overview

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.
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.
The platform incorporates the following components:

- A data science workbench that includes Jupyter Notebook, integrated analytics engines, and Python packages
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## Getting-Started Tutorial

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.
Start out by running the getting-started tutorial to familiarize yourself with the platform and experience firsthand some of its main capabilities.

<a href="demos/getting-started-tutorial/README.ipynb"><img src="./assets/images/view-tutorial-button.png" alt="View tutorial"/></a>

You can also view the tutorial on [GitHub](https://docs.mlrun.org/en/latest/tutorial/01-mlrun-basics.html).

<a id="demos"></a>

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"# Welcome to the Iguazio Data Science Platform\n",
"# Welcome to the Iguazio MLOps Platform\n",
"\n",
"An initial introduction to the Iguazio Data Science Platform and the platform tutorials"
"An initial introduction to the Iguazio MLOps Platform and the platform tutorials"
]
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"## Platform Overview\n",
"\n",
"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",
"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",
"The platform incorporates the following components:\n",
"\n",
"- A data science workbench that includes Jupyter Notebook, integrated analytics engines, and Python packages\n",
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"\n",
"<a href=\"demos/getting-started-tutorial/README.ipynb\"><img src=\"./assets/images/view-tutorial-button.png\" alt=\"View tutorial\"/></a>\n",
"\n",
"You can also view the tutorial on [GitHub](https://github.com/mlrun/demos/blob/release/v0.6.x-latest/getting-started-tutorial/README.md)."
"You can also view the tutorial on [GitHub](https://docs.mlrun.org/en/latest/tutorial/01-mlrun-basics.html)."
]
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" <a href=\"demos/mask-detection/README.md\"><img src=\"./assets/images/Jupyter-Logo-32px.png\"/><br>Open locally</a>\n",
" </td>\n",
" <td align=\"center\", style=\"min-width:45px; padding: 10px;\">\n",
" <a target=\"_blank\" href=\"https://github.com/mlrun/demos/tree/1.1.x/mask-detection/\">\n",
" <a target=\"_blank\" href=\"https://github.com/mlrun/demos/tree/1.3.x-latest/mask-detection/\">\n",
" <img src=\"./assets/images/GitHub-Mark-32px.png\"/><br>View on GitHub</a>\n",
" </td>\n",
" <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",
" </td>\n",
" <td align=\"center\", style=\"min-width:45px; padding: 10px;\">\n",
" <a target=\"_blank\" href=\"https://github.com/mlrun/demos/blob/1.1.x/fraud-prevention-feature-store/\">\n",
" <a target=\"_blank\" href=\"https://github.com/mlrun/demos/blob/1.3.x-latest/fraud-prevention-feature-store/\">\n",
" <img src=\"./assets/images/GitHub-Mark-32px.png\"/><br>View on GitHub</a>\n",
" </td>\n",
" <td>Demonstrates the feature store usage for fraud prevention: Data ingestion & preparation; Model training & testing; Model serving; Building An Automated ML Pipeline.\n",
Expand All @@ -218,7 +222,7 @@
" <a href=\"demos/news-article-nlp/README.md\"><img src=\"./assets/images/Jupyter-Logo-32px.png\"/><br>Open locally</a>\n",
" </td>\n",
" <td align=\"center\", style=\"min-width:45px; padding: 10px;\">\n",
" <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",
" <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",
" </td>\n",
" <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",
"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",
Expand All @@ -230,13 +234,26 @@
" <a href=\"demos/network-operations/README.md\"><img src=\"./assets/images/Jupyter-Logo-32px.png\"/><br>Open locally</a>\n",
" </td>\n",
" <td align=\"center\", style=\"min-width:45px; padding: 10px;\">\n",
" <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",
" <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",
" </td>\n",
" <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",
"The demo implements feature engineering, model training, testing, inference, and model monitoring (with concept-drift detection).\n",
"The demo uses a offline/real-time metrics simulator to generate semi-random network telemetry data that is used across the pipeline.\n",
" </td>\n",
" </tr>\n",
"\t <tr>\n",
" <td><b>Stocks Prediction</b></td>\n",
" <td align=\"center\", style=\"min-width:45px; padding: 10px;\">\n",
" <a href=\"demosstocke-prediction/README.md\"><img src=\"./assets/images/Jupyter-Logo-32px.png\"/><br>Open locally</a>\n",
" </td>\n",
" <td align=\"center\", style=\"min-width:45px; padding: 10px;\">\n",
" <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",
" </td>\n",
" <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",
"\t\tused machine and deep learning frameworks, providing features such as automatic logging, model management, and distributed training). The demo predicts stock prices, \n",
"\t\tand it creates a Grafana dashbord for model analysis.\n",
" </td>\n",
" </tr>\n",
"</table>"
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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",
" </td>\n",
" <td align=\"center\", style=\"min-width:45px; padding: 10px;\">\n",
" <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",
" <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",
" </td>\n",
" <td>Demonstrates how to convert existing ML code to an MLRun project.\n",
" 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",
Expand All @@ -283,7 +301,7 @@
" <a href=\"demos/howto/spark/spark-mlrun-read-csv.ipynb\"><img src=\"./assets/images/Jupyter-Logo-32px.png\"/><br>Open locally</a>\n",
" </td>\n",
" <td align=\"center\", style=\"min-width:45px; padding: 10px;\">\n",
" <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",
" <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",
" </td>\n",
" <td>Demonstrates how to run a Spark job that reads a CSV file and logs the data set to an MLRun database.\n",
" </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",
" </td>\n",
" <td align=\"center\", style=\"min-width:45px; padding: 10px;\">\n",
" <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",
" <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",
" </td>\n",
" <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",
" </td>\n",
Expand All @@ -305,7 +323,7 @@
" <a href=\"demos/howto/spark/spark-operator.ipynb\"><img src=\"./assets/images/Jupyter-Logo-32px.png\"/><br>Open locally</a>\n",
" </td>\n",
" <td align=\"center\", style=\"min-width:45px; padding: 10px;\">\n",
" <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",
" <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",
" </td>\n",
" <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",
" </td>\n",
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"<a id=\"v3io-dir\"></a>\n",
"### The v3io Directory\n",
"\n",
"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/)."
"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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