Skip to content

Commit

Permalink
Merge pull request #367 from jillnogold/demo-links
Browse files Browse the repository at this point in the history 
update demo links
  • Loading branch information
@aviaIguazio
aviaIguazio authored May 10, 2022
2 parents a8e2717 + 81df0a7 commit 281957a
Showing 1 changed file with 28 additions and 46 deletions.
74 changes: 28 additions & 46 deletions welcome.ipynb
View file
Original file line number Diff line number Diff line change
Expand Up @@ -187,73 +187,55 @@
" <th>Description</th>\n",
" </tr>\n",
" <tr>\n",
" <td><b>scikit-learn Demo: Full AutoML pipeline</b></td>\n",
" <td><b>Mask detection</b></td>\n",
" <td align=\"center\", style=\"min-width:45px; padding: 10px;\">\n",
" <a href=\"demos/scikit-learn-pipeline/sklearn-project.ipynb\"><img src=\"./assets/images/Jupyter-Logo-32px.png\"/><br>Open locally</a>\n",
" <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/release/v0.6.x-latest/scikit-learn-pipeline\">\n",
" <a target=\"_blank\" href=\"https://github.com/mlrun/demos/tree/1.0.x/mask-detection/\">\n",
" <img src=\"./assets/images/GitHub-Mark-32px.png\"/><br>View on GitHub</a>\n",
" </td>\n",
" <td>Demonstrates how to build a full end-to-end automated-ML (AutoML) pipeline using <a href=\"https://scikit-learn.org\">scikit-learn</a> and the UCI <a href=\"http://archive.ics.uci.edu/ml/datasets/iris\">Iris data set</a>.\n",
" <td>This demo contains 3 notebooks where we:\n",
" 1. Train and evaluate a model for detecting if a person is wearing a mask in an image by using Tensorflow.Keras or PyTorch.<br>\n",
" 2. Serve the model as a serverless function in a http endpoint.<br>\n",
" 3. Write an automatic pipeline where we download a dataset of images, train and evaluate the model, then optimize the model (using ONNX) and serve it.\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td><b>Image-Classification Demo: Image classification with distributed training</b></td>\n",
" <td><b>Fraud Prevention - Iguazio Feature Store</b></td>\n",
" <td align=\"center\", style=\"min-width:45px; padding: 10px;\">\n",
" <a href=\"demos/image-classification-with-distributed-training/horovod-project.ipynb\"><img src=\"./assets/images/Jupyter-Logo-32px.png\"/><br>Open locally</a>\n",
" <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/tree/release/v0.6.x-latest/image-classification-with-distributed-training/\">\n",
" <a target=\"_blank\" href=\"https://github.com/mlrun/demos/blob/1.0.x/fraud-prevention-feature-store/README.md/\">\n",
" <img src=\"./assets/images/GitHub-Mark-32px.png\"/><br>View on GitHub</a>\n",
" </td>\n",
" <td>Demonstrates an end-to-end image-classification solution using <a href=\"https://www.tensorflow.org/\">TensorFlow</a> (versions 1 or 2), <a href=\"https://keras.io/\">Keras</a>, <a href=\"https://eng.uber.com/horovod/\">Horovod</a>, and <a href=\"https://nuclio.io/\">Nuclio</a>.\n",
" <td>Demonstrates the feature store usage for fraud prevention: Data ingestion & preparation; Model training & testing; Model serving; Building An Automated ML Pipeline.\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td><b>Faces Demo: Real-time image recognition with deep learning</b></td>\n",
" <td><b>News Article Summarization and Keyword Extraction via NLP</b></td>\n",
" <td align=\"center\", style=\"min-width:45px; padding: 10px;\">\n",
" <a href=\"demos/realtime-face-recognition/notebooks/face-recognition.ipynb\"><img src=\"./assets/images/Jupyter-Logo-32px.png\"/><br>Open locally</a>\n",
" <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/release/v0.6.x-latest/realtime-face-recognition/\"><img src=\"./assets/images/GitHub-Mark-32px.png\"/><br>View on GitHub</a>\n",
" <a target=\"_blank\" href=\"https://github.com/mlrun/demos/tree/1.0.x/news-article-nlp/\"><img src=\"./assets/images/GitHub-Mark-32px.png\"/><br>View on GitHub</a>\n",
" </td>\n",
" <td>Demonstrates real-time capture, recognition, and classification of face images over a video stream, as well as location tracking of identities, using <a href=\"https://pytorch.org/\">PyTorch</a>, <a href=\"https://opencv.org/\">OpenCV</a>, and <a href=\"https://www.streamlit.io/\">Streamlit</a>.\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",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td><b>Churn Demo: Real-time customer-churn prediction</b></td>\n",
" <td><b>NetOps Demo: Predictive Network Operations/Telemetry</b></td>\n",
" <td align=\"center\", style=\"min-width:45px; padding: 10px;\">\n",
" <a href=\"demos/customer-churn-prediction/churn-project.ipynb\"><img src=\"./assets/images/Jupyter-Logo-32px.png\"/><br>Open locally</a>\n",
" <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/release/v0.6.x-latest/customer-churn-prediction/\"><img src=\"./assets/images/GitHub-Mark-32px.png\"/><br>View on GitHub</a>\n",
" <a target=\"_blank\" href=\"https://github.com/mlrun/demos/tree/1.0.x/network-operations/\"><img src=\"./assets/images/GitHub-Mark-32px.png\"/><br>View on GitHub</a>\n",
" </td>\n",
" <td>Demonstrates analysis of customer-churn data using the Kaggle <a href=\"https://www.kaggle.com/blastchar/telco-customer-churn\" rel=\"nofollow\">Telco Customer Churn data set</a>, model training and validation using <a href=\"https://xgboost.readthedocs.io/\" rel=\"nofollow\">XGBoost</a>, and model serving using real-time Nuclio serverless functions.\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td><b>Stock-Analysis Demo</b></td>\n",
" <td align=\"center\", style=\"min-width:45px; padding: 10px;\">\n",
" <a href=\"demos/stock-analysis/project.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/release/v0.6.x-latest/stock-analysis/\"><img src=\"./assets/images/GitHub-Mark-32px.png\"/><br>View on GitHub</a>\n",
" </td>\n",
" <td>Demonstrates how to tackle a common requirement of running a data-engineering pipeline as part of ML model serving by reading data from external data sources and generating insights using ML models.\n",
" The demo reads stock data from an external source, analyzes the related market news, and visualizes the analyzed data in a Grafana dashboard.\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td><b>NetOps Demo: Predictive network operations / telemetry</b></td>\n",
" <td align=\"center\", style=\"min-width:45px; padding: 10px;\">\n",
" <a href=\"demos/network-operations/project.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/release/v0.6.x-latest/network-operations/\"><img src=\"./assets/images/GitHub-Mark-32px.png\"/><br>View on GitHub</a>\n",
" </td>\n",
" <td>Demonstrates how to build an automated ML pipeline for predicting network outages based on network-device telemetry, also known as Network Operations (NetOps).\n",
" The demo implements both model training and inference, including model monitoring and concept-drift detection.\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",
"</table>"
Expand Down Expand Up @@ -290,7 +272,7 @@
" <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/release/v0.6.x-latest/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/1.0.x/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 @@ -302,7 +284,7 @@
" <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/release/v0.6.x-latest/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/1.0.x/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",
Expand All @@ -313,7 +295,7 @@
" <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/release/v0.6.x-latest/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/1.0.x/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 @@ -324,7 +306,7 @@
" <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/release/v0.6.x-latest/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/1.0.x/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",
Expand Down Expand Up @@ -484,7 +466,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
Expand All @@ -498,7 +480,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
"version": "3.7.7"
}
},
"nbformat": 4,
Expand Down

0 comments on commit 281957a

Please sign in to comment.