From 3e591e09904731de7137cda81589afe3aef2edaa Mon Sep 17 00:00:00 2001 From: jillnogold Date: Tue, 10 May 2022 09:38:28 +0300 Subject: [PATCH 1/3] update end to end demo links --- welcome.ipynb | 64 ++++++++++++++++++--------------------------------- 1 file changed, 22 insertions(+), 42 deletions(-) diff --git a/welcome.ipynb b/welcome.ipynb index d6205916..7988052a 100644 --- a/welcome.ipynb +++ b/welcome.ipynb @@ -187,73 +187,53 @@ " Description\n", " \n", " \n", - " scikit-learn Demo: Full AutoML pipeline\n", + " Mask detection\n", " \n", - "
Open locally\n", + "
Open locally\n", " \n", " \n", - " \n", + " \n", "
View on GitHub\n", " \n", - " Demonstrates how to build a full end-to-end automated-ML (AutoML) pipeline using scikit-learn and the UCI Iris data set.\n", + " 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.
\n", + " 2. Serve the model as a serverless function in a http endpoint.
\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", " \n", " \n", " \n", - " Image-Classification Demo: Image classification with distributed training\n", + " Fraud Prevention - Iguazio Feature Store\n", " \n", - "
Open locally\n", + "
Open locally\n", " \n", " \n", - " \n", + " \n", "
View on GitHub\n", " \n", - " Demonstrates an end-to-end image-classification solution using TensorFlow (versions 1 or 2), Keras, Horovod, and Nuclio.\n", + " Demonstrates the feature store usage for fraud prevention: Data ingestion & preparation; Model training & testing; Model serving; Building An Automated ML Pipeline.\n", " \n", " \n", " \n", - " Faces Demo: Real-time image recognition with deep learning\n", + " News Article Summarization and Keyword Extraction via NLP\n", " \n", - "
Open locally\n", + "
Open locally\n", " \n", " \n", - "
View on GitHub\n", + "
View on GitHub\n", " \n", - " Demonstrates real-time capture, recognition, and classification of face images over a video stream, as well as location tracking of identities, using PyTorch, OpenCV, and Streamlit.\n", + " 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", " \n", " \n", " \n", - " Churn Demo: Real-time customer-churn prediction\n", + " Converting Existing ML Code to an MLRun Project\n", " \n", - "
Open locally\n", + "
Open locally\n", " \n", " \n", - "
View on GitHub\n", + "
View on GitHub\n", " \n", - " Demonstrates analysis of customer-churn data using the Kaggle Telco Customer Churn data set, model training and validation using XGBoost, and model serving using real-time Nuclio serverless functions.\n", - " \n", - " \n", - " \n", - " Stock-Analysis Demo\n", - " \n", - "
Open locally\n", - " \n", - " \n", - "
View on GitHub\n", - " \n", - " 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", - " \n", - " \n", - " \n", - " NetOps Demo: Predictive network operations / telemetry\n", - " \n", - "
Open locally\n", - " \n", - " \n", - "
View on GitHub\n", - " \n", - " 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", + " This demo demonstrates how to convert existing machine-learning (ML) code to an MLRun project. The demo implements an MLRun project for taxi ride-fare prediction based on a [Kaggle notebook](https://www.kaggle.com/code/jsylas/python-version-of-top-ten-rank-r-22-m-2-88/script) with an ML Python script that uses data from the [New York City Taxi Fare Prediction competition](https://www.kaggle.com/c/new-york-city-taxi-fare-prediction/).\n", " \n", " \n", "" @@ -484,7 +464,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -498,7 +478,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.7" } }, "nbformat": 4, From 40054a35bf2bd83c41fc97c03d6f3559cde90d50 Mon Sep 17 00:00:00 2001 From: jillnogold Date: Tue, 10 May 2022 09:42:25 +0300 Subject: [PATCH 2/3] fic hrefs --- welcome.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/welcome.ipynb b/welcome.ipynb index 7988052a..4f140b85 100644 --- a/welcome.ipynb +++ b/welcome.ipynb @@ -233,7 +233,7 @@ " \n", "
View on GitHub\n", " \n", - " This demo demonstrates how to convert existing machine-learning (ML) code to an MLRun project. The demo implements an MLRun project for taxi ride-fare prediction based on a [Kaggle notebook](https://www.kaggle.com/code/jsylas/python-version-of-top-ten-rank-r-22-m-2-88/script) with an ML Python script that uses data from the [New York City Taxi Fare Prediction competition](https://www.kaggle.com/c/new-york-city-taxi-fare-prediction/).\n", + " This demo demonstrates how to convert existing machine-learning (ML) code to an MLRun project. The demo implements an MLRun project for taxi ride-fare prediction based on a Kaggle notebook with an ML Python script that uses data from the New York City Taxi Fare Prediction competition.\n", " \n", " \n", "" From 81df0a7044386307d3fa65a903a098718b60de1a Mon Sep 17 00:00:00 2001 From: jillnogold Date: Tue, 10 May 2022 10:12:27 +0300 Subject: [PATCH 3/3] update how-to demo links to v1.0.x --- welcome.ipynb | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/welcome.ipynb b/welcome.ipynb index 4f140b85..af8c0ae2 100644 --- a/welcome.ipynb +++ b/welcome.ipynb @@ -226,14 +226,16 @@ " \n", " \n", " \n", - " Converting Existing ML Code to an MLRun Project\n", + " NetOps Demo: Predictive Network Operations/Telemetry\n", " \n", - "
Open locally\n", + "
Open locally\n", " \n", " \n", - "
View on GitHub\n", + "
View on GitHub\n", " \n", - " This demo demonstrates how to convert existing machine-learning (ML) code to an MLRun project. The demo implements an MLRun project for taxi ride-fare prediction based on a Kaggle notebook with an ML Python script that uses data from the New York City Taxi Fare Prediction competition.\n", + " 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", " \n", " \n", "" @@ -270,7 +272,7 @@ "
Open locally\n", " \n", " \n", - "
View on GitHub\n", + "
View on GitHub\n", " \n", " 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 Kaggle notebook with an ML Python script that uses data from the New York City Taxi Fare Prediction competition.\n", @@ -282,7 +284,7 @@ "
Open locally\n", " \n", " \n", - "
View on GitHub\n", + "
View on GitHub\n", " \n", " Demonstrates how to run a Spark job that reads a CSV file and logs the data set to an MLRun database.\n", " \n", @@ -293,7 +295,7 @@ "
Open locally\n", " \n", " \n", - "
View on GitHub\n", + "
View on GitHub\n", " \n", " Demonstrates how to create and run a Spark job that generates a profile report from an Apache Spark DataFrame based on pandas profiling.\n", " \n", @@ -304,7 +306,7 @@ "
Open locally\n", " \n", " \n", - "
View on GitHub\n", + "
View on GitHub\n", " \n", " Demonstrates how to use Spark Operator to run a Spark job over Kubernetes with MLRun.\n", " \n",