diff --git a/README.md b/README.md index 19561c0..93054cf 100644 --- a/README.md +++ b/README.md @@ -102,7 +102,7 @@ For full usage instructions, run the script with the `-h` or `--help` flag:
Open locally - +
View on GitHub This demo contains 3 notebooks where we: @@ -117,7 +117,7 @@ For full usage instructions, run the script with the `-h` or `--help` flag:
Open locally - +
View on GitHub Demonstrates the feature store usage for fraud prevention: Data ingestion & preparation; Model training & testing; Model serving; Building An Automated ML Pipeline. @@ -129,7 +129,7 @@ For full usage instructions, run the script with the `-h` or `--help` flag:
Open locally -
View on GitHub +
View on GitHub 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. 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. @@ -141,7 +141,7 @@ Additionally, we will use MLRun's real-time inference graphs to create the pipel
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View on GitHub 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). The demo implements feature engineering, model training, testing, inference, and model monitoring (with concept-drift detection). @@ -154,7 +154,7 @@ The demo uses a offline/real-time metrics simulator to generate semi-random netw
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View on GitHub +
View on GitHub 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 used machine and deep learning frameworks, providing features such as automatic logging, model management, and distributed training). The demo predicts stock prices, @@ -180,7 +180,7 @@ The demo uses a offline/real-time metrics simulator to generate semi-random netw
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View on GitHub +
View on GitHub Demonstrates how to convert existing 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. @@ -192,7 +192,7 @@ The demo uses a offline/real-time metrics simulator to generate semi-random netw
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View on GitHub +
View on GitHub Demonstrates how to run a Spark job that reads a CSV file and logs the data set to an MLRun database. @@ -203,7 +203,7 @@ The demo uses a offline/real-time metrics simulator to generate semi-random netw
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View on GitHub +
View on GitHub Demonstrates how to create and run a Spark job that generates a profile report from an Apache Spark DataFrame based on pandas profiling. @@ -214,7 +214,7 @@ The demo uses a offline/real-time metrics simulator to generate semi-random netw
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View on GitHub +
View on GitHub Demonstrates how to use Spark Operator to run a Spark job over Kubernetes with MLRun. diff --git a/welcome.ipynb b/welcome.ipynb index 57e04dd..8d367c0 100644 --- a/welcome.ipynb +++ b/welcome.ipynb @@ -195,7 +195,7 @@ "
Open locally\n", " \n", " \n", - " \n", + " \n", "
View on GitHub\n", " \n", " This demo contains 3 notebooks where we:\n", @@ -210,7 +210,7 @@ "
Open locally\n", " \n", " \n", - " \n", + " \n", "
View on GitHub\n", " \n", " Demonstrates the feature store usage for fraud prevention: Data ingestion & preparation; Model training & testing; Model serving; Building An Automated ML Pipeline.\n", @@ -222,7 +222,7 @@ "
Open locally\n", " \n", " \n", - "
View on GitHub\n", + "
View on GitHub\n", " \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", @@ -234,7 +234,7 @@ "
Open locally\n", " \n", " \n", - "
View on GitHub\n", + "
View on GitHub\n", " \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", @@ -247,7 +247,7 @@ "
Open locally\n", " \n", " \n", - "
View on GitHub\n", + "
View on GitHub\n", " \n", " 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", @@ -289,7 +289,7 @@ "
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", @@ -301,7 +301,7 @@ "
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", @@ -312,7 +312,7 @@ "
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", @@ -323,7 +323,7 @@ "
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",