From f888707368404a002c742c69d0307f641b5c7a83 Mon Sep 17 00:00:00 2001 From: jilnogold Date: Mon, 24 Apr 2023 14:50:51 +0300 Subject: [PATCH] add "v" to github paths: v1.3.x-latest --- welcome.ipynb | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/welcome.ipynb b/welcome.ipynb index 8d367c0..87f40e3 100644 --- a/welcome.ipynb +++ b/welcome.ipynb @@ -195,7 +195,7 @@ "
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\n", " \n", " This demo contains 3 notebooks where we:\n", @@ -210,7 +210,7 @@ "
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\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 @@ "
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\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 @@ "
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\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 @@ "
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\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", @@ -301,7 +301,7 @@ "
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\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 @@ "
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\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 @@ "
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\n", " \n", " Demonstrates how to use Spark Operator to run a Spark job over Kubernetes with MLRun.\n", " \n",