diff --git a/README.md b/README.md index ec0a3a7..19561c0 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ -# 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) @@ -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 @@ -55,7 +55,11 @@ The home directory of the platform's running-user directory (**/User/<running ## 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. + +View tutorial + +You can also view the tutorial on [GitHub](https://docs.mlrun.org/en/latest/tutorial/01-mlrun-basics.html). diff --git a/welcome.ipynb b/welcome.ipynb index 5779c9c..57e04dd 100644 --- a/welcome.ipynb +++ b/welcome.ipynb @@ -1,12 +1,13 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "# 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" ] }, { @@ -31,12 +32,13 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## 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", @@ -101,6 +103,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -110,7 +113,7 @@ "\n", "\"View\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)." ] }, { @@ -175,6 +178,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -191,7 +195,7 @@ "
Open locally\n", " \n", " \n", - " \n", + " \n", "
View on GitHub\n", " \n", " This demo contains 3 notebooks where we:\n", @@ -206,7 +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", @@ -218,7 +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", @@ -230,13 +234,26 @@ "
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", "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", + "\t \n", + " Stocks Prediction\n", + " \n", + "
Open locally\n", + " \n", + " \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", + "\t\tand it creates a Grafana dashbord for model analysis.\n", + " \n", + " \n", "" ] }, @@ -255,6 +272,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -271,7 +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", @@ -283,7 +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", @@ -294,7 +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", @@ -305,7 +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", @@ -407,13 +425,14 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "\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/)." ] }, { @@ -443,7 +462,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.6 (main, Nov 14 2022, 16:10:14) [GCC 11.3.0]" }, "vscode": { "interpreter": {