From 66252e9fc4693862145838b711fb0373c8157424 Mon Sep 17 00:00:00 2001 From: Amagash Date: Mon, 30 Aug 2021 17:56:46 +0200 Subject: [PATCH] [Lesson 19] Add Introduction --- 5-Data-Science-In-Cloud/19-tbd/README.md | 37 +++++++++++++++++- .../19-tbd/img/project-schema.PNG | Bin 0 -> 122497 bytes 2 files changed, 35 insertions(+), 2 deletions(-) create mode 100644 5-Data-Science-In-Cloud/19-tbd/img/project-schema.PNG diff --git a/5-Data-Science-In-Cloud/19-tbd/README.md b/5-Data-Science-In-Cloud/19-tbd/README.md index 5695df9..c2f722f 100644 --- a/5-Data-Science-In-Cloud/19-tbd/README.md +++ b/5-Data-Science-In-Cloud/19-tbd/README.md @@ -1,9 +1,42 @@ -# Data Science in the Cloud: TBD - +# Data Science in the Cloud: The "Azure ML SDK" way + +Table of contents: + +- [Data Science in the Cloud: The "Azure ML SDK" way](#data-science-in-the-cloud-the-azure-ml-sdk-way) + - [Pre-Lecture Quiz](#pre-lecture-quiz) + - [1. Introduction](#1-introduction) + - [1.1 What is Azure ML SDK?](#11-what-is-azure-ml-sdk) + - [🚀 Challenge](#-challenge) + - [Post-Lecture Quiz](#post-lecture-quiz) + - [Review & Self Study](#review--self-study) + - [Assignment](#assignment) ## Pre-Lecture Quiz [Pre-lecture quiz]() + +## 1. Introduction + +### 1.1 What is Azure ML SDK? + +Data scientists and AI developers use the Azure Machine Learning SDK to build and run machine learning workflows with the Azure Machine Learning service. You can interact with the service in any Python environment, including Jupyter Notebooks, Visual Studio Code, or your favorite Python IDE. + +Key areas of the SDK include: + +- Explore, prepare and manage the lifecycle of your datasets used in machine learning experiments. +- Manage cloud resources for monitoring, logging, and organizing your machine learning experiments. +- Train models either locally or by using cloud resources, including GPU-accelerated model training. +- Use automated machine learning, which accepts configuration parameters and training data. It automatically iterates through algorithms and hyperparameter settings to find the best model for running predictions. +- Deploy web services to convert your trained models into RESTful services that can be consumed in any application. + +[Learn more about the Azure Machine Learning SDK](https://docs.microsoft.com/en-us/python/api/overview/azure/ml/?view=azure-ml-py) + +In the [previous lesson](../18-tbd/README.md), we saw how to train, deploy and consume a model in a Low code/No code fashion. We used the Heart Failure dataset to generate and Heart failure prediction model. In this lesson, we are going to do the exact same thing but using the Azure Machine Learning SDK. + +![project-schema](img/project-schema.PNG) + +Check [here](../18-tbd/README.md) the Heart failure prediction project and dataset introduction. + ## 🚀 Challenge diff --git a/5-Data-Science-In-Cloud/19-tbd/img/project-schema.PNG b/5-Data-Science-In-Cloud/19-tbd/img/project-schema.PNG new file mode 100644 index 0000000000000000000000000000000000000000..9d330f14f71ef138ec4627b2ccef6429d0c5585e GIT binary patch literal 122497 zcmeEvWn9yH8@F-90y)P*La;y@1V+dJ6#?mP*;FK_fYj(6MLFsiq=FbBy8GzJ2>>;N%My?)Tq2x8SNMd1d>TOj;H}%XALO*Hw9Ms9?f-bB-5C93O8Uc5n>+e;`}PUEBmcLb{h`6>-?3qjrs(@?-H)9&g|K zlpj-+&97$HIr9Icj)0t$-Uv1h`Dfz z^5xe$cYYoG|CrUSs!LcHZfhukX-B#myV8|vl$y)+CT_rCHtghIwJYpOmcXA2s+L+R zI-y}axSsmd$4_qi?3s6`IY zB&PwvL=As&8Yt}i`oTjh3>1wT2-*F5;In1#r!BXs7fb>MAWGE9{huK0QYWs}RLeQF zLq07UGf-~Da`l^T=j+X}3`=dSZoEvq_o(@c&+q5B-r^Ztq}Z+riK<%MnZ`>~ZQrF( 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