This document discusses DevOps for data scientists using Azure services. It introduces Azure DevOps as a tool that provides developer services to support teams in planning work, collaborating on code development, and building and deploying applications. Azure Notebooks is introduced as a hosted Jupyter notebook service in Azure that allows data scientists to develop and run notebooks in the cloud without installation. Azure Boards is described as providing Agile tools like Kanban boards and backlogs to support tracking work in Scrum and Kanban methodologies.

1. DEVOPS FOR DATA SCIENTISTS
Stefano Tucci - MCP
stefano.tucci@outlook.com

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Business: Economics limited budgets/Economies of scale/Resiliency/Ease of
access/Consistent experience/Access and Policies/Data Quality/Predictive
Analytics/real Time data Creation.
IT Manager-Application Team: Economics limited budgets/Economies of
scale/Resiliency/Complex legacy applications/Rapid development/Familiar
tools.
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#DifferentVision
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COMMUNICATION
COLLABORATION
INTEGRATION
DEV
OPS

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Azure DevOps provides developer services to
support teams to plan work, collaborate on code
development, and build and deploy applications.
Developers can work in the cloud using Azure
DevOps Services.
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#AzureDevOps

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Azure Repos provides Git repositories or Team Foundation Version Control (TFVC) for
source control of your code
Azure Boards delivers a suite of Agile tools to support planning and tracking work,
code defects, and issues using Kanban and Scrum methods
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#AzureDevOps

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Azure Notebooks is a free hosted service to develop and run Jupyter notebooks in the
cloud with no installation.
Jupyter (formerly IPython) is an open-source project that lets you easily combine
Markdown text, executable code, persistent data, graphics, and visualizations onto a
single, sharable canvas, the notebook (image courtesy of jupyter.org)
AZURENOTEBOOKS
#AzureNotebooks

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Notebook Jupyter è un’applicazione basata sul modello client-server dell’organizzazione no
profit Progetto Jupyter fondata nel 2015. Permette la creazione e la condivisione di
documenti web nel formato JSON, che seguono uno schema e una lista ordinata di celle
input/output. Queste celle offrono tra l’altro spazio per codici, testi in markdown, formule
matematiche ed equazioni o contenuti multimediali (rich media).
L’elaborazione funziona su un’applicazione client basata sul web che si avvia con un
browser standard. Basta che sul sistema sia installato e venga eseguito anche il server del
Notebook Jupyter. I documenti Jupyter creati si possono esportare come documenti HTML,
PDF, Markdown o Python o in alternativa si possono condividere con altri utenti tramite e-
mail, Dropbox, GitHub o il proprio Notebook Jupyter
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Due componenti centrali di Notebook Jupyter sono un set di diversi kernel
(interpreti) e la dashboard. I kernel sono piccoli programmi che elaborano richieste
(“request”) specifiche nel linguaggio e reagiscono con relative risposte. Un kernel
standard è IPython, un interprete della riga di comando che permette di lavorare
con Python. La dashboard serve da una parte come interfaccia di gestione per i
singoli kernel e dall’altra come centrale per la creazione di nuovi documenti
Notebook o per aprire progetti già esistenti. Notebook Jupyter è disponibile
gratuitamente per tutti gli utenti grazie a una licenza BSD modificata.
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Notebook Jupyter mette a disposizione un ambiente perfetto fatto su misura per le esigenze
e il flusso di lavoro di scienza e simulazione dei dati. In una sola istanza gli utenti possono
scrivere, documentare ed eseguire codici, visualizzare dati, eseguire calcoli ed esaminare i
risultati corrispondenti. In particolare durante la fase di prototipo possono trarre beneficio
dal fatto che ciascun codice può essere ospitato in celle indipendenti: così è possibile testare
individualmente specifici blocchi di codici. Grazie ai numerosi kernel aggiuntivi Jupyter non si
limita a Python per quanto riguarda il linguaggio di programmazione e ciò significa più
flessibilità al momento della codifica e dell’analisi.
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Tra gli scopi d’utilizzo più importanti di Notebook Jupyter si possono menzionare:
• Pulizia dei dati: differenziazione tra dati importanti e meno importanti nell’analisi dei big
data
• Modellizzazione statistica: metodo matematico per determinare la stimata probabilità di
distribuzione di una determinata caratteristica
• Creazione e training di modelli di machine learning: progetto, programmazione e training
di modelli basati sul machine learning
• Visualizzazione dati: rappresentazione grafica di dati per spiegare modelli, tendenze,
dipendenze ecc.
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Azure Notebooks helps you to get started quickly on prototyping, data science, academic
research, or learning to program Python:
• A data scientist has instant access to a full Anaconda environment with no installation.
• A teacher can provide a hassle-free Python environment to students.
• A presenter can give a like talk or webinar without asking attendees to spend 45 mins
installing software.
• A developer or hobbyist can use Notebooks as a quick code scratchpad.
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Notebooks become even more powerful when people can collaborate on them through a
browser-accessible cloud service like Azure Notebooks (in Preview). In the cloud, users
need not install Jupyter locally or concern themselves with maintaining an environment.
The cloud also makes it simple to share notebooks (and associated data files) with other
authorized users, avoiding the complications of sharing notebooks through external means
like source-control repositories.
With Azure Notebooks, users can also copy (or "clone") notebooks into their own account
for modification or experimentation, which is especially useful for instruction purposes.
Azure Notebooks is a free service but each project is limited to 4GB memory and 1GB data
to prevent abuse. Legitimate users that exceed these limits see a Captcha challenge to
continue running notebooks.
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For each notebook, you select the kernel (that is, the runtime environment) that's used to run any code cells.
Azure Notebooks supports the following kernels:
• Python 2.7 + Anaconda2-5.3.0
• Python 3.6 + Anaconda3-5.3.0
• Python 3.5 + Anaconda3-4.2.0 (will be deprecated)
• R 3.4.1 + Microsoft R Open 3.4.1
• F# 4.1.9
AVAILABLEKERNELSANDENVIRONMENTS

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aka.ms/azureboards
@AzureDevOps
AZUREBOARDS
#AzureBoards

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Track work with Kanban boards, backlogs, team
dashboards, and custom reporting.
AZUREBOARDS
Connected from idea to release
Track all your ideas at every development stage and keep
your team aligned with all code changes linked directly to
work items.
Scrum ready
Use built-in scrum boards and planning tools to help your
teams run sprints, stand-ups, and planning meetings.
Project insights
Gain new insights into the health and status of your
project with powerful analytics tools and dashboard
widgets.

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Use work items to find and focus on work you care
about.
WORKITEMSHUB
Find work items assigned to you
Track work items that you’re following or have viewed or
modified recently.
Rich filtering
Filter work items on types, assignment, states, area, and
tags.
Query and Search work items
Query work items within your project or across projects.

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Dashboards give a clear view of what’s happening to
track progress and direction.
DASHBOARDS
Real-time information
Dashboards provide easy-to-read, easy access, real-time
information
Thriving community of widgets
Widgets smartly format data to provide access to easily
consumable data. Add widgets to your team dashboards
to gain visibility into the status and trends occurring as
you develop your software project.
Add dashboards as needed
Tailor configure the layout that makes sense for your
team, and easily monitor progress throughout the
lifecycle of your project.

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Demo

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https://thegeeksalive.com/how-to-create-a-new-git-repository-
and-push-it-to-github/
https://docs.microsoft.com/en-us/azure/notebooks/azure-
notebooks-overview
https://notebooks.azure.com
https://notebooks.azure.com/Microsoft/projects
https://dev.azure.com

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