- Article
REFERS TO:
Python-SDK azure-ai-mlv2
In this tutorial you will learn how to:
- Upload your data to cloud storage
- Create an Azure Machine Learning data resource
- Access your notebook data for interactive programming
- Create new versions of your data assets
Starting a machine learning project usually involves exploratory data analysis (EDA), data pre-processing (cleaning, feature engineering), and prototyping machine learning models to test hypotheses. ThisprototypesThe design phase is highly interactive. Suitable for programming in IDE or Jupyter notebook, zPython interactive console. This tutorial covers these ideas.
previous requirements
To use Azure Machine Learning, you first need a workspace. If you don't have it, fill it inCreate the resources you need to get startedto create a workspace and learn more about how to use it.
To registertestand select your workspace if it's not already open.
Open or create a notebook in your workspace:
- To createnew notebookif you want to copy/paste code into cells.
- open ittutoriales/get-started-notebooks/explore-data.ipynbvanmonstersstudy section. then chooseCloneto add a notebook to yoursAction. (See where to find examples.)
configure your core
In the top bar above an open notebook, create a compute instance if you don't already have one.
If the compute instance is stopped, selectstarts the calculationand wait for it to run.
Make sure the kernel is present in the top right corner
Python 3.10 — SDK v2. If not, use the drop-down menu to select that kernel.If you see a banner telling you to authenticate, selectAuthenticeren.
Important
The rest of this tutorial contains cells from the Tutorial Notebook. Copy and paste them into a new notebook or switch to the notebook now if it's cloned.
Download the data used in this tutorial
For data ingestion, Azure Data Explorer processes raw datathese formats. It's a tutorial, use itSample credit card customer data in CSV format. We can see the steps continue in the Azure Machine Learning resource. In this resource we will create a local folder with the suggested namedeendirectly under the folder where this notebook is located.
Use
This tutorial is based on data placed in the Azure Machine Learning resource folder location. In this tutorial, "local" means the location of the folder on this Azure Machine Learning resource.
To electopen terminalunder the three dots as shown in this image:
The terminal window opens in a new tab.
Make sure that
CDto the same folder where this notebook is located. For example, if your notebook is in a folder namednotebooks to start with:cd get-started-notebooks # change this to the path where your laptop is locatedType these commands in the terminal window to copy the data to the compute instance:
mkdir datacd data # subdirectory where you will store datawget https://azuremlexamples.blob.core.windows.net/datasets/credit_card/default_of_credit_card_clients.csvNow you can close the terminal window.
More information on this data can be found in UCI's machine learning repository.
Create an ID for the workspace
Before we dive into the code, you need a way to reference your workspace. you will createml_clientfor the workspace ID. then you will benefitml_clientmanage resources and jobs.
In the next cell, enter your subscription ID, resource group name, and workspace name. To find these values:
- Select the name of your workspace from the top right toolbar of Azure Machine Learning Studio.
- Copy the workspace, resource group, and subscription ID into the code.
- You have to copy the value, close and paste the area, then come back for the next one.
from azure.ai.ml import MLClientfrom azure.identity import DefaultAzureCredentialfrom azure.ai.ml.entities import Datafrom azure.ai.ml.constants import AssetTypes# Authenticatecredential = DefaultAzureCredential()# Verkrijg een identificatie van de espacio de trabajoml_client = MLClient( credential= credential, subscribe_id="<SUBSCRIPTION_ID>", resource_group_name="<RESOURCE_GROUP>", workspace_name="<AML_WORKSPACE_NAME>",)Use
Make MLClient does not connect to the workspace. Client initialization is lazy, it will wait for the first time to call (this happens in the next code cell).
Upload data to cloud storage
Azure Machine Learning uses Uniform Resource Identifiers (URIs) that point to storage locations in the cloud. The URI makes it easy to access data in notebooks and tasks. Data URI formats are similar to the URLs you use in your web browser to access web pages. For example:
- Access public https server data:
https://<account_name>.blob.core.windows.net/<container_name>/<folder>/<file> - Azure Data Lake Gen 2 access credentials:
abfss://<filesystem>@<account name>.dfs.core.windows.net/<folder>/<file>
The Azure Machine Learning data source is similar to web browser bookmarks (favorites). Instead of memorizing long storage paths (URIs) that point to the data you use most often, you can create a data item and then access it with a friendly name.
Creating data assets also creates a filereferenceto the location of the data source along with a copy of the metadata. Because the data remains in its current location, you have no additional storage costs and you do not jeopardize the integrity of the data source. You can create data assets from Azure Machine Learning data stores, Azure Storage, public URLs, and local files.
She works
For smaller data workloads, creating Azure Machine Learning data assets works well for loading data from on-premises machine resources into cloud storage. This approach avoids the need for additional tools or utilities. For example, a larger data load may require a special tool or utilitya cup of tea. The azcopy command-line tool moves data to and from Azure Storage. learn more abouta cup of tea.
The next cell in the notebook creates the data source. The sample code uploads a raw data file to a designated cloud storage resource.
Each time you create a data asset, you need a unique version for it. If the version already exists, an error message appears. In this code, we use time to generate a unique version each time a cell is executed.
You can also use theversionand a version number is generated, starting with 1 and going up. For this tutorial, we want to reference specific version numbers, so we'll create a version number instead.
from azure.ai.ml.entities import Datafrom azure.ai.ml.constants import AssetTypesimport time# update "my_path" to match the location where the data was retrieved in the file system # localmy_path = "./data/ default_of_credit_card_clients. csv " # set the data source version number to current UTC timev1 = time.strftime("%Y.%m.%d.%H%M%S", time.gmtime())my_data = Data( name= " credit card", version=v1, description="Credit card details", path=my_path, type=TiposDeActivo.URI_FILE,)# create data assetml_client.data.create_or_update(my_data)print( f"Data asset created. Name: { my_data. name} , version: {my_data.version}")The uploaded data can be viewed by selectingDeenon the left side. You will see the data being loaded and the data source being created:
This data has been namedcredit card, and indata sourcestab, we can see itNamecolumn. This data is loaded into your workspace's default datastore namedblob storage workspace, seen inData sourcecolumn.
The Azure Machine Learning data warehouse is areferenceto oneexistingstorage account in Azure. The data warehouse offers the following benefits:
- A common and easy-to-use API for interacting with different storage types (Blob/Files/Azure Data Lake Storage) and authentication methods.
- An easier way to discover useful data archives while collaborating.
- In scripts, a way to hide connection information to access data based on credentials (service principal/SAS/key).
Access your notepad data
Pandas directly supports URIs - this example shows how to read a CSV file from an Azure Machine Learning datastore:
zaimportuj pandy jako pddf = pd.read_csv("azureml://subscriptions/<subid>/resourcegroups/<rgname>/workspaces/<workspace_name>/datastores/<datastore_name>/paths/<folder>/<filename>.csv" )However, as mentioned above, these URIs can be hard to remember. You also have to replace them all manually<subchain>valuespd.read_csvcommand with the actual values of its sources.
You want to create data assets for frequently used data. Here's an easier way to access a CSV file in Pandas:
Important
Run this code in a notebook cell to installazureml-fsspecPython library in Jupyter kernel:
% pip install -U azureml-fsspecimport pandas as pd# get data asset id and print urid data_asset = ml_client.data.get(name="credit-card", version=v1)print(f"data asset URI: {data_asset.path}") # read in pandach - note that you will see 2 headers in the dataframe - ok for now df = pd.read_csv(data_asset.path)df.head()DurationAccess data from Azure cloud storage during interactive developmentfor more information about accessing notebook data.
Create a new version of the data item
You may have noticed that the data needs a bit of cleaning to make it suitable for training a machine learning model. Has:
- two cups
- customer ID column; we wouldn't use this feature in machine learning
- spaces in the answer variable name
Also, compared to CSV format, Parquet file format becomes a better way to store this data. The parquet provides compression and maintains the schedule. Therefore, to clear the data and save it in Parquet, use:
# read the data again, this time with the second row as the header =True)# drop ID columndf.drop("ID", as=1, inplace=True)# write the file to the filesystemdf.to_parquet("./data /cleaned up - credit card.parquet")This table shows the data structure in the original.default_of_credit_card_clients.csvThe .CSV file you downloaded in the previous step. The loaded data contains 23 explanatory variables and 1 response variable, as shown below:
| column names | variabel type | Description |
|---|---|---|
| X1 | explanatory | Credit Amount (NT Dollars): Includes both individual consumer credit and family credit (additional). |
| x2 | explanatory | Gender (1 = male; 2 = female). |
| X3 | explanatory | Education (1 = postgraduate; 2 = college; 3 = high school; 4 = other). |
| X4 | explanatory | Marital status (1 = married; 2 = single; 3 = other). |
| X5 | explanatory | Annual). |
| X6-X11 | explanatory | History of previous payments. We keep previous monthly payment data (April to September 2005). -1 = payment due; 1 = one month late payment; 2 = two months late payment; . . .; 8 = eight months in arrears; 9 = payment arrears of at least nine months. |
| X12-17 | explanatory | Statement Amount (NT Dollars) April to September 2005 |
| X18-23 | explanatory | Previous payment amount (in NT dollars) from April to September 2005. |
| Y | Answer | Standard payment (Yes = 1, No = 0) |
Then create a new oneversiondata source (data is automatically uploaded to the cloud storage):
Use
This cell of python code assembliesnamejversionvalues for the data source being created. As a result, the code in that cell will fail if it is run more than once without changing these values. He fixed itnamejversionvalues provide a way to pass values that work in specific situations without worrying about automatically or randomly generated values.
from azure.ai.ml.entities import Datafrom azure.ai.ml.constants import AssetTypesimport time# Then create a new *version* of the data item (data is automatically uploaded to the cloud storage): v2 = v1 + "_cleaned " my_path = " ./data/cleaned-credit-card.parquet"# Define the data item and use tags to make it clear that the resource can be used in training my_data = Data( name="credit-card ", version=v2 , description="Default credit card customer data.", tags={"training_data": "true", "format": "parquet"}, path=my_path, type=AssetTypes.URI_FILE, )## create data assetmy_data = ml_client .data. create_or_update (my_data)print(f"Data source created. Name: {my_data.name}, version: {my_data.version}")The clean parquet file is the data source for the latest version. This code displays the CSV version result set first, then the Parquet version:
importeer panda's als pd# haal data asset id op en print URIdata_asset_v1 = ml_client.data.get(name="credit-card", version=v1)data_asset_v2 = ml_client.data.get(name="credit-card", version=v2 )# print data trace v1(f"URI van data asset v1: {data_asset_v1.path}") ")print(f"V2 data asset URI: {data_asset_v2.path}")v2df = pd.read_parquet(data_asset_v2.path) print(v2df.head(5))clean resources
If you plan to continue with other tutorials now, please visitNext steps.
stop the compute instance
If you're not going to use it now, stop the compute instance:
- In a survey, select in the left navigation areaCalculate.
- Select from the top tabscalculate instances
- Select a compute instance from the list.
- Select in the top toolbarTo arrest.
Remove all sources
Important
The resources you create can be used as prerequisites for other Azure Machine Learning tutorials and how-to articles.
If you don't plan to use resources you've created, delete them to avoid being charged:
Select in the Azure portalresource groupsto the left.
Select the resource group you created from the list.
To electDelete the resource group.
Enter a resource group name. then chooseTo delete.
Next steps
DurationCreate data assetsfor more information on data sources.
DurationCreate data warehousesfor more information about data stores.
Continue through the tutorials to learn how to develop a training script.
Model development on a cloud workstation
FAQs
How do I access data from Datastore in Azure ML? ›
Datastores can be accessed directly in code by using the Azure Machine Learning SDK and further use it to download or upload data or mount a datastore in an experiment to read or write data.
Is Azure Machine Learning easy? ›Azure Machine Learning saves both cost and time, along with making development easy. Who would have thought that one could build Machine Learning models using features like drag and drop? It is possible to do so in Azure Machine Learning Studio, and it offers almost all major algorithms built-in to work on.
How do I access Azure ML dataset? ›In Machine Learning Studio (classic), click DATASETS in the navigation bar on the left. Select the dataset you would like to access. You can select any of the datasets from the MY DATASETS list or from the SAMPLES list.
How do you retrieve data from table storage in Azure? ›Enter an Account Name, Account Key, and Table Name on the Azure Table tab of the New Session dialog. Select either HTTP or HTTPS as the connection Protocol. Ensure that the Analysis Grid viewer is selected in the Start With drop-down list. Start retrieving data by clicking the Start button in the New Session dialog.
What is the difference between datastore and Dataset in Azure? ›While a Datastore acts as a container for your data files, you can think of a Dataset as a reference or pointer to specific data that's in your datastore. The following Datasets types are supported: TabularDataset represents data in a tabular format created by parsing the provided file or list of files.
How do I load a ML model? ›To load a saved model from a Pickle file, all you need to do is pass the “pickled” model into the Pickle load() function and it will be deserialized. By assigning this back to a model object, you can then run your original model's predict() function, pass in some test data and get back an array of predictions.
How do you train a ML model for dataset? ›- Step 1: Begin with existing data. Machine learning requires us to have existing data—not the data our application will use when we run it, but data to learn from. ...
- Step 2: Analyze data to identify patterns. ...
- Step 3: Make predictions.
- Create a new Python control script in the get-started folder (make sure it is in get-started, not in the /src folder). Name the script upload-data.py and copy this code into the file: ...
- Select Save and run script in terminal to run the upload-data.py script.
Learn Azure in a Month of Lunches gets you started by breaking down the most important concepts and tasks into 21 bite-sized lessons, complete with examples, exercises, and labs. You'll be productive immediately, and when you finish you'll be well on the way to Azure mastery!
Which is the most difficult Azure exam? ›Expert in Azure Solutions Architecture
Earning the Azure Solutions Architect Expert certification and passing its two difficult certification exams is one of the most difficult feats in cloud certification. So it comes as no surprise that CIO has named it one of the most in-demand IT certifications for 2021.
Can I learn Azure without coding? ›
Yes, you can learn Microsoft Azure without learning to program. But this would restrict your work roles to just non-technical roles. If you're a beginner, learning a programming language is recommended, such as Python, to get a high-paying job in the cloud computing industry.
How do I use Azure ML in Excel? ›Use Azure ML web services
All you need to do is add the web service by providing the URL (found on the API help page) and the API key (found on the API dashboard page). When you save the Excel workbook, your web services are also saved, so you can share the workbook with others and enable them to use the web service.
- On the File menu, select Connect to SQL Azure (this option is enabled after the creation of a project). ...
- In the connection dialog box, enter or select the server name of Azure SQL Database.
- Enter, select, or Browse the Database name.
- Enter or select Username.
- Enter the Password.
- Sign in to the Azure Machine Learning studio.
- Under Assets in the left navigation, select Data.
- Select the name of the dataset you want to view.
- Select the Explore tab.
- Select the Preview tab.
- Select the Profile tab.
- Select the Export from Azure transfer type.
- Select the subscription to use for the Import/Export job.
- Select a resource group.
- Select the Source Azure region for the job.
- Select the Destination country/region for the job.
- Then select Apply.
- Create a data factory.
- Create Azure SQL Database, Azure Synapse Analytics, and Azure Storage linked services.
- Create Azure SQL Database and Azure Synapse Analytics datasets.
- Create a pipeline to look up the tables to be copied and another pipeline to perform the actual copy operation.
- Start a pipeline run.
The Log Analytics user interface in the Azure portal helps you query the log data collected by Azure Monitor so that you can quickly retrieve, consolidate, and analyze collected data.
What are the 3 types of data that can be stored in Azure? ›- File.
- Blob.
- Queue.
- Table.
So, anything somehow related to the data is a part of the dataset. Dataflow: Dataflow is the data transformation component of Power BI, which is independent of any other Power BI artifacts. It is a power query process that runs in the cloud and stores the data in Azure Data Lake storage or Dataverse.
What is the difference between schema and DataSet? ›The main difference between schema and database is that schema is a logical definition of a database that defines the tables, columns and types of columns, while the database is a collection of related data stored in tables. Database refers to a set of data.
How do I feed new data to ML model? ›
- Articulate the problem early.
- Establish data collection mechanisms. ...
- Check your data quality.
- Format data to make it consistent.
- Reduce data.
- Complete data cleaning.
- Create new features out of existing ones.
- Contextualise machine learning in your organisation.
- Explore the data and choose the type of algorithm.
- Prepare and clean the dataset.
- Split the prepared dataset and perform cross validation.
- Perform machine learning optimisation.
- Deploy the model.
In machine learning, the combining of models is done by using two approaches namely “Ensemble Models” & “Hybrid Models”. Ensemble Models use multiple machine learning algorithms to bring out better predictive results, as compared to using a single algorithm.
What is the difference between test data and train data? ›The main difference between training data and testing data is that training data is the subset of original data that is used to train the machine learning model, whereas testing data is used to check the accuracy of the model. The training dataset is generally larger in size compared to the testing dataset.
How do you practice machine learning models? ›- Step 1: Adjust Mindset. Believe you can practice and apply machine learning. ...
- Step 2: Pick a Process. Use a systemic process to work through problems. ...
- Step 3: Pick a Tool. ...
- Step 4: Practice on Datasets. ...
- Step 5: Build a Portfolio.
To create a dataset with the Azure Data Factory Studio, select the Author tab (with the pencil icon), and then the plus sign icon, to choose Dataset. You'll see the new dataset window to choose any of the connectors available in Azure Data Factory, to set up an existing or new linked service.
What are Azure machine learning Datasets? ›Represents a resource for exploring, transforming, and managing data in Azure Machine Learning. A Dataset is a reference to data in a Datastore or behind public web urls.
How do I transfer data to Azure? ›You can also go to your Azure Storage account in Azure portal and select the Data transfer feature. Provide the network bandwidth in your environment, the size of the data you want to transfer, and the frequency of data transfer.
Can a non it person learn Azure? ›Yes! There is no pre-requisite in learning Azure and the AZ-900 in this platform will help you understand Azure basics and for sure can make you explain what each and every offering Azure currently has.
Can I get a job if I learn Azure? ›A Microsoft azure certification can enable you to pursue a wide range of career options. You can become a cloud architect, a developer, or a solution architect. In addition, the certification enables you to work in various industries at different locations.
Is learning Azure difficult? ›
Both Azure and AWS may be difficult to learn if you don't know what you're doing, or they can be quite simple if you're well instructed. Many IT experts, however, argue that AWS is far easier to learn and obtain certification in.
How many times can you fail Azure exam? ›If you don't pass an exam the first time, you must wait 24 hours before retaking it. A 14-day waiting period is imposed between all subsequent attempts (up to 5). You may not take a given exam more than five (5) times within a 12-month period from the first attempt.
Which course is easy in Azure? ›Azure DevOps Fundamentals for Beginners [Udemy]
Since this course is designed for people who are new to DevOps terms and concepts, I highly recommend to both beginners and experienced software developers join this course.
It takes the majority of Azure solutions experts over 2 months to prepare for the Azure Solutions Architect Expert exam – most of them over 4 months. If you're studying for the AZ-303 and you want to earn the Azure Architect cert, you should prepare yourself to take as long as 4 months or more to study for it.
Should I learn Azure or Python? ›Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best. Azure Machine Learning belongs to "Machine Learning as a Service" category of the tech stack, while Python can be primarily classified under "Languages".
How do I get started with Azure Machine Learning? ›- Set up a handle to your Azure Machine Learning workspace.
- Create your training script.
- Create a scalable compute resource, a compute cluster.
- Create and run a command job that will run the training script on the compute cluster, configured with the appropriate job environment.
Azure Machine Learning designer: use the designer to train and deploy machine learning models without writing any code.
Is Azure ML worth it? ›Microsoft Azure Machine Learning provides highest availability and is very pocket friendly for any sized company. Its intelligent bot service provides great customer service by interacting them with very high speed.
Can I run Excel on Azure? ›Yes. To open Excel from the web portal, install the Azure DevOps Open in Excel Marketplace extension. Otherwise, you can open Excel and then open a query that you've created in the web portal or from Team Explorer.
Where is data stored in Azure? ›Most Azure services enable you to specify the region where your customer data will be stored and processed. Microsoft may replicate to other regions for data resiliency, but Microsoft will not store or process customer data outside the selected Geo.
What is Azure Data Explorer used for? ›
Azure Data Explorer is a fast, fully managed data analytics service for real-time analysis on large volumes of data streaming from applications, websites, IoT devices, and more.
What is the difference between datastore and dataset in Azure? ›While a Datastore acts as a container for your data files, you can think of a Dataset as a reference or pointer to specific data that's in your datastore.
How do I access data from datastore in Azure ML? ›Datastores can be accessed directly in code by using the Azure Machine Learning SDK and further use it to download or upload data or mount a datastore in an experiment to read or write data.
How to read data from datastore? ›data = read( ds ) returns data from a datastore. Subsequent calls to the read function continue reading from the endpoint of the previous call. [ data , info ] = read( ds ) also returns information about the extracted data in info , including metadata.
How do I access data on Azure storage? ›- In Storage Explorer, select View > Account Management or select the Manage Accounts button.
- ACCOUNT MANAGEMENT now displays all the Azure accounts you're signed in to. ...
- The Connect to Azure Storage dialog opens. ...
- In the Select Azure Environment panel, select an Azure environment to sign in to.
- Download the data from Azure blob with the following Python code sample using Blob service. Replace the variable in the following code with your specific values: Python Copy. ...
- Read the data into a pandas DataFrame from the downloaded file. Python Copy.
Click your host, then under Storage, righ click your datastore and choose Browse Datastore. This will take you to a Datastore Browser window where you have the option to upload files, etc.
What is the difference between DataStore and database? ›A data store is a repository for persistently storing and managing collections of data which include not just repositories like databases, but also simpler store types such as simple files, emails, etc. A database is a series of bytes that is managed by a database management system (DBMS).
What is DataStore in Azure ML? ›Datastores are attached to workspaces and are used to store connection information to Azure storage services so you can refer to them by name and don't need to remember the connection information and secret used to connect to the storage services.
Which type of datastores are created by Azure machine learning? ›An Azure Machine Learning datastore serves as a reference to an existing Azure storage account. The benefits of Azure Machine Learning datastore creation and use include: A common, easy-to-use API that interacts with different storage types (Blob/Files/ADLS).
What is the difference between Azure files and blob storage? ›
In summary, the difference between the two storage services is that Azure Blob Storage is a store for objects capable of storing large amounts of unstructured data. On the other hand, Azure File Storage is a distributed, cloud-based file system.
What is used to access data stored in Azure storage account? ›You can use Azure RBAC for granular control over a client's access to Azure Files resources in a storage account.
How many types of storage are there in Azure? ›In Microsoft Azure, you can choose two Storage Account types. They are General-Purpose Account and Blob Storage account.
What is the difference between storage account and blob storage? ›What is the difference between blob and file storage? Azure Blob Storage is an object store used for storing vast amounts unstructured data, while Azure File Storage is a fully managed distributed file system based on the SMB protocol and looks like a typical hard drive once mounted.
Can we query data from blob storage? ›Query Blob Storage BigLake tables
After creating a Blob Storage BigLake table, you can query it using GoogleSQL syntax, the same as if it were a standard BigQuery table. The cached query results are stored in a BigQuery temporary table. To query a temporary BigLake table, see Query a temporary BigLake table.
Highly scalable NoSQL database
Learn more about upgrading to Firestore. Datastore is a highly scalable NoSQL database for your applications. Datastore automatically handles sharding and replication, providing you with a highly available and durable database that scales automatically to handle your applications' load.
A data store can be network-connected storage, distributed cloud storage, a physical hard drive, or virtual storage. It can store both structured data like information tables and unstructured data like emails, images, and videos.
What are the DataStore types? ›- Relational database.
- Non-relational (“NoSQL”) database.
- Key-value store.
- Full-text search engine.
- Message queue.