You're reading from Engineering MLOps

Product typeBook

Published inApr 2021

PublisherPackt

ISBN-139781800562882

Edition1st Edition

Tools

Azure Functions

Concepts

Machine Learning

Author (1)

Emmanuel Raj

Data ingestion and feature engineering

Data is essential to train ML models; without data, there is no ML. Data ingestion is a trigger step for the ML pipeline. It deals with the volume, velocity, veracity, and variety of data by extracting data from various data sources and ingesting the needed data for model training.

The ML pipeline is initiated by ingesting the right data for training the ML models. We will start by accessing the preprocessed data we registered in the previous chapter. Follow these steps to access and import the preprocessed data and get it ready for ML training:

Using the Workspace() function from the Azure ML SDK, access the data from the datastore in the ML workspace as follows:

from azureml.core import Workspace, Dataset
subscription_id = 'xxxxxx-xxxxxx-xxxxxxx-xxxxxxx'
resource_group = 'Learn_MLOps'
workspace_name = 'MLOps_WS'
workspace = Workspace(subscription_id, resource_group, workspace_name)

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Engineering MLOps

Published in: Apr 2021Publisher: PacktISBN-13: 9781800562882

Author (1)

Emmanuel Raj

Emmanuel Raj is a Finland-based Senior Machine Learning Engineer with 6+ years of industry experience. He is also a Machine Learning Engineer at TietoEvry and a Member of the European AI Alliance at the European Commission. He is passionate about democratizing AI and bringing research and academia to industry. He holds a Master of Engineering degree in Big Data Analytics from Arcada University of Applied Sciences. He has a keen interest in R&D in technologies such as Edge AI, Blockchain, NLP, MLOps and Robotics. He believes "the best way to learn is to teach", he is passionate about sharing and learning new technologies with others.
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