Manage ML Project Lifecycle using Azure Machine Learning

Overview

Azure Machine Learning is a cloud service for managing the lifecycle of machine learning projects. Also, we can create models in Azure ML or use a built-in model from an open-source platform like TensorFlow and Pytorch, etc.

The primary goal of ML is to enable computers to learn automatically by themselves without human intervention which means computers can recognize, analyze and deliver proper information without the involvement of humans.

Machine Learning is the process of using mathematical solution models to guide a computer to learn without human involvement. ML is a subset of Artificial Intelligence (AI). ML uses some algorithms to identify patterns from the huge data and then those patterns will be used to create a data model which will make predictions.

How are Machine Learning and Artificial Intelligence both related?

Machine Learning is a part of Artificial Intelligence as it implements an algorithm that can learn from the data so that it will be able to perform the tasks without explicit instructions.

Even though they have different ways of doing it, both AI and ML programs can learn from the data and models. They won’t be static and change over time based on the models.

• Machine Learning relates to AI – AI is one way to train a computer to mimic human behavior (uses neural network), which is a series of algorithms that are modeled like the human brain.
• Machine Learning relates to Predictive Analytics Model – It is a type of predictive analytics as it is significant to implement with real time data and at the same time gains more data.
• Machine Learning relates to Deep Learning – Deep Learning is a specialized form of ML that uses Neural Networks to deliver instructions.

Machine Learning Lifecycle

• Process of Preparing Data

Data Labeling – Label training data and manage labeling projects.

Data Preparation – Uses analytics engines for data preparation.

Datasets – Access data and create and share datasets.

• Tools for building and training models

Notebooks – Use Jupyter Notebooks with attached compute.

Automated Machine Learning – It automatically trains accurate models.

CLI and Python SDK – Accelerate the model training process.

Visual Studio Code and GitHub – It switches easily from local to cloud training.

Compute Instance – Secure environment with cloud CPUs, GPUs, and supercomputing clusters.

Open-Source Libraries and frameworks – PyTorch, TensorFlow, Keras.

• Validate and Deploy

Managed Endpoints – Deploy models for batch and real time data.

Pipelines and CI/CD – Automate machine learning workflows.

Prebuilt Images – Access container images with frameworks and libraries.

Model Repository – Share and track models and data.

Hybrid and Multicloud – Train and deploy models on-premises and across multi-cloud environments.

• Manage and Monitor

Monitoring and Analysis – Track, log and analyze data, models, and resources.

Data Drift – Detect drift and maintain model accuracy.

Error Analysis – Debug models and optimize model accuracy.

Auditing – Trace machine learning artifacts for compliance.

Policies – Use built-in and custom policies for compliance management.

Security – Enjoy continuous monitoring with Azure Security Center.

Azure Machine Learning Workspace

The ML workspace is the top most level resource for Azure Machine Learning which provides a centralized place to work with all the artifacts that you create when you use ML.

The workspace keeps the history of all training runs including logs, metrics, output, and snapshots of your scripts. This information will be used to determine which training run produces the best model.

Tools for Interacting with Workspace

• Azure Machine Learning Studio
• Azure Machine Learning Designer
• Azure Machine Learning SDK for Python
• Azure Machine Learning CLI Extension
• Azure Machine Learning VS Code Extension

Sub Resources of a Workspace

The following sub resources are the main resources that will be made in the process of creating the Azure Machine Learning Workspace.

VM – It provides computing power for your Azure Machine Learning Workspace and is also an integral part while deploying and training models.

Load Balancer – It’s a network load balancer that will be created for each compute instance to manage traffic even if the compute instance is stopped.

Virtual Network – It helps resources to communicate one with another and other on-premises networks.

Bandwidth – This encapsulates all outbound data that transfers across the region.

Storage Account – It will be used as the default datastore for the workspace and also it stores the Jupyter Notebooks that are used with your Azure ML compute instances.

Container Registry – Which registers docker containers that are used for the Azure ML environments, AutoML, and Data Profiling.

Application Insights – It stores monitoring and diagnostic information.

Key Vault – It stores secrets that are used by compute targets and other sensitive information which is needed by the workspace.

Advantages

• ML Service can handle a large amount of data and scale up and down as its requirement.
• It mainly manages the infrastructure of both software and hardware.
• It supports various ML frameworks like PyTorch and Tensorflow etc.

Disadvantages

• It is very expensive for large scale projects.
• It is the exact type of other machine learning platforms which may limit its capabilities for other use cases.
• It’s not supported in all the regions.

Conclusion

From this article, we can learn the Azure Machine Learning service details as described in the introduction, its lifecycle, workspace, and workspace sub-resources which are main the components of a workspace while it’s going to be created.

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