Azure Machine Learning Engineering Process and Best Practices

To successfully implement Azure Machine Learning, it's essential to follow the Azure Machine Learning Engineering Process. This process involves designing, building, and deploying machine learning models using Azure services.

The process begins with problem formulation, where you define the problem you're trying to solve and identify the data required to train your model. This involves data exploration, feature engineering, and data preprocessing.

The Azure Machine Learning Engineering Process emphasizes the importance of data quality and governance. Data is the backbone of machine learning, and ensuring its quality is critical to model accuracy and reliability.

Collaboration is key to successful machine learning projects, and Azure Machine Learning has tools to make it happen.

You can share notebooks, compute resources, serverless compute, data, and environments with your team, making it easier to work together.

Developing models that are fair and explainable is crucial, and Azure Machine Learning helps you track and audit compliance requirements.

Azure Machine Learning enables you to deploy ML models quickly and easily at scale, and manage and govern them efficiently with MLOps.

Built-in governance, security, and compliance features allow you to run machine learning workloads anywhere.

Here are some of the ways Azure Machine Learning supports team productivity:

You can build Generative AI applications powered by Large Language Models (LLMs) using Azure Machine Learning, which includes a model catalog and prompt flow to streamline the development cycle.

Azure Machine Learning provides multiple services to ingest big data, such as Azure SQL Database, Azure Cosmos DB, and Azure Data lake, making it easier to create predictive models.

Working with big data can be challenging, but Azure Machine Learning offers services like Apache Spark engines in Azure HDInsight and Databricks to transfer and transform the data.

You can also use Automated Machine Learning in Azure Machine Learning to train and deploy predictive models, making the process easier and more efficient.

You can build Generative AI applications powered by Large Language Models (LLMs) with Azure Machine Learning. This solution includes a model catalog, prompt flow, and tools to streamline development.

Azure Machine Learning Studio and Azure AI Studio allow you to work with LLMs. Use the guide to determine which studio is best for you.

A workspace is the top-level entity in Azure Machine Learning, containing everything you need to work with machine learning models. This includes cloud resources, assets, and artifacts created during the machine learning process.

Azure Container Registry (ACR) stores machine learning models and their associated code as Docker containers. Azure Storage account stores your machine learning datasets and Jupyter notebooks.

To deploy machine learning models, you can use Azure ML Inference Compute, which provides pre-built environments with varying degrees of model portability. This can reduce model development and deployment time.

Here are some options for managed environments in Azure Machine Learning:

Automated machine learning can help make the machine learning process easier by reducing the time and compute resources required. In Azure Machine Learning, you can use the automated machine learning capability to train and deploy a predictive model.

Managing big data is a crucial step in building predictive models with machine learning. Azure ML offers multiple services like Azure SQL Database, Azure Cosmos DB, and Azure Data lake to help ingest big data.

These services provide a robust solution for managing large datasets. Azure ML also benefits from services like Apache Spark engines in Azure HDInsight and Databricks for transferring and transforming big data.

Ingesting big data can be a complex task, but with the right tools, it becomes more manageable. Azure ML's services help you handle the challenges of working with large datasets and make it easier to build accurate predictive models.

To train a machine learning model, you'll want to create a Python environment to run the experiment. This environment is specified in a file called environment.yml.

You'll also need to create a folder to save all your Python scripts, as explained in the example where a folder named diabetes-training is created.

To get started, you'll need to create a Machine Learning Resource from the Azure Portal, which involves selecting a new compute instance from the left navigation.

Here are the steps to create a Python environment:

By following these steps, you'll be able to train your machine learning model using Python within the Azure ML framework.

Machine learning projects involve multiple people working together towards an objective and goals, and development is an iterative process that involves experimenting with data, algorithms, and models.

Models are developed as part of a project, and the development process is typically part of a larger workflow that involves more than one person.

The machine learning model lifecycle is a key concept in Azure Machine Learning, and understanding MLOps can help streamline the development process.

A workspace is a centralized place to manage resources for model training. It's where you can group resources based on projects, deployment environments, or organization units.

You can define a workspace within a resource group in Azure. This is where you'll store data, computing targets, Notebooks, Experiments, Models, Pipelines, and more.

A workspace typically includes a storage account to store data for model training, Application Insights to monitor predictive services, and Azure Key Vault to manage credentials.

To access these resources, users need to authenticate using the Azure Active Directory.

Here are the typical assets you'll find in a workspace:

You can connect to a workspace using Azure Notebooks, which allows you to write code that uses resources in the workspace.

You can build a model in Azure Machine Learning Designer by creating a training pipeline. This pipeline is designed using drag and drop features, making it a no-code environment. The pipeline consists of steps like data ingestion and feature engineering, which can be executed independently.

The Azure Machine Learning Designer includes a wide range of pre-defined modules for data ingestion, feature selection and engineering, model training, and validation. These modules can be used to process the data, normalize the features, and split the data into training and test sets.

To train the model, you can use the pre-defined modules in the designer. One powerful feature of the ML designer is the explanation generator, which can be turned on by going into the Parameters setting. This feature helps to save compute resources and provides an explanation to understand the context and better interpret the results.

Here are the steps involved in creating a training pipeline in Azure Machine Learning Designer:

By using the Azure Machine Learning Designer, you can create a model without writing any lines of code. However, if you need to add custom scripts, you can do so by adding Python, R, or SQL logic to a data flow.

Training a machine learning model is a crucial step in the machine learning engineering process. You can run your training script in the cloud or build a model from scratch in Azure Machine Learning.

To train a model, you'll need to create a training script that includes the code to train the model and save it to a .py file. This script should include steps such as creating datasets X and Y, splitting the data into train and test sets, and using a logistic regression model for classification problems.

Here are the key steps to train a model:

Automated machine learning (AutoML) can also speed up the process of training a model by automatically selecting the right data featurization and algorithm. This can be done through the Machine Learning studio UI or the Python SDK.

MLOps and DevOps is a process for developing machine learning models for production. It's a way to ensure that a model's lifecycle from training to deployment is auditable if not reproducible.

MLOps is built with the model lifecycle in mind, allowing you to audit the model lifecycle down to a specific commit and environment. This includes features like git integration.MLflow integration.Machine learning pipeline scheduling.Azure Event Grid integration for custom triggers.Ease of use with CI/CD tools like GitHub Actions or Azure DevOps.

Machine Learning also includes features for monitoring and auditing, such as job artifacts and lineage between jobs and assets. This helps ensure that your machine learning models are reliable, reproducible, and easy to maintain.

Enterprise-readiness and security are crucial for any machine learning project. Machine Learning integrates with the Azure cloud platform to add security to ML projects.

Azure Virtual Networks with network security groups provide an additional layer of security. This helps protect your ML projects from unauthorized access.

Azure Key Vault is a secure way to save security secrets, such as access information for storage accounts. This keeps your sensitive information safe and secure.

Azure Container Registry can be set up behind a virtual network for even more security. This adds an extra layer of protection for your ML projects.

Here are some key security integrations to consider:

For more information on setting up a secure workspace, check out the tutorial.

Complete solutions are a key part of MLOps and DevOps. Azure integrations provide a comprehensive platform for building and deploying machine learning projects from start to finish.

Azure Synapse Analytics is used to process and stream data with Spark, making it a powerful tool for data analysis. It's a game-changer for data-intensive projects.

Azure Arc allows you to run Azure services in a Kubernetes environment, giving you the flexibility to deploy and manage your projects in a containerized way. This is a huge advantage for teams that need to scale quickly.

Storage and database options like Azure SQL Database and Azure Blob Storage provide a robust foundation for data storage and retrieval. You can rely on these services to keep your data safe and accessible.

Azure App Service is used to deploy and manage ML-powered apps, making it a crucial part of the deployment process. With it, you can ensure that your apps are running smoothly and efficiently.

Here are some key Azure integrations for complete solutions:

These integrations work together seamlessly to provide a complete solution for machine learning projects. By using Azure's comprehensive platform, you can streamline your workflow and focus on what matters most – building and deploying high-quality models.

MLOps: DevOps for machine learning is a process for developing models for production. A model's lifecycle from training to deployment must be auditable if not reproducible. DevOps for ML models is a paradigm shift in the way ML would integrate within the larger software development framework.

Azure Machine Learning has capabilities to integrate with overall DevOps systems like Azure DevOps and GitHub integration. This enables collaboration and automation of the ML development cycle. The experiment is represented by Experiment Class and each trial in an experiment is represented by Run Class.

MLOps activities help achieve this by merging with the DevOps activities of the overall software engineering. These activities include model training, deployment, and maintenance. The set of practices for driving efficiency in developing software at an enterprise scale is known as DevOps.

In Azure Machine Learning, you can use automated machine learning to train and deploy a predictive model. Automated machine learning can help make the training process easier. This is especially useful for iterative processes that require time and compute resources.

To achieve MLOps, you need to consider scalability considerations. Distributed training efficiency is always lower than 100% due to network overhead and device-to-device synchronization. However, distributed learning is ideal for large models that cannot be trained on a single VM.

Here are some key features of Azure Machine Learning that support MLOps:

These features enable you to audit the model lifecycle down to a specific commit and environment.

You can run an automated algorithm using Azure Machine Learning, which speeds up the process of selecting the right data featurization and algorithm for training.

Automated ML (AutoML) can be used through the Machine Learning studio UI or the Python SDK.

To run an automated algorithm, you need to specify the dataset with labels to train the data, configure the automated machine learning run, select the algorithm and settings to apply, and review the best model generated.

Here are the key steps to run an automated machine learning algorithm:

The Azure Machine learning studio supports only supervised machine learning models, including classification models, regression models, and time series forecasting.

Automated machine learning can help identify the best model for a particular use case by parallel processing of multiple models and optimizing algorithms for the best outcomes.

Storage Considerations are crucial when it comes to training deep learning models, especially when working with large datasets.

Storing datasets on premium blob storage can significantly improve data access performance, as it provides higher performance compared to standard storage options.

Using a local cache mechanism is also essential, as it ensures that data is readily available in internal storage on the VM, reducing the time it takes to access data.

This approach can be particularly beneficial for large-scale, distributed deep learning jobs on Azure, where high performance is critical.

By combining premium blob storage with a local cache mechanism, you can achieve high performance data access, even with slow storage.

In an unmanaged environment, you have the freedom to manage your production environment yourself, giving you the best of both worlds. This is perfect for ML Engineers who have a model and artifacts ready to deploy.

You can leverage the Azure CLI to deploy your model and environment to a LINUX VM in Azure. This VM will serve as the scoring engine for production applications or as an MVP application to monitor model performance.

With an unmanaged environment, you have the flexibility to choose how you deploy your machine learning model. Here are some options:

This table shows you the different resources, levels, and training assets URLs available for deployment in an unmanaged environment.