Machine Learning Workflow Automation with Airflow

Machine Learning Workflow Automation with Airflow

Big data is playing an important role in many organizations. Innovative organizations depend more on it to improve their decision-making, for effective growth, and to create efficient machine learning processes. In other words, many organizations are based on machine learning workflows.

 

Machine learning workflow

machine-learning-workflow

Machine learning workflow is helpful to define the phases of a project like building datasets, data collection, processing of data, training of ML model, evaluations, and production. Some phases of this workflow like model training and feature selection can be automated, but not all.  

 

Machine learning workflow automation

Machine learning automation is applied specifically to present ML algorithms to develop new models. The purpose is not the automation of the complete model development process, but to reduce the number of human interventions for successful development.

 

Moreover, it helps the development team to start and end a project faster. It also improves the unsupervised machine learning process and deep learning that enables self-correction in the models being developed.

 

Workflow Automation with Airflow

machine-learning-workflow-automation

Airflow is an open-source Apache platform to authorize, schedule, compute, and monitor workflows. It was developed first by Airbnb and now Apache has taken over it. It uses Python to generate easily monitored and scheduled workflows. You can run anything with Airflow, it is skeptical of the type of running.

 

There are different task packages (building datasets, data collection, processing of data, training of ML model, evaluations, and production) in the ML workflows as discussed earlier. It is responsible to execute the workflow runs that are individual. You can also create a workflow according to your needs by integrating the workflow steps. While in the case of individual components, you need to define workflows separately for each component.

 

What are the Workflow Phases?

Data Preparation

In data preparation for training purposes, many ETL (extract, transform, load) processes are followed. Important information can be generated within the workflow about the scope of data and statistical properties. This is helpful to reveal any hidden error source.

 

Model evaluation and training

To make the model transform itself according to the new data, regular training of data is useful. You can start it with REST APIs or chronologically if a particular error obstacle is achieved by the model.

 

Model Deployment

The deployment is different as per the scope of the model. It is different from the service-based model and forecast table generator model.

 

Why is Machine learning workflow automation with airflow suitable?

As compared to ETL workflows, ML workflows are far more complex because they have dependencies between a large number of data sources and individual steps. Moreover, there are different requirements for hardware based on different models. Thus, the workflow that starts with simple cron jobs usually achieves its limits and is subject to error for the lack of dependencies among the individual tasks.

 

The Apache Airflow is easy to implement and use, which is why it is widely known for the workflow management of ML applications.

 

Key Features of Apache Airflow

Apache Airflow has the following benefits:

  • It is easy to use. You need to have only a little bit of knowledge of Python to start it.
  • It is open-source software, which is why it is free, and many users are actively using it.
  • It has ready-to-use operators that enable you to perform the integrations between Airflow and cloud platforms like Azure, Google, AWS, etc.
  • You don’t need to have any knowledge about the additional framework of Python or any other technology to create flexible workflows.
  • It is based on the graphical user interface that makes you manage and monitor workflows and check the status of completed and ongoing tasks.

 

Use cases of Apache Airflow

An enormous growth has been observed in Airflow due to the increase in the need for organized data pipelines. It has flourished from data to ML, and now it is being used in several scenarios:

  • Use case 1
    It is helpful for batch jobs.
  • Use case 2
    It is helpful to organize, monitor, and execute workflows.
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  • Use case 3
    It is used during the pre-scheduling of data pipelines for a certain time interval.
  • Use case 4
    It is also useful for ETL pipelines that are useful for batch data. Moreover, it is also helpful for pipelines that are responsible for data transformation and collecting data from various sources.
  • Use case 5
    Airflow is useful to train ML models and to trigger jobs such as Sage Maker.
  • Use case 6
    It is also useful to generate reports
  • Use case 7
    It is useful in certain scenarios that have the backup requirement from DevOps and sorting requirement after the Spark job execution in a Hadoop cluster.

 

Airflow Best Practices

There are many functionalities associated with Airflow that are useful in various scenarios. But to get the maximum potential, optimizations play an important role. Here are some best practices to get the best optimization in Airflow.

 

The Airflow depends on Python, and it needs to be updated for effective workflows. It can be achieved easily by syncing your code with GitHub. Airflow will load files to its directory from the DAG folder. This way you can create sub-folders and link them with your GitHub repository.

 

You can use pull requests to synchronize your directory. At the start of the workflow, make the pull request. All the files of the workflow including the ML models scripts can also be synchronized using Git.

 

Algoscale utilizing Airflow for ML workflow automation

It is clear from our discussion that Apache Airflow has proved itself as a leading tool in workflow management. It has several features: it is easy to use, has high-level functionality, and several others. They are useful in many scenarios.

 

In this article, we have discussed some important use cases of Airflow and provided some real-life examples.

 

There are several machine learning companies in the USA, but Algoscale is utilizing Airflow for machine learning workflow automation. You can come to us with your use case, and we will recommend you the best workflow process.

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