Machine Learning

Improve your machine learning platform with the right data orchestration tool! Apache Airflow is a flexible, community-supported, and Python-based solution that allows you to access more data, iterate better, and speed up your work.

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Machine Learning and the challenge of complexity

As modern organizations rely more and more on data to make the right business decisions and guarantee their growth, creating successful machine learning processes has never been more crucial. Even though a lot of organizations are machine learning-driven, 90 percent of all machine learning models never make it into production.

Apache Airflow—an end-to-end solution for your machine learning needs

Machine learning engineers point to non-reproducible pipelines and inefficient integration with different databases and tools as the main obstacle to efficient MLOps. Create a robust production environment, using a flexible, reliable, and extensive data orchestrator.

Working with machine learning models in production requires automation and orchestration for repeated model training, testing, evaluation, and likely integration with other services to acquire and prepare data. With Apache Airflow you can easily orchestrate each step of your pipeline, integrate with services that clean your data, and store and publish your results using simple Python scripts for ‘configuration as code’.

Apache Airflow
  1. Productionize
    your ML pipeline

    Airflow allows you to build pieces of a machine learning pipeline easily and systematically. Reuse the same code for different machine learning models and datasets, solving the problem of complexity.

  2. Conduct an end-to-end
    ML process from one place

    Have full observability over your data and models: from getting the data, cleaning it, and putting your models in production.

  3. Simplify and speed up
    ML engineering

    Apply principles of generalizability, scalability, and reproducibility to machine learning. Airflow is an extensive, Python-based, open-source tool with a wide range of operators, hooks, and modules that can be used and adjusted to your specific needs.

Airflow has a central place in our machine learning platform because it is responsible for retraining the models in SageMaker. We use Airflow with SageMaker to retrain workflows, spin up the SageMaker training instances, and then train the models regularly.

Alexandra Abbas

Alexandra Abbas

Machine Learning Engineer at Wise

After 6-7 months with Apache Airflow, we’ve built more than ninety DAGs. The tool made the experience so much easier.

Gautam Doulani

Gautam Doulani

Data Engineering Lead at CRED

An open source project, such as Apache Airflow, works great in the production environment, even for the sensitive use cases of the banking industry.

Alaeddine Maaoui

Alaeddine Maaoui

Product Owner at Societe Generale

Modern,Cloud-Native Data Orchestration powered by Apache Airflow

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