Data Science

Discover a more productive side of data science with the right data orchestration tool. Apache Airflow is a flexible, community-supported, and Python-based solution that fits right into your exploration, experimentation, and machine learning tasks.

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Data science—turning insights into actions

Data science today is centered around exploration and experimentation—asking the right questions, iterating, and going through many exploration cycles. As companies are becoming more data-driven, finding valuable insights and turning them into actions fast has become essential. For this reason, data scientists need to find better, more productive ways of creating machine learning models and cooperating with data engineers.

Apache Airflow — the orchestration solution for all your data science needs

The lack of end-to-end ownership of the process and experimentation frameworks can make life challenging for data scientists. Additionally, navigating many different tools and databases spread across the organization may turn insights into confusion.

Implementing a data orchestrator like Apache Airflow allows data scientists to perform reproducible, long-running experiments and cooperate smoothly with other teams; from prepping the data, building and testing machine learning models, to action.

Apache Airflow eliminates friction throughout pipelines and workflows by helping data scientists find their way to data—allowing you to easily “plug and play” with any tool, database, or system.

Apache Airflow
  1. End-to-end

    Instead of handing off notebooks to data or ML engineers early on, benefit from the modular structure of Airflow and carry out the process yourself. Since Airflow code is close to production code, other team members don’t have to do much work on top of it when bringing it to production.

  2. Improved cooperation between data teams

    Apache Airflow serves as a common language, helping combine the use cases of data scientists, data engineers, and machine learning engineers into one operationalized framework.

  3. Automated data science experiments

    Schedule long-running jobs. Run repeated experiments to monitor how models behave over time. Use Airflow to chain together disparate technologies to reproduce experimentation modules easily.

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

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

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

Product Owner at Societe Generale

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