Need advice about which tool to choose?Ask the StackShare community!
Airflow vs MLflow: What are the differences?
Key Differences between Airflow and MLflow
Introduction
Airflow and MLflow are both popular open-source platforms used in data engineering and machine learning workflows. While they serve similar purposes, there are key differences between the two platforms. This article highlights the main differences between Airflow and MLflow.
Workflow Management vs. Model Management: Airflow is primarily a workflow management platform that focuses on managing and scheduling complex data pipelines. It provides task dependencies, parallel execution, and retry capabilities. MLflow, on the other hand, is designed for managing the machine learning lifecycle, including experiment tracking, reproducibility, and model deployment.
Orchestration vs. Experiment Tracking: Airflow excels in orchestrating and scheduling tasks across different systems. It provides a graphical interface to define, monitor, and manage workflows. MLflow, however, shines in experiment tracking and management. It allows data scientists to track experiments, log parameters, metrics, and artifacts, and reproduce past results.
Task Execution vs. Model Registry: In Airflow, each task represents a unit of work, which can be executed on different platforms or systems. It focuses on task execution and provides operators for various tasks, such as data ingestion, transformation, and processing. MLflow emphasizes the model registry, where you can register, version, and deploy machine learning models.
Pythonic vs. Language-Agnostic: Airflow is written in Python and supports Python-based tasks out of the box. While you can integrate other languages into Airflow, it is primarily a Python-based framework. MLflow, on the other hand, is designed to be language-agnostic. It supports multiple programming languages and frameworks, allowing data scientists to use their preferred tools for model development and training.
DAGs vs. Experiments: Airflow uses Directed Acyclic Graphs (DAGs) to define and represent workflows. DAGs provide a visual representation of tasks and their dependencies. MLflow, on the other hand, uses experiments as the central unit of work. Each experiment can have multiple runs, representing different iterations or versions of models.
Community and Ecosystem: Both Airflow and MLflow have vibrant communities and ecosystems. Airflow has been around since 2014 and has a large community of contributors, offering a wide range of plugins and integrations. MLflow, although relatively newer, has gained significant popularity, especially in the machine learning community, and also has a growing ecosystem of extensions and integrations.
In summary, Airflow is primarily a workflow management platform focused on task execution and orchestration, while MLflow is a tool designed specifically for machine learning lifecycle management, including experiment tracking and model registry. Airflow is Python-centric, while MLflow is language-agnostic. However, both platforms have their unique strengths and can be used together in data engineering and machine learning workflows.
I am so confused. I need a tool that will allow me to go to about 10 different URLs to get a list of objects. Those object lists will be hundreds or thousands in length. I then need to get detailed data lists about each object. Those detailed data lists can have hundreds of elements that could be map/reduced somehow. My batch process dies sometimes halfway through which means hours of processing gone, i.e. time wasted. I need something like a directed graph that will keep results of successful data collection and allow me either pragmatically or manually to retry the failed ones some way (0 - forever) times. I want it to then process all the ones that have succeeded or been effectively ignored and load the data store with the aggregation of some couple thousand data-points. I know hitting this many endpoints is not a good practice but I can't put collectors on all the endpoints or anything like that. It is pretty much the only way to get the data.
For a non-streaming approach:
You could consider using more checkpoints throughout your spark jobs. Furthermore, you could consider separating your workload into multiple jobs with an intermittent data store (suggesting cassandra or you may choose based on your choice and availability) to store results , perform aggregations and store results of those.
Spark Job 1 - Fetch Data From 10 URLs and store data and metadata in a data store (cassandra) Spark Job 2..n - Check data store for unprocessed items and continue the aggregation
Alternatively for a streaming approach: Treating your data as stream might be useful also. Spark Streaming allows you to utilize a checkpoint interval - https://spark.apache.org/docs/latest/streaming-programming-guide.html#checkpointing
Pros of Airflow
- Features53
- Task Dependency Management14
- Beautiful UI12
- Cluster of workers12
- Extensibility10
- Open source6
- Complex workflows5
- Python5
- Good api3
- Apache project3
- Custom operators3
- Dashboard2
Pros of MLflow
- Code First5
- Simplified Logging4
Sign up to add or upvote prosMake informed product decisions
Cons of Airflow
- Observability is not great when the DAGs exceed 2502
- Running it on kubernetes cluster relatively complex2
- Open source - provides minimum or no support2
- Logical separation of DAGs is not straight forward1