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Software · head to head

Dask vs Azure Machine Learning

Dask logo

Dask

Software

Scalable analytics in Python

From
Free
Rated
-
Full reviewWebsite
Azure Machine Learning logo

Azure Machine Learning

Software

Enterprise-grade machine learning service

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Dask each Dask task carries between 200 microseconds and 1 millisecond of scheduler overhead, so graphs of millions of tasks add 10 minutes to hours of pure overhead; Azure Machine Learning requires knowledge of Azure ecosystem and integration with other Azure services
  • They diverge on capability: Dask covers Parallel computing, Azure Machine Learning covers Automated ML.

Where they differ

Only the attributes on which Dask and Azure Machine Learning actually diverge.

Attributes where Dask and Azure Machine Learning differ
AttributeDaskAzure Machine Learning
Pricing modelopen-sourceusage-based
PlatformsLinux, Mac, WindowsAzure Cloud
Founded20151975

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Unknown).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Dask

  • Parallel computing
  • Distributed DataFrames
  • Lazy evaluation
  • Dynamic task scheduling
  • Dashboard
  • NumPy
  • Pandas
  • scikit-learn

Only in Azure Machine Learning

  • Automated ML
  • Designer (drag-and-drop)
  • Notebooks
  • MLOps
  • Model registry
  • Azure Blob Storage
  • Azure DevOps
  • Power BI

What people use each for

The jobs each tool is most often brought in to do.

Dask

  • Scaling pandas and NumPy workloads beyond a single machine's memorynot Azure Machine Learning
  • Parallelising custom Python task graphsnot Azure Machine Learning
  • Processing larger than memory arrays and dataframes on a clusternot Azure Machine Learning

Azure Machine Learning

  • Machine learningnot Dask
  • Data analysisnot Dask
  • Model trainingnot Dask
  • Predictive analyticsnot Dask

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Dask

  • Each Dask task carries between 200 microseconds and 1 millisecond of scheduler overhead, so graphs of millions of tasks add 10 minutes to hours of pure overhead
  • Partition sizing is left to the user: chunks must fit several times over in worker memory, and both oversized and undersized chunks are documented failure modes
  • Embedding large locally created DataFrames or Arrays into a Dask computation is documented as a practice to avoid because of network overhead
  • Calling compute repeatedly in a loop rather than batching prevents parallelisation of queries
  • The documentation itself advises trying better algorithms, file formats or sampling before adopting Dask

Azure Machine Learning

  • Requires knowledge of Azure ecosystem and integration with other Azure services
  • Compute resources for training and inference generate separate charges

Pricing, plan by plan

Dask

Free
  • Open SourceFree
    • Parallel computing
    • Distributed DataFrames
    • ML integration

Azure Machine Learning

Free
  • Free TierFree
    • Limited compute
    • Basic features
  • Pay-as-you-go$0.05/hour
    • Full platform
    • All compute options
    • Enterprise features

Which should you pick?

Choose Dask if

  • You need parallel computing.
  • You want to start without paying.
  • You work on Linux, Mac, Windows.
  • You also want distributed dataframes.
Dask review

Choose Azure Machine Learning if

  • You need automated ml.
  • You want to start without paying.
  • You work on Azure Cloud.
  • You also want designer (drag-and-drop).
Azure Machine Learning review

Questions people ask

Is Dask or Azure Machine Learning better?
Neither clearly leads. Dask starts at Free and Azure Machine Learning at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Dask or Azure Machine Learning?
Dask starts at Free and Azure Machine Learning at Free.
Does Dask or Azure Machine Learning run on more platforms?
Dask runs on Linux, Mac, Windows. Azure Machine Learning runs on Azure Cloud.
Can I use Dask for free?
Both have a free tier, so you can try either at no cost before committing.
What is Dask best used for?
Dask is most often used for scaling pandas and numpy workloads beyond a single machine's memory, parallelising custom python task graphs, processing larger than memory arrays and dataframes on a cluster. Of those, scaling pandas and numpy workloads beyond a single machine's memory and parallelising custom python task graphs are not what Azure Machine Learning is typically brought in for.
What can Dask do that Azure Machine Learning cannot?
Dask covers Parallel computing, Distributed DataFrames, Lazy evaluation, Dynamic task scheduling. Azure Machine Learning covers Automated ML, Designer (drag-and-drop), Notebooks, MLOps.

Answered from the vendors’ own pages

Azure Machine Learning: Does Azure Machine Learning have any platform licensing fees?

No, Azure Machine Learning carries no extra cost. You only pay for the underlying compute resources utilized during model training or inference.

Source
Azure Machine Learning: What AutoML capabilities does Azure Machine Learning provide?

Azure Machine Learning supports automated model creation for classification, regression, vision, and natural language processing tasks.

Source
Azure Machine Learning: Does Azure ML support language model fine-tuning?

Yes, Azure Machine Learning supports fine-tuning of foundation models from providers including OpenAI, Meta, Hugging Face, and Cohere.

Source
Azure Machine Learning: What MLOps features are included?

Azure ML includes end-to-end pipeline automation with CI/CD capabilities, managed endpoints for model deployment, and monitoring tools.

Source
Azure Machine Learning: Can I access foundation models from multiple vendors?

Yes, Azure Machine Learning provides access to a model catalog with foundation models from Microsoft, OpenAI, Hugging Face, Meta, and Cohere.

Source
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Related pages

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