Alternatives to Azure Machine Learning logo

Alternatives to Azure Machine Learning

Python, Azure Databricks, Amazon SageMaker, Amazon Machine Learning, and Databricks are the most popular alternatives and competitors to Azure Machine Learning.
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What is Azure Machine Learning and what are its top alternatives?

Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning.
Azure Machine Learning is a tool in the Machine Learning as a Service category of a tech stack.

Top Alternatives to Azure Machine Learning

  • Python
    Python

    Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best. ...

  • Azure Databricks
    Azure Databricks

    Accelerate big data analytics and artificial intelligence (AI) solutions with Azure Databricks, a fast, easy and collaborative Apache Spark–based analytics service. ...

  • Amazon SageMaker
    Amazon SageMaker

    A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. ...

  • Amazon Machine Learning
    Amazon Machine Learning

    This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure. ...

  • Databricks
    Databricks

    Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications. ...

  • MLflow
    MLflow

    MLflow is an open source platform for managing the end-to-end machine learning lifecycle. ...

  • TensorFlow
    TensorFlow

    TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. ...

  • IBM Watson
    IBM Watson

    It combines artificial intelligence (AI) and sophisticated analytical software for optimal performance as a "question answering" machine. ...

Azure Machine Learning alternatives & related posts

Python logo

Python

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A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
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PROS OF PYTHON
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    Great libraries
  • 947
    Readable code
  • 834
    Beautiful code
  • 780
    Rapid development
  • 682
    Large community
  • 426
    Open source
  • 385
    Elegant
  • 278
    Great community
  • 268
    Object oriented
  • 214
    Dynamic typing
  • 75
    Great standard library
  • 56
    Very fast
  • 51
    Functional programming
  • 43
    Scientific computing
  • 43
    Easy to learn
  • 33
    Great documentation
  • 26
    Matlab alternative
  • 25
    Productivity
  • 25
    Easy to read
  • 21
    Simple is better than complex
  • 18
    It's the way I think
  • 17
    Imperative
  • 15
    Very programmer and non-programmer friendly
  • 15
    Free
  • 14
    Powerfull language
  • 14
    Machine learning support
  • 14
    Powerful
  • 13
    Fast and simple
  • 12
    Scripting
  • 9
    Explicit is better than implicit
  • 8
    Unlimited power
  • 8
    Ease of development
  • 8
    Clear and easy and powerfull
  • 7
    Import antigravity
  • 6
    It's lean and fun to code
  • 6
    Print "life is short, use python"
  • 5
    Great for tooling
  • 5
    Fast coding and good for competitions
  • 5
    I love snakes
  • 5
    Python has great libraries for data processing
  • 5
    There should be one-- and preferably only one --obvious
  • 5
    High Documented language
  • 5
    Flat is better than nested
  • 5
    Although practicality beats purity
  • 4
    Rapid Prototyping
  • 4
    Readability counts
  • 3
    Great for analytics
  • 3
    Web scraping
  • 3
    Now is better than never
  • 3
    Plotting
  • 3
    Lists, tuples, dictionaries
  • 3
    Socially engaged community
  • 3
    Complex is better than complicated
  • 3
    Multiple Inheritence
  • 3
    Beautiful is better than ugly
  • 3
    CG industry needs
  • 2
    No cruft
  • 2
    Many types of collections
  • 2
    Easy to learn and use
  • 2
    Special cases aren't special enough to break the rules
  • 2
    If the implementation is hard to explain, it's a bad id
  • 2
    If the implementation is easy to explain, it may be a g
  • 2
    List comprehensions
  • 2
    Generators
  • 2
    Simple and easy to learn
  • 2
    Easy to setup and run smooth
  • 2
    Import this
  • 1
    Powerful language for AI
  • 1
    Because of Netflix
  • 1
    A-to-Z
  • 1
    Only one way to do it
  • 1
    Can understand easily who are new to programming
  • 1
    Flexible and easy
  • 1
    Better outcome
  • 1
    Batteries included
  • 1
    Good for hacking
  • 1
    Should START with this but not STICK with This
  • 1
    Pip install everything
  • 1
    It is Very easy , simple and will you be love programmi
  • 0
    Powerful
CONS OF PYTHON
  • 51
    Still divided between python 2 and python 3
  • 28
    Performance impact
  • 26
    Poor syntax for anonymous functions
  • 21
    GIL
  • 19
    Package management is a mess
  • 14
    Too imperative-oriented
  • 12
    Hard to understand
  • 12
    Dynamic typing
  • 10
    Very slow
  • 8
    Not everything is expression
  • 7
    Explicit self parameter in methods
  • 7
    Indentations matter a lot
  • 6
    Poor DSL capabilities
  • 6
    Incredibly slow
  • 6
    No anonymous functions
  • 6
    Requires C functions for dynamic modules
  • 5
    Hard to obfuscate
  • 5
    Threading
  • 5
    Fake object-oriented programming
  • 5
    The "lisp style" whitespaces
  • 4
    Official documentation is unclear.
  • 4
    Circular import
  • 4
    Lack of Syntax Sugar leads to "the pyramid of doom"
  • 4
    Not suitable for autocomplete
  • 4
    The benevolent-dictator-for-life quit
  • 2
    Meta classes
  • 1
    Training wheels (forced indentation)

related Python posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 41 upvotes · 5.6M views

How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

https://eng.uber.com/distributed-tracing/

(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

See more
Nick Parsons
Building cool things on the internet 🛠️ at Stream · | 35 upvotes · 1.8M views

Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.

We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)

We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.

Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.

#FrameworksFullStack #Languages

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Azure Databricks logo

Azure Databricks

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Fast, easy, and collaborative Apache Spark–based analytics service
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PROS OF AZURE DATABRICKS
    Be the first to leave a pro
    CONS OF AZURE DATABRICKS
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      related Azure Databricks posts

      Amazon SageMaker logo

      Amazon SageMaker

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      Accelerated Machine Learning
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      PROS OF AMAZON SAGEMAKER
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        CONS OF AMAZON SAGEMAKER
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          related Amazon SageMaker posts

          Amazon SageMaker constricts the use of their own mxnet package and does not offer a strong Kubernetes backbone. At the same time, Kubeflow is still quite buggy and cumbersome to use. Which tool is a better pick for MLOps pipelines (both from the perspective of scalability and depth)?

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          Julien DeFrance
          Principal Software Engineer at Tophatter · | 2 upvotes · 61.3K views

          Which #IaaS / #PaaS to chose? Not all #Cloud providers are created equal. As you start to use one or the other, you'll build around very specific services that don't have their equivalent elsewhere.

          Back in 2014/2015, this decision I made for SmartZip was a no-brainer and #AWS won. AWS has been a leader, and over the years demonstrated their capacity to innovate, and reducing toil. Like no other.

          Year after year, this kept on being confirmed, as they rolled out new (managed) services, got into Serverless with AWS Lambda / FaaS And allowed domains such as #AI / #MachineLearning to be put into the hands of every developers thanks to Amazon Machine Learning or Amazon SageMaker for instance.

          Should you compare with #GCP for instance, it's not quite there yet. Building around these managed services, #AWS allowed me to get my developers on a whole new level. Where they know what's under the hood. Where they know they have these services available and can build around them. Where they care and are responsible for operations and security and deployment of what they've worked on.

          See more
          Amazon Machine Learning logo

          Amazon Machine Learning

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          Visualization tools and wizards that guide you through the process of creating ML models w/o having to learn...
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          PROS OF AMAZON MACHINE LEARNING
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            CONS OF AMAZON MACHINE LEARNING
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              related Amazon Machine Learning posts

              Julien DeFrance
              Principal Software Engineer at Tophatter · | 2 upvotes · 61.3K views

              Which #IaaS / #PaaS to chose? Not all #Cloud providers are created equal. As you start to use one or the other, you'll build around very specific services that don't have their equivalent elsewhere.

              Back in 2014/2015, this decision I made for SmartZip was a no-brainer and #AWS won. AWS has been a leader, and over the years demonstrated their capacity to innovate, and reducing toil. Like no other.

              Year after year, this kept on being confirmed, as they rolled out new (managed) services, got into Serverless with AWS Lambda / FaaS And allowed domains such as #AI / #MachineLearning to be put into the hands of every developers thanks to Amazon Machine Learning or Amazon SageMaker for instance.

              Should you compare with #GCP for instance, it's not quite there yet. Building around these managed services, #AWS allowed me to get my developers on a whole new level. Where they know what's under the hood. Where they know they have these services available and can build around them. Where they care and are responsible for operations and security and deployment of what they've worked on.

              See more
              Databricks logo

              Databricks

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              A unified analytics platform, powered by Apache Spark
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              PROS OF DATABRICKS
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                Best Performances on large datasets
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                True lakehouse architecture
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                Scalability
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                Databricks doesn't get access to your data
              • 1
                Usage Based Billing
              • 1
                Security
              • 1
                Data stays in your cloud account
              • 1
                Multicloud
              CONS OF DATABRICKS
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                related Databricks posts

                Jan Vlnas
                Developer Advocate at Superface · | 5 upvotes · 17.7K views

                From my point of view, both OpenRefine and Apache Hive serve completely different purposes. OpenRefine is intended for interactive cleaning of messy data locally. You could work with their libraries to use some of OpenRefine features as part of your data pipeline (there are pointers in FAQ), but OpenRefine in general is intended for a single-user local operation.

                I can't recommend a particular alternative without better understanding of your use case. But if you are looking for an interactive tool to work with big data at scale, take a look at notebook environments like Jupyter, Databricks, or Deepnote. If you are building a data processing pipeline, consider also Apache Spark.

                Edit: Fixed references from Hadoop to Hive, which is actually closer to Spark.

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                MLflow logo

                MLflow

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                An open source machine learning platform
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                PROS OF MLFLOW
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                  Code First
                • 4
                  Simplified Logging
                CONS OF MLFLOW
                  Be the first to leave a con

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                  Shared insights
                  on
                  MLflowMLflowDVCDVC

                  I already use DVC to keep track and store my datasets in my machine learning pipeline. I have also started to use MLflow to keep track of my experiments. However, I still don't know whether to use DVC for my model files or I use the MLflow artifact store for this purpose. Or maybe these two serve different purposes, and it may be good to do both! Can anyone help, please?

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                  Biswajit Pathak
                  Project Manager at Sony · | 6 upvotes · 151.3K views

                  Can you please advise which one to choose FastText Or Gensim, in terms of:

                  1. Operability with ML Ops tools such as MLflow, Kubeflow, etc.
                  2. Performance
                  3. Customization of Intermediate steps
                  4. FastText and Gensim both have the same underlying libraries
                  5. Use cases each one tries to solve
                  6. Unsupervised Vs Supervised dimensions
                  7. Ease of Use.

                  Please mention any other points that I may have missed here.

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                  TensorFlow logo

                  TensorFlow

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                  Open Source Software Library for Machine Intelligence
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                  PROS OF TENSORFLOW
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                    High Performance
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                    Connect Research and Production
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                    Deep Flexibility
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                    Auto-Differentiation
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                    True Portability
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                    Powerful
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                    High level abstraction
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                    Easy to use
                  CONS OF TENSORFLOW
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                    Hard
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                    Hard to debug
                  • 1
                    Documentation not very helpful

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                  Conor Myhrvold
                  Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.5M views

                  Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

                  At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

                  TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

                  Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

                  https://eng.uber.com/horovod/

                  (Direct GitHub repo: https://github.com/uber/horovod)

                  See more

                  In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.

                  Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

                  !

                  See more
                  IBM Watson logo

                  IBM Watson

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                  A question-answering computer system capable of answering questions posed in natural language
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                  PROS OF IBM WATSON
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                    Api
                  • 1
                    Prebuilt front-end GUI
                  • 1
                    Intent auto-generation
                  • 1
                    Custom webhooks
                  • 1
                    Disambiguation
                  CONS OF IBM WATSON
                  • 1
                    Multi-lingual

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