Alternatives to Amazon SageMaker logo

Alternatives to Amazon SageMaker

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

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 SageMaker is a tool in the Machine Learning as a Service category of a tech stack.

Top Alternatives to Amazon SageMaker

  • 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. ...

  • Azure Machine Learning

    Azure Machine Learning

    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. ...

  • Kubeflow

    Kubeflow

    The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions. ...

  • 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. ...

  • H2O

    H2O

    H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark. ...

  • Algorithms.io

    Algorithms.io

    Build And Run Predictive Applications For Streaming Data From Applications, Devices, Machines and Wearables ...

Amazon SageMaker alternatives & related posts

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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      Julien DeFrance
      Principal Software Engineer at Tophatter · | 2 upvotes · 51.5K 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
      • 1
        Scalability
      • 1
        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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        Azure Machine Learning logo

        Azure Machine Learning

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        A fully-managed cloud service for predictive analytics
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        PROS OF AZURE MACHINE LEARNING
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            Kubeflow logo

            Kubeflow

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            Machine Learning Toolkit for Kubernetes
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            PROS OF KUBEFLOW
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              System designer
            • 3
              Customisation
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              Kfp dsl
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              Google backed
            CONS OF KUBEFLOW
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              Biswajit Pathak
              Project Manager at Sony · | 5 upvotes · 39.8K 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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              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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              TensorFlow logo

              TensorFlow

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              PROS OF TENSORFLOW
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                High Performance
              • 16
                Connect Research and Production
              • 13
                Deep Flexibility
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                True Portability
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                Auto-Differentiation
              • 2
                Easy to use
              • 2
                High level abstraction
              • 1
                Powerful
              CONS OF TENSORFLOW
              • 9
                Hard
              • 6
                Hard to debug
              • 1
                Documentation not very helpful

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              Conor Myhrvold
              Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.3M 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.

              !

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              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
              • 3
                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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              H2O logo

              H2O

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              H2O.ai AI for Business Transformation
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              PROS OF H2O
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                Highly customizable
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                Very fast and powerful
              • 1
                Auto ML is amazing
              • 1
                Super easy to use
              CONS OF H2O
              • 1
                Not very popular

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              Algorithms.io logo

              Algorithms.io

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              Machine learning as a service for streaming data from connected devices.
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