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 cloud-based service provided by Microsoft that facilitates the building, training, and deployment of machine learning models. Its key features include automated machine learning, visual drag-and-drop interface, support for various programming languages, and seamless integration with Azure services. However, some limitations of Azure Machine Learning include a steeper learning curve for beginners and higher pricing compared to other alternatives.

  1. Google Cloud AI Platform: Google Cloud AI Platform offers a suite of tools for training and deploying machine learning models, with features like scalable infrastructure, versioning, and hyperparameter tuning. Pros include integration with other Google Cloud services, while cons include limited support for custom algorithms.

  2. Amazon SageMaker: Amazon SageMaker is a fully managed service that enables developers to build, train, and deploy machine learning models quickly. Key features include built-in algorithms, model optimization, and model monitoring. Pros include seamless integration with AWS services, while cons include higher pricing for large-scale usage.

  3. IBM Watson Studio: IBM Watson Studio provides tools for data scientists, application developers, and subject matter experts to collaborate on building and deploying models. Features include AutoAI, model building, and deployment options. Pros include a user-friendly interface, while cons include limited customization options.

  4. Databricks: Databricks offers a Unified Analytics Platform for data engineering, data science, and machine learning. Key features include collaborative workspaces, integrated data orchestration, and MLflow for managing the ML lifecycle. Pros include ease of use, while cons include higher costs for additional features.

  5. H2O.ai: H2O.ai provides open-source machine learning platforms for data science and machine learning practitioners. Features include automatic machine learning, model interpretability, and support for a wide range of algorithms. Pros include open-source nature, while cons include limited enterprise support.

  6. DataRobot: DataRobot is an automated machine learning platform that helps organizations build and deploy machine learning models. Key features include automated model building, model deployment, and model management. Pros include ease of use, while cons include higher pricing compared to some alternatives.

  7. RapidMiner: RapidMiner is a data science platform that offers features like data preparation, machine learning, and model deployment. Pros include a user-friendly visual interface, while cons include limited scalability for large datasets.

  8. BigML: BigML provides a machine learning platform that offers features like automatic feature engineering, model evaluation, and model interpretation. Pros include ease of use, while cons include limited support for deep learning algorithms.

  9. KNIME: KNIME is an open-source data analytics, reporting, and integration platform with a strong focus on machine learning and data mining. Key features include a visual workflow editor, support for various data formats, and community extensions. Pros include open-source nature, while cons include a steeper learning curve for beginners.

  10. Seldon: Seldon is an open-source platform for deploying machine learning models at scale. Features include model serving, monitoring, and explainability. Pros include scalability, while cons include limited support compared to some commercial solutions.

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
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    Readable code
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    Beautiful code
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    Rapid development
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    Large community
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    Open source
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    Elegant
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    Great community
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    Object oriented
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    Dynamic typing
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    Great standard library
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    Very fast
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    Functional programming
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    Easy to learn
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    Scientific computing
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    Great documentation
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    Productivity
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    Easy to read
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    Matlab alternative
  • 23
    Simple is better than complex
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    It's the way I think
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    Imperative
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    Free
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    Very programmer and non-programmer friendly
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    Powerfull language
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    Machine learning support
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    Fast and simple
  • 14
    Scripting
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    Explicit is better than implicit
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    Ease of development
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    Clear and easy and powerfull
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    Unlimited power
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    It's lean and fun to code
  • 8
    Import antigravity
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    Print "life is short, use python"
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    Python has great libraries for data processing
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    Although practicality beats purity
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    Flat is better than nested
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    Great for tooling
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    Rapid Prototyping
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    Readability counts
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    High Documented language
  • 6
    I love snakes
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    Fast coding and good for competitions
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    There should be one-- and preferably only one --obvious
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    Now is better than never
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    Great for analytics
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    Lists, tuples, dictionaries
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    Easy to learn and use
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    Simple and easy to learn
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    Easy to setup and run smooth
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    Web scraping
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    CG industry needs
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    Socially engaged community
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    Complex is better than complicated
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    Multiple Inheritence
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    Beautiful is better than ugly
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    Plotting
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    If the implementation is hard to explain, it's a bad id
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    Special cases aren't special enough to break the rules
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    Pip install everything
  • 3
    List comprehensions
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    No cruft
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    Generators
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    Import this
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    It is Very easy , simple and will you be love programmi
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    Many types of collections
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    If the implementation is easy to explain, it may be a g
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    Batteries included
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    Should START with this but not STICK with This
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    Powerful language for AI
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    Can understand easily who are new to programming
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    Flexible and easy
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    Good for hacking
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    A-to-Z
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    Because of Netflix
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    Only one way to do it
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    Better outcome
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    Sexy af
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    Slow
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    Securit
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    Ni
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    Powerful
CONS OF PYTHON
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    Still divided between python 2 and python 3
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    Performance impact
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    Poor syntax for anonymous functions
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    GIL
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    Package management is a mess
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    Too imperative-oriented
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    Hard to understand
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    Dynamic typing
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    Very slow
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    Indentations matter a lot
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    Not everything is expression
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    Incredibly slow
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    Explicit self parameter in methods
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    Requires C functions for dynamic modules
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    Poor DSL capabilities
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    No anonymous functions
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    Fake object-oriented programming
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    Threading
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    The "lisp style" whitespaces
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    Official documentation is unclear.
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    Hard to obfuscate
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    Circular import
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    Lack of Syntax Sugar leads to "the pyramid of doom"
  • 4
    The benevolent-dictator-for-life quit
  • 4
    Not suitable for autocomplete
  • 2
    Meta classes
  • 1
    Training wheels (forced indentation)

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Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 10.2M 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

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Nick Parsons
Building cool things on the internet 🛠️ at Stream · | 35 upvotes · 3.5M 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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    CONS OF AZURE DATABRICKS
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      Amazon SageMaker logo

      Amazon SageMaker

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      Accelerated Machine Learning
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          Harsh Solanki
          Machine Learning Engineer at Quantiphi · | 4 upvotes · 23.2K views

          We have to process the video stream to identify emotions. For which we need to use Amazon Rekognition/custom model on Amazon SageMaker. With Kinesis WebRTC Javascript SDK, currently, video can be streamed only into the kinesis signaling channel. Signaling channel data is available for streaming only and not processing (ML). So, how can we get real-time data for processing into Kinesis Streams from the frontend?

          For streaming the video from frontend to backend into the Amazon Kinesis Video Streams for processing, we tested with Kinesis webRTC JavaScript SDK, and we are facing issues while implementing as mentioned above, so would Chime SDK serve as an alternative to this?

          In Rekognition, "create-stream-processor" has a settings parameter. This currently only supports FaceSearch. We are looking to Detect and analyze faces. Is that possible with "create-stream-processor" in the Python SDK? Or do we have to use the Java SDK?

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          Arthur Boghossian
          DevOps Engineer at PlayAsYouGo · | 3 upvotes · 142.1K views

          For our Compute services, we decided to use AWS Lambda as it is perfect for quick executions (perfect for a bot), is serverless, and is required by Amazon Lex, which we will use as the framework for our bot. We chose Amazon Lex as it integrates well with other #AWS services and uses the same technology as Alexa. This will give customers the ability to purchase licenses through their Alexa device. We chose Amazon DynamoDB to store customer information as it is a noSQL database, has high performance, and highly available. If we decide to train our own models for license recommendation we will either use Amazon SageMaker or Amazon EC2 with AWS Elastic Load Balancing (ELB) and AWS ASG as they are ideal for model training and inference.

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          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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              Julien DeFrance
              Principal Software Engineer at Tophatter · | 2 upvotes · 84.7K 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.

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              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
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                Usage Based Billing
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                Security
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                Data stays in your cloud account
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                Multicloud
              CONS OF DATABRICKS
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                Jan Vlnas
                Developer Advocate at Superface · | 5 upvotes · 429.8K 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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                Vamshi Krishna
                Data Engineer at Tata Consultancy Services · | 4 upvotes · 245.9K views

                I have to collect different data from multiple sources and store them in a single cloud location. Then perform cleaning and transforming using PySpark, and push the end results to other applications like reporting tools, etc. What would be the best solution? I can only think of Azure Data Factory + Databricks. Are there any alternatives to #AWS services + Databricks?

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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
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                  Simplified Logging
                CONS OF MLFLOW
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                  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 · 845.5K 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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                    Easy to use
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                    High level abstraction
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                    Powerful
                  CONS OF TENSORFLOW
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                    Hard
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                    Hard to debug
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                    Documentation not very helpful

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                  Google Analytics is a great tool to analyze your traffic. To debug our software and ask questions, we love to use Postman and Stack Overflow. Google Drive helps our team to share documents. We're able to build our great products through the APIs by Google Maps, CloudFlare, Stripe, PayPal, Twilio, Let's Encrypt, and TensorFlow.

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                  Conor Myhrvold
                  Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 2.8M 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)

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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
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                    Api
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                    Prebuilt front-end GUI
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                    Intent auto-generation
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                    Custom webhooks
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                    Disambiguation
                  CONS OF IBM WATSON
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                    Multi-lingual

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