Alternatives to Kubeflow logo

Alternatives to Kubeflow

TensorFlow, Apache Spark, MLflow, Airflow, and Polyaxon are the most popular alternatives and competitors to Kubeflow.
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What is Kubeflow and what are its top alternatives?

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.
Kubeflow is a tool in the Machine Learning Tools category of a tech stack.
Kubeflow is an open source tool with GitHub stars and GitHub forks. Here’s a link to Kubeflow's open source repository on GitHub

Top Alternatives to Kubeflow

  • 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. ...

  • Apache Spark
    Apache Spark

    Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning. ...

  • MLflow
    MLflow

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

  • Airflow
    Airflow

    Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed. ...

  • Polyaxon
    Polyaxon

    An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications. ...

  • Argo
    Argo

    Argo is an open source container-native workflow engine for getting work done on Kubernetes. Argo is implemented as a Kubernetes CRD (Custom Resource Definition). ...

  • Kubernetes
    Kubernetes

    Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions. ...

  • Pachyderm
    Pachyderm

    Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations. ...

Kubeflow alternatives & related posts

TensorFlow logo

TensorFlow

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Open Source Software Library for Machine Intelligence
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PROS OF TENSORFLOW
  • 32
    High Performance
  • 19
    Connect Research and Production
  • 16
    Deep Flexibility
  • 12
    Auto-Differentiation
  • 11
    True Portability
  • 6
    Easy to use
  • 5
    High level abstraction
  • 5
    Powerful
CONS OF TENSORFLOW
  • 9
    Hard
  • 6
    Hard to debug
  • 2
    Documentation not very helpful

related TensorFlow posts

Tom Klein

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.

See more
Shared insights
on
TensorFlowTensorFlowDjangoDjangoPythonPython

Hi, I have an LMS application, currently developed in Python-Django.

It works all very well, students can view their classes and submit exams, but I have noticed that some students are sharing exam answers with other students and let's say they already have a model of the exams.

I want with the help of artificial intelligence, the exams to have different questions and in a different order for each student, what technology should I learn to develop something like this? I am a Python-Django developer but my focus is on web development, I have never touched anything from A.I.

What do you think about TensorFlow?

Please, I would appreciate all your ideas and opinions, thank you very much in advance.

See more
Apache Spark logo

Apache Spark

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Fast and general engine for large-scale data processing
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PROS OF APACHE SPARK
  • 61
    Open-source
  • 48
    Fast and Flexible
  • 8
    One platform for every big data problem
  • 8
    Great for distributed SQL like applications
  • 6
    Easy to install and to use
  • 3
    Works well for most Datascience usecases
  • 2
    Interactive Query
  • 2
    Machine learning libratimery, Streaming in real
  • 2
    In memory Computation
CONS OF APACHE SPARK
  • 4
    Speed

related Apache Spark posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 11.2M 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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Eric Colson
Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 6.1M views

The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

For more info:

#DataScience #DataStack #Data

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MLflow logo

MLflow

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An open source machine learning platform
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PROS OF MLFLOW
  • 5
    Code First
  • 4
    Simplified Logging
CONS OF MLFLOW
    Be the first to leave a con

    related MLflow posts

    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 · 847.3K 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.

    See more
    Airflow logo

    Airflow

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    A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb
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    PROS OF AIRFLOW
    • 51
      Features
    • 14
      Task Dependency Management
    • 12
      Beautiful UI
    • 12
      Cluster of workers
    • 10
      Extensibility
    • 6
      Open source
    • 5
      Complex workflows
    • 5
      Python
    • 3
      Good api
    • 3
      Apache project
    • 3
      Custom operators
    • 2
      Dashboard
    CONS OF AIRFLOW
    • 2
      Observability is not great when the DAGs exceed 250
    • 2
      Running it on kubernetes cluster relatively complex
    • 2
      Open source - provides minimum or no support
    • 1
      Logical separation of DAGs is not straight forward

    related Airflow posts

    Data science and engineering teams at Lyft maintain several big data pipelines that serve as the foundation for various types of analysis throughout the business.

    Apache Airflow sits at the center of this big data infrastructure, allowing users to “programmatically author, schedule, and monitor data pipelines.” Airflow is an open source tool, and “Lyft is the very first Airflow adopter in production since the project was open sourced around three years ago.”

    There are several key components of the architecture. A web UI allows users to view the status of their queries, along with an audit trail of any modifications the query. A metadata database stores things like job status and task instance status. A multi-process scheduler handles job requests, and triggers the executor to execute those tasks.

    Airflow supports several executors, though Lyft uses CeleryExecutor to scale task execution in production. Airflow is deployed to three Amazon Auto Scaling Groups, with each associated with a celery queue.

    Audit logs supplied to the web UI are powered by the existing Airflow audit logs as well as Flask signal.

    Datadog, Statsd, Grafana, and PagerDuty are all used to monitor the Airflow system.

    See more

    We are a young start-up with 2 developers and a team in India looking to choose our next ETL tool. We have a few processes in Azure Data Factory but are looking to switch to a better platform. We were debating Trifacta and Airflow. Or even staying with Azure Data Factory. The use case will be to feed data to front-end APIs.

    See more
    Polyaxon logo

    Polyaxon

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    An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.
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    PROS OF POLYAXON
    • 2
      Cli
    • 2
      API
    • 2
      Streamlit integration
    • 2
      Python Client
    • 2
      Notebook integration
    • 2
      Tensorboard integration
    • 2
      VSCode integration
    CONS OF POLYAXON
      Be the first to leave a con

      related Polyaxon posts

      Argo logo

      Argo

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      Container-native workflows for Kubernetes
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      PROS OF ARGO
      • 3
        Open Source
      • 2
        Autosinchronize the changes to deploy
      • 1
        Online service, no need to install anything
      CONS OF ARGO
        Be the first to leave a con

        related Argo posts

        Kubernetes logo

        Kubernetes

        59.2K
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        Manage a cluster of Linux containers as a single system to accelerate Dev and simplify Ops
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        PROS OF KUBERNETES
        • 164
          Leading docker container management solution
        • 128
          Simple and powerful
        • 107
          Open source
        • 76
          Backed by google
        • 58
          The right abstractions
        • 25
          Scale services
        • 20
          Replication controller
        • 11
          Permission managment
        • 9
          Supports autoscaling
        • 8
          Cheap
        • 8
          Simple
        • 6
          Self-healing
        • 5
          Promotes modern/good infrascture practice
        • 5
          Open, powerful, stable
        • 5
          Reliable
        • 5
          No cloud platform lock-in
        • 4
          Scalable
        • 4
          Quick cloud setup
        • 3
          Custom and extensibility
        • 3
          A self healing environment with rich metadata
        • 3
          Cloud Agnostic
        • 3
          Backed by Red Hat
        • 3
          Runs on azure
        • 3
          Captain of Container Ship
        • 2
          Expandable
        • 2
          Sfg
        • 2
          Everything of CaaS
        • 2
          Golang
        • 2
          Easy setup
        • 2
          Gke
        CONS OF KUBERNETES
        • 16
          Steep learning curve
        • 15
          Poor workflow for development
        • 8
          Orchestrates only infrastructure
        • 4
          High resource requirements for on-prem clusters
        • 2
          Too heavy for simple systems
        • 1
          Additional vendor lock-in (Docker)
        • 1
          More moving parts to secure
        • 1
          Additional Technology Overhead

        related Kubernetes posts

        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 11.2M 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

        See more
        Ashish Singh
        Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 3M views

        To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

        Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.

        We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

        Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.

        Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

        #BigData #AWS #DataScience #DataEngineering

        See more
        Pachyderm logo

        Pachyderm

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        MapReduce without Hadoop. Analyze massive datasets with Docker.
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        PROS OF PACHYDERM
        • 3
          Containers
        • 1
          Versioning
        • 1
          Can run on GCP or AWS
        CONS OF PACHYDERM
        • 1
          Recently acquired by HPE, uncertain future.

        related Pachyderm posts