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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Iguazio

    Score10 out of 10
    N/AIguazio, a McKinsey company, offers the Iguazio MLOps Platform used to develop and manage AI applications at scale. It provides data science, data engineering and DevOps teams with a platform to deploy operational ML pipelines.N/A

    Weights & Biases

    Score10 out of 10
    N/AWeights & Biases helps machine learning teams build better models. Practitioners can debug, compare and reproduce their models — architecture, hyperparameters, git commits, model weights, GPU usage, datasets and predictions — and collaborate with their teammates.

    $50

    per month per user

    Pricing
    IguazioWeights & Biases
    Editions & Modules
    No answers on this topic
    Starter
    $50
    per month per user
    Enterprise
    custom pricing
    Offerings
    Pricing Offerings
    IguazioWeights & Biases
    Free Trial
    NoNo
    Free/Freemium Version
    NoYes
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details——
    More Pricing Information
    User Ratings
    IguazioWeights & Biases
    Likelihood to Recommend
    10.0
    (2 ratings)
    10.0
    (1 ratings)
    User Testimonials
    IguazioWeights & Biases
    Likelihood to Recommend
    McKinsey & Company
    With Iguazio we are able to scale up our organisations AI infrastructure which us vital to meet business goals and accelerate time-to-time. We are also able to manage our ML pipeline end-to-end using a full-stack,user-friendly environment, feature-rich integrated feature store and powerful data transformation and real-time feature engineering capabilities.
    Incentivized
    Read full review
    Weights & Biases
    No brainer to use it when doing ML experiments as it is very easy compared to any other open source tool. You don't have to host anything like in Tensorboard.
    Experiment details can be shared very easily with public using the reports
    Incentivized
    Read full review
    Pros
    McKinsey & Company
    • Dynamic scaling capacity.
    • Central Metadata management.
    • Data ingestion and preparation.
    Incentivized
    Read full review
    Weights & Biases
    • Metrics Logging
    • Hyperparmeters Sweeps
    • Model Artifcats
    Incentivized
    Read full review
    Cons
    McKinsey & Company
    • The user interface is not so much user-friendly, and easy-to-use, navigate.
    Incentivized
    Read full review
    Weights & Biases
    • Dashboard lags when we log a lot of metrics
    • Improved support for matplotlib charts and documentation of wandb custom charts is not straghtforward
    Incentivized
    Read full review
    Alternatives Considered
    McKinsey & Company
    Execution, experiment, data, model tracking, and automated deployment is done automatically through the MLRun serverless runtime engine. MLRun maintains a project hierarchy with strict membership and cross-team collaboration. End-to-end data governance is fully solidified and managed with authentication and identity management. Customers securely share data by providing access directly to it and not to copies.
    Incentivized
    Read full review
    Weights & Biases
    No answers on this topic
    Return on Investment
    McKinsey & Company
    • Is a fully integrated solution with a user-friendly portal.
    • Manage our ML pipeline end-to-end using Full-stack,user friendly environment.
    • Iguazio enables our teams to manage all artefacts throughout their lifecycle.
    • Enhance team work and collaboration in our teams.
    Incentivized
    Read full review
    Weights & Biases
    • Made it very easy to track experiments
    • Track ML and Business Metrics improvements across experiments
    • Reproduce runs which is essential in ML modelling
    Incentivized
    Read full review
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