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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Amazon SageMaker AI

    Score8.9 out of 10
    N/AAmazon SageMaker AI is a fully managed AWS service for building, training, customizing, deploying, and managing AI and machine-learning models. It provides development environments, managed training infrastructure, model-serving options, experiment tracking, and governance controls for the model development lifecycle.N/A

    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
    Pricing
    Amazon SageMaker AIIguazio
    Editions & Modules
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    Offerings
    Pricing Offerings
    Amazon SageMaker AIIguazio
    Free Trial
    NoNo
    Free/Freemium Version
    NoNo
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details
    More Pricing Information
    Best Alternatives
    Amazon SageMaker AIIguazio
    Small Businesses
    RapidMiner
    Score8.9 out of 10
    No answers on this topic
    Medium-sized Companies
    Anaconda
    Score8.8 out of 10
    No answers on this topic
    Enterprises
    IBM Watson Studio
    Score10 out of 10
    No answers on this topic
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Amazon SageMaker AIIguazio
    Likelihood to Recommend
    9.0
    (5 ratings)
    10.0
    (2 ratings)
    User Testimonials
    Amazon SageMaker AIIguazio
    Likelihood to Recommend
    Amazon AWS
    It allows for one-click processes and for things to be auto checked before they are moved through the process but through the system. It also makes training easy. I am able to train users on the basic fundamentals of the tool and how it is used very easily as it is fully managed on its own which is incredible.
    Incentivized
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    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
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    Pros
    Amazon AWS
    • Machine Learning at scale by deploying huge amount of training data
    • Accelerated data processing for faster outputs and learnings
    • Kubernetes integration for containerized deployments
    • Creating API endpoints for use by technical users
    Incentivized
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    McKinsey & Company
    • Dynamic scaling capacity.
    • Central Metadata management.
    • Data ingestion and preparation.
    Incentivized
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    Cons
    Amazon AWS
    • It's very good for the hardcore programmer, but a little bit complex for a data scientist or new hire who does not have a strong programming background.
    • Most of the popular library and ML frameworks are there, but we still have to depend on them for new releases.
    Incentivized
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    McKinsey & Company
    • The user interface is not so much user-friendly, and easy-to-use, navigate.
    Incentivized
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    Alternatives Considered
    Amazon AWS
    Amazon SageMaker took the heavy lifting out of building and creating models. It allowed for our organization to use our current system for integration and essentially added on a feature to help all levels of Data scientists and IT professionals in our department and company as a whole. The training was simple as well.
    Incentivized
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    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
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    Return on Investment
    Amazon AWS
    • We have been able to deliver data products more rapidly because we spend less time building data pipelines and model servers.
    • We can prototype more rapidly because it is easy to configure notebooks to access AWS resources.
    • For our use-cases, serving models is less expensive with SageMaker than bespoke servers.
    Incentivized
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    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
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