Bring New People and Roles Into AI Projects With Dataiku 10

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ChristinaH
ChristinaH Dataiker, Alpha Tester, Dataiku DSS Core Designer, Dataiku DSS ML Practitioner, Dataiku DSS Adv Designer, Registered Posts: 15 Dataiker

Originally posted by Dataiku on November 30, 2021

Analysts, data engineers, and data scientists have always been core roles that contribute to advanced analytics projects. But in order for an organization to scale AI initiatives with a more systematic approach, the development, operationalization, and oversight of AI projects must also include contributors from different parts of the organization, including IT operators, project managers, risk managers, and subject matter experts (SMEs). Dataiku 10 has exciting new capabilities that accelerate time to value and empower people across diverse functions to engage in data projects and responsibly deliver and manage AI applications.

Deploy and Maintain More Models in Production

With Dataiku 10, data scientists and IT operators have additional tools and more flexibility to deploy, monitor, and manage machine learning (ML) models at scale. This release features built-in drift monitoring and alerts, as well as a model evaluation store to capture and visualize performance metrics over time. Once a model is developed or retrained, a model comparison tool aids with Champion/Challenger analysis to help operators determine the best course of action and continuously improve outcomes.

Furthermore, in addition to models developed natively in Dataiku, models developed externally in MLFlow can now also be deployed, managed, and governed in Dataiku. Risk and operations teams will appreciate Dataiku’s robust MLOps framework that orchestrates model delivery activities in a central location and reduces the burdens of manual model oversight and routine maintenance.

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Safely Scale Initiatives With Oversight

Dataiku 10 also introduces new capabilities for AI Governance, project workflows, reviews and sign-off, and portfolio oversight for enterprise customers. Key business and project stakeholders can first use a standardized framework to assess potential project value and risks, both to compare initiatives for investment and determine appropriate oversight levels.

Customizable project plans with clear steps and a sign-off feature for model deployment enable analytics teams to explore, build, test, and deploy projects with the appropriate reviews and approvals. Finally, the model registry provides a centralized way to see all models (whether developed in Dataiku or externally) in one place, versioned and with performance metrics and project summaries. The result of this multifaceted governance strategy is increased transparency, trust, and confidence in both the analytical process and the system’s outputs.

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Deliver Value Faster With Business Solutions and Accelerators

Organizations can achieve faster speed to value by leveraging Dataiku Industry Solutions, off-the-shelf starter projects for vertical use cases that teams can adapt and apply for their own purposes. Enhanced visual statistics, geospatial analytics, and new visualization capabilities make it even easier for designers to explore and enrich data and distribute interactive insights to business stakeholders. Dedicated workspaces provide AI consumers with a single point of access to a variety of analytic outputs, facilitating adoption of Everyday AI. Finally, data scientists and SMEs alike will love the additions to interactive scoring, taking what-if analysis to the next level by enabling teams to prescribe the specific changes to inputs needed in order to influence a different outcome.

Dataiku is the platform for Everyday AI, systemizing the use of data for exceptional business results. With Dataiku 10, organizations can involve more users across a diverse set of roles (i.e., IT operations, domain experts, risk managers) to ultimately create, deliver, and manage more high-value projects at scale. Ready to dive into the details on Dataiku 10? Check out the full list of resources on what's new.

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Answers

  • tgb417
    tgb417 Dataiku DSS Core Designer, Dataiku DSS & SQL, Dataiku DSS ML Practitioner, Dataiku DSS Core Concepts, Neuron 2020, Neuron, Registered, Dataiku Frontrunner Awards 2021 Finalist, Neuron 2021, Neuron 2022, Frontrunner 2022 Finalist, Frontrunner 2022 Winner, Dataiku Frontrunner Awards 2021 Participant, Frontrunner 2022 Participant, Neuron 2023 Posts: 1,595 Neuron
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    Congratulations to the team on the release of V10!!

  • Skjorwald
    Skjorwald Registered Posts: 1 ✭✭✭
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    Hey, guys, glad to see the new release. Have you solved Apple M1 compatibility problem in it?

  • CoreyS
    CoreyS Dataiker Alumni, Dataiku DSS Core Designer, Dataiku DSS Core Concepts, Registered Posts: 1,150 ✭✭✭✭✭✭✭✭✭
    edited July 17
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    Hi @Skjorwald
    and welcome to the Dataiku Community. At this time DSS installation on Apple M1 MacBooks is not supported. We currently do not have an ETA for a compatible release. More information about M1 compatibility with DSS can be found on these threads: DSS fail to install on Mac M1

    If this is the first time you are trying out DSS and would still like to use it, we encourage you to use Dataiku Online with a 14 day free trial.

    This is a known limitation due to python compilation issues (not really DSS but still). Based on the community thread, you can try to workaround this by installing the Rosetta emulator:

    https://support.apple.com/en-us/HT211861

    Also, you can try an option with base Python3.7 for installer.sh:

    <DSS_INSTALL_DIR>/installer.sh -d <DSS_DATA_DIR> -P python3.7

    I hope this helps!

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