All resources sponsored by:

Iguazio

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How to Decide Whether to Build or Buy your MLOps Platform
sponsored by Iguazio
WHITE PAPER:MLOps is a great way to take advantage of AI and operationalize data science, but building an in-house solution can be time consuming and expensive. Access this white paper to learn how you can properly weigh the pros and cons of an off the shelf MLOps when it comes to key factors like time constrains, costs, and team skillsets in mind.
Posted: 16 Sep 2021 | Published: 16 Sep 2021
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GigaOm Radar for Evaluating Machine Learning Operations (MLOps)
sponsored by Iguazio
ANALYST REPORT:Download your copy of the GigaOm 2021 “Radar for MLOps Report.” The report includes a checklist of key criteria to consider when choosing an MLOps platform, a list of the leading MLOps solutions in the market today, and explanation of the strengths and weaknesses of each platform. Also find out why GigaOm named Iguazio a “fast-moving leader.”
Posted: 15 Sep 2021 | Published: 05 Apr 2021
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Understanding and Using Data Science and AI, at the Edge and in the Cloud
sponsored by Iguazio
WHITE PAPER:Download this e-guide to learn how to best position your AI projects to take advantage of both edge and cloud environments and discover how to achieve success with your data science initiatives.
Posted: 17 May 2021 | Published: 11 May 2021
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Hygiene technologies leader Ecolab brings data science to production with Microsoft Azure and Iguazio
sponsored by Iguazio
CASE STUDY:To deal with the complexity of scaling ML projects, Ecolab teamed up with Iguazio to accelerate their cloud data science projects without incurring exorbitant costs. Prior to implementing this solution, model deployment times exceeded 12 months. Learn how, thanks to Iguazio and Microsoft Azure, these had been reduced to between 30 and 90 days.
Posted: 10 May 2021 | Published: 01 Apr 2021
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What Successful AI Looks Like: Understanding AI/ML Infrastructure & Operationalization
sponsored by Iguazio
WHITE PAPER:AI and ML projects are far different than app development, or any other IT project. For this reason, it’s essential that you understand the infrastructure requirements underpinning it. Download this expert guide to discover the unique ins and outs that are crucial to seeing AI and ML success in development, deployment, and operationalization.
Posted: 03 May 2021 | Published: 29 Apr 2021
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Payoneer Customer Case Study
sponsored by Iguazio
CASE STUDY:In this case study, explore how a digital payment platform used the Iguazio data science platform to leverage real-time machine learning and predictive analytics to proactively detect and prevent fraud across its complex network. Read the case study now.
Posted: 28 Apr 2021 | Published: 28 Apr 2021
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TOPICS:  .NET

Creating a unified data science and data analytics solution ideal for AI and automation at scale
sponsored by Iguazio
CASE STUDY:Access this short case study to learn how Iguazio overhauled a cloud provider’s legacy data warehouse and Hadoop-based data lake with a cloud-native, Kubernetes-powered platform, complete with highly accessible end-to-end ML pipelines ideal for AI and ML operations at scale.
Posted: 27 Apr 2021 | Published: 27 Apr 2021
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How Feature Stores Accelerate & Simplify Deployment of AI to Production
sponsored by Iguazio
WEBCAST:MLOps is the fuel driving AI, but it can be hard to create, synchronize, access and monitor its key components. Access this resource to learn how a feature store allows you to easily research, deploy, and govern features within an MLOps framework and see the benefits of this feature store in action.
Posted: 22 Apr 2021 | Premiered: Apr 22, 2021
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Git-Based CI/CD for Machine Learning & MLOps
sponsored by Iguazio
WEBCAST:Running AI experiments at scale, enabling data access, building reproducible products—all of these things are often holding back AI and ML initiatives. Explore this resource to learn how GitHub-based CI/CD processes fit with an MLOps vision and allow better scalability, collaboration, version control, model training, deployment and more.
Posted: 22 Apr 2021 | Premiered: May 31, 2020
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Predicting 1st-Day Churn in Real-Time with MLOps
sponsored by Iguazio
WEBCAST:Explore this machine learning (ML) operationalization resource to learn how ML can be used to help gaming and mobile app companies predict and mitigate first-day user churn in real-time, discover an improved churn prediction framework, investigate real-time feature engineering and build a real-time ML pipeline in a few simple steps.
Posted: 21 Apr 2021 | Premiered: Apr 21, 2021
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