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Iguazio

Looking into 2023: Predictions for a New Year in MLOps

In 2022, AI and ML came into the mainstream consciousness, with generative AI applications like Dall-E and GPT AI becoming massively popular among the general public, and ethical questions of AI usage stirring up impassioned public debate. No longer a side project for forward-thinking businesses or CEOs that find it intriguing, AI and ML are now moving towards the center of the business.

Iguazio Named a Major Player in the IDC MLOps MarketScape 2022

The IDC MarketScape: Worldwide Machine Learning Operations Platforms 2022 Vendor Assessment is an annual study that evaluates technology vendors based on a comprehensive framework. It provides an in-depth quantitative and qualitative assessment of MLOps solution vendors in a long-form research report, to help buyers make important technology decisions that will create long term business success.

Iguazio Named a Leader and Outperformer In GigaOm Radar for MLOps 2022

The GigaOm Radar reports support leaders looking to evaluate technologies with an eye towards the future. In this year's Radar for MLOps report, GigaOm gave Iguazio top scores on multiple evaluation metrics, including Advanced Monitoring, Autoscaling & Retraining, CI/CD, and Deployment. Iguazio was therefore named a leader and also classified as an Outperformer for its rapid pace of innovation.

Deploying Your Hugging Face Models to Production at Scale with MLRun

Hugging Face is a popular model repository that provides simplified tools for building, training and deploying ML models. The growing adoption of Hugging Face usage among data professionals, alongside the increasing global need to become more efficient and sustainable when developing and deploying ML models, make Hugging Face an important technology and platform to learn and master.

How to Easily Deploy Your Hugging Face Models to Production - MLOps Live #20- With Hugging Face

Watch Julien Simon (Hugging Face), Noah Gift (MLOps Expert) and Aaron Haviv (Iguazio) discuss how you can deploy models into real business environments, serve them continuously at scale, manage their lifecycle in production, and much more in this on-demand webinar!

How to Run Workloads on Spark Operator with Dynamic Allocation Using MLRun

With the Apache Spark 3.1 release in early 2021, the Spark on Kubernetes project has been production-ready for a few years. Spark on Kubernetes has become the new standard for deploying Spark. In the Iguazio MLOps platform, we built the Spark Operator into the platform to make the deployment of Spark Operator much simpler.