Iguazio

Herzliya, Israel
2014
Nov 17, 2022   |  By Alexandra Quinn
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.
Oct 11, 2022   |  By Xingsheng Qian
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.
Sep 20, 2022   |  By Adi Hirschtein
When operationalizing machine and deep learning, a production-first approach is essential for moving from research and development to scalable production pipelines in a much faster and more effective manner. Without the need to refactor code, add glue logic and spend significant efforts on data and ML engineering, more models will make it to production and with less issues like drift.
Sep 6, 2022   |  By Alexandra Quinn
Financial services companies are leveraging data and machine learning to mitigate risks like fraud and cyber threats and to provide a modern customer experience. By following these measures, they are able to comply with regulations, optimize their trading and answer their customers’ needs. In today’s competitive digital world, these changes are essential for ensuring their relevance and efficiency.
Aug 30, 2022   |  By Alexandra Quinn
Iguazio users can now run their ML workloads on AWS EC2 Spot instances. When running ML functions, you might want to control whether to run on Spot nodes or On-Demand compute instances. When deploying Iguazio MLOps platform on AWS, running a job (e.g. model training) or deploying a serving function users are now able to choose to deploy it on AWS EC2 Spot compute instances.
Aug 23, 2022   |  By Guy Lecker
AutoML with experiment tracking enables logging and tracking results and parameters, to optimize machine learning processes. But current AutoML platforms only train models based on provided data. They lack solutions that automate the entire ML pipeline, leaving data scientists and data engineers to deal with manual operationalization efforts. In this post, we provide an open source solution for AutoMLOps, which automates engineering tasks so that your code is automatically ready for production.
Aug 10, 2022   |  By Xingsheng Qian
In this article, we will walk you through steps to run a Jenkins server in docker and deploy the MLRun project using Jenkins pipeline. Before we dive into the actual set up, let’s have a brief background on the MLRun and Jenkins.
Aug 2, 2022   |  By Sahar Dolev-Blitental
We’re so proud to share that Iguazio has been named a sample vendor in eight Gartner Hype Cycles in 2022: Iguazio was mentioned in the following categories: MLOps, Logical Feature Store, Adaptive ML, Data-Centric AI, AI Engineering, AI TRiSM, Operational AI Systems, ModelOps, AI Engineering in HCLS and Continuous Intelligence. We are delighted to have been mentioned alongside global industry leaders like AWS, IBM, Microsoft, Google, Databricks and Dataiku.
Jul 19, 2022   |  By Nick Schenone and John Crupi
Machine learning is more accessible than ever, with datasets available online and Jupyter notebooks providing an easy way to explore and train models. In building a model, we often forget that it will be incorporated into an application that will provide value to the user. Therefore, we wanted to demonstrate how we can "use" the models we build in an application.
Jul 13, 2022   |  By Alexandra Quinn
Machine Learning is revolutionizing the world of healthcare. ML models can help predict patient deterioration, optimize logistics, assist with real-time surgery and even determine drug dosage. As a result, medical personnel are able to work more efficiently, serve patients better and provide higher quality healthcare.
Nov 23, 2022   |  By Iguazio
In this session, Jiri shares enterprise secrets to establishing efficient systems for ML/AI and how his team: Watch Jiri and Yaron's fascinating deep dive into HCI’s journey to MLOps efficiency.
Oct 26, 2022   |  By Iguazio
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!
Oct 6, 2022   |  By Iguazio
A demo showing how to use our feature store in conjunction with Snowflake. Focusing on.
Sep 12, 2022   |  By Iguazio
Watch this video with Yaron, CTO and Co-Founder of Iguazio as he dives into different features and ways to use Iguazio's open source tool, MLRun.
Aug 16, 2022   |  By Iguazio
Take a look at this product overview in-depth tutorial of the Iguazio MLOps Platform.
Aug 16, 2022   |  By Iguazio

The Iguazio Data Science Platform automates MLOps with end-to-end machine learning pipelines, transforming AI projects into real-world business outcomes. It accelerates the development, deployment and management of AI applications at scale, enabling data scientists to focus on delivering better, more accurate and more powerful solutions instead of spending their time on infrastructure.

The platform is open and deployable anywhere - multi-cloud, on prem or edge. Iguazio powers real-time data science applications for financial services, gaming, ad-tech, manufacturing, smart mobility and telecoms.

Dive Into the Machine Learning Pipeline:

  • Collect and Enrich Data from Any Source: Ingest in real-time multi-model data at scale, including event-driven streaming, time series, NoSQL, SQL and files.
  • Prepare Online and Offline Data at Scale: Explore and manipulate online and offline data at scale, powered by Iguazio's real-time data layer and using your favorite data science and analytics frameworks, already pre-installed in the platform.
  • Accelerate and Automate Model Training: Continuously train models in a production-like environment, dynamically scaling GPUs and managed machine learning frameworks.
  • Deploy in Seconds: Deploy models and APIs from a Jupyter notebook or IDE to production in just a few clicks and continuously monitor model performance.

Bring Your Data Science to Life.