Iguazio

Herzliya, Israel
2014
Jun 21, 2022   |  By Xingsheng Qian
In your machine learning projects, have you ever wondered “why is model Y is performing better than Z, which dataset was model Y trained on, what are the training parameters I used for model Y, and what are the model performance metrics I used to select model Y?” Does this sound familiar to you? Have you wondered if there is a simple way to answer the questions above? Data science experiments can get complex, which is why you need a system to simplify tracking.
Jun 13, 2022   |  By Sahar Dolev-Blitental
We’re proud to share that Iguazio has been named in Gartner's 2022 Market Guide for Data Science & Machine Learning Engineering Platforms. According to Gartner, “The AI & data science platform market is due to grow to over $10 billion by 2025 at a 21.6% compounded annual growth rate.
Jun 10, 2022   |  By Adi Hirschtein
As a very hands-on VP of Product, I have many, many conversations with enterprise data science teams who are in the process of developing their MLOps practice. Almost every customer I meet is in some stage of developing an ML-based application. Some are just at the beginning of their journey while others are already heavily invested. It’s fascinating to see how data science, a once commonly used buzz word, is becoming a real and practical strategy for almost any company.
May 31, 2022   |  By Alexandra Quinn
Data science is an important skill, but the hard truth is many organizations aren’t seeing the ROI showing that data science work is making a business impact. Yet today, many organizations are still struggling to adopt a holistic approach centered around creating business value. Instead, they are focused on theoretical work. Here at Iguazio, we recently held a webinar with Noah Gift, founder of Pragmatic A.I. Labs, professor, author and MLOps consultant.
May 9, 2022   |  By Brennan Smith and Xingsheng Qian
Many organizations are turning to Snowflake to store their enterprise data, as the company has expanded its ecosystem of data science and machine learning initiatives. Snowflake offers many connectors and drivers for various frameworks to get data out of their cloud warehouse. For machine learning workloads, the most attractive of these options is the Snowflake Connector for Python.
Apr 8, 2022   |  By Adi Hirschtein
First, we collect data from an existing Kafka stream into an Iguazio time series table. Next, we visualize the stream with a Grafana dashboard; and finally, we access the data in a Jupyter notebook using Python code. We use a Nuclio serverless function to “listen” to a Kafka stream and then ingest its events into our time series table. Iguazio gets you started with a template for Kafka to time series.
Apr 7, 2022   |  By Sahar Dolev-Blitental
We’re proud to share that the Iguazio MLOps Platform has been named a leader and outperformer in the GigaOm Radar for Data Science Platforms: Pure-Play Specialist and Startup Vendors report. The GigaOm Radar reports take a forward-looking view of the market and are geared towards IT leaders tasked with evaluating solutions with an eye to the future. GigaOm analysts emphasize the value of innovation and differentiation over incumbent market position.
Apr 4, 2022   |  By Sahar Dolev-Blitental
We are delighted to share that Iguazio has been named along with Microsoft, Databricks, Cloudera, Alteryx and others in Now Tech: AI/ML Platforms, Q1 2022, Forrester’s Overview of the Leading AI/ML Platform Providers, by Mike Gualtieri. This report by Forrester Research looks at AI/ML Platform providers, to help technology executives evaluate and select one based on functionality aligned with their needs.
Apr 4, 2022   |  By Alexandra Quinn
Machine learning is a practice that is evolving and developing every day. Newfound technologies, inventions and methodologies are being introduced to the community on a daily basis. As ML professionals, we can enrich our knowledge and become better at what we do by constantly learning from each other. But with so many resources out there, it might be overwhelming to choose which ones to stay up-to-date on. So where is the best place to start?
Mar 17, 2022   |  By Alexandra Quinn
When businesses begin applying machine learning (ML) workflows to their use cases, it’s typically a manual and iterative process—each step in the ML workflow is executed until a suitably trained ML model is deployed to production.
May 9, 2022   |  By Iguazio
Watch this MLRun tutorial with CTO and Co-Founder, Yaron Haviv to see a step by step tutorial of MLRun and how it's used to bring models to production.
Mar 29, 2022   |  By Iguazio
Watch this 4 minute Introduction to Iguazio video to learn more about the technical functionalities, benefits, and features of the Iguazio MLOps Platform. Want to discuss your MLOps challenges or use case? Get in touch with us today.
Mar 9, 2022   |  By Iguazio
Watch this 4 minute Introduction to Iguazio video to learn more about the technical functionalities, benefits, and features of the Iguazio MLOps Platform.
Jan 12, 2022   |  By Iguazio
In this session, Yaron Haviv, Iguazio's Co-Founder and CTO, discussed how to enable continuous delivery of machine learning to production using Git-based ML pipelines (Github Actions) with hosted training and model serving environments. He touched upon how to leverage Git to solve rigorous MLOps needs: automating workflows, reviewing models, storing versioned models as artifacts, and running CI/CD for ML. He also covered how to enable controlled collaboration across ML teams using Git review processes and how to implement an MLOps solution based on available open-source tools and hosted ML platforms. The session includes a live demo.
Dec 15, 2021   |  By Iguazio
The data science team at IHS Markit will be sharing practical advice on building sophisticated NLP pipelines that work at scale. Using a robust and automated MLOps process, they run complex models that make massive amounts of unstructured data searchable and indexable. In this session, they will share their journey with MLOps and provide practical advice for other data science teams looking to.
Dec 14, 2021   |  By Iguazio
MLOps holds the key to accelerating the development, deployment and management of AI, so that enterprises can derive real business value from their AI initiatives. Deploying and managing deep learning models in production carries its own set of complexities. In this talk, we will discuss real-life examples from customers that have built MLOps pipelines for deep learning use cases. For example, predicting rainfall from CCTV footage to prevent flooding.
Nov 30, 2021   |  By Iguazio
MLOps holds the key to accelerating the development and deployment of AI, so that enterprises can derive real business value from their AI initiatives. From the first model deployed to scaling data science across the organization. The foundation you set will enable your team to build and monitor a growing amount of AI applications in production. In this talk, we will share best practices from our experience with enterprise customers who have effectively built and deployed composite machine and deep learning pipelines.
Nov 29, 2021   |  By Iguazio
Given the growing number of AI projects and the complexities associated with bringing these projects to production, and specifically the challenges associated with feature engineering, the industry needs a way to standardize and automate the core of feature engineering. Feature stores provide enterprises with a competitive edge, as they enable them to expedite and simplify the path from lab to production. They enable sharing and re-use of features across teams and projects to save time and effort and ensure consistency across training and inference.
Nov 28, 2021   |  By Iguazio
The data science team at IHS Markit has been hard at work building sophisticated NLP pipelines that work at scale using the Iguazio MLOps platform and open-source MLRun framework. Today they will share their journey and provide advice for other data science teams looking to: Nick (IHS Markit) and Yaron (Iguazio) will share their approach to automating the NLP pipeline end to end. They’ll also provide details on leveraging capabilities such as Spot integration and Serving Graphs to reduce costs and improve the data science process.

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.