Herzliya, Israel
Oct 13, 2020   |  By Yaron Haviv
Modern business applications leverage Machine Learning (ML) and Deep Learning (DL) models to analyze real-world and large-scale data, to predict or to react intelligently to events. Unlike data analysis for research purposes, models deployed in production are required to handle data at scale and often in real-time, and must provide accurate results and predictions for end-users.
Oct 13, 2020   |  By Amber Silvers
Data science has come a long way, and it has changed organizations across industries profoundly. In fact, over the last few years, data science has been applied not for the sake of gathering and analyzing data but to solve some of the most pertinent business problems afflicting commercial enterprises.
Oct 13, 2020   |  By Adi Hirschtein
We’re delighted to announce the release of the Iguazio Data Science Platform version 2.8. The new version takes another leap forward in solving the operational challenge of deploying machine and deep learning applications in real business environments. It provides a robust set of tools to streamline MLOps and a new set of features that address diverse MLOps challenges.
Oct 13, 2020   |  By Yaron Haviv
Tapping into more compute power is the next frontier of data science. Data scientists need it to complete increasingly complex machine learning (ML) and deep learning (DL) tasks without it taking forever. Otherwise, faced with a long wait for compute jobs to finish, data scientists give in to the temptation to test smaller datasets or run fewer iterations in order to produce results more quickly.
Oct 13, 2020   |  By Bill Bodei
Much has been written on the growth of machine learning and its impact on almost every industry. As businesses continue to evolve and digitally transform, it’s become an imperative for businesses to include AI and ML in their strategic plans in order to remain competitive. In Competing in the Age of AI, Harvard professors Marco Iansiti and Karim R. Lakhani illustrate how this can be confounding for CEOs, especially in the face of AI-powered competition.
Oct 13, 2020   |  By Adi Hirschtein
A feature store provides a single pane of glass for sharing all available features across the organization. When a data scientist starts a new project, he or she can go to this catalog and easily find the features they are looking for. But a feature store is not only a data layer; it is also a data transformation service enabling users to manipulate raw data and store it as features ready to be used by any machine learning model.
Sep 29, 2020   |  By Alex Joseph
As more and more companies are embedding AI projects into their systems, attracted by the promise of efficiencies and competitive advantages, data science teams are feeling the growing pains of a relatively immature practice without widespread established and repeatable norms.
Sep 15, 2020   |  By Yaron Haviv
A Forbes survey shows that data scientists spend 19% of their time collecting data sets and 60% of their time cleaning and organizing data. All told, data scientists spend around 80% of their time on preparing and managing data for analysis. One of the greatest obstacles that make it so difficult to bring data science initiatives to life is the lack of robust data management tools.
Aug 31, 2020   |  By Yaron Haviv
Data scientists today have to choose between a massive toolbox where every item has its pros and cons. We love the simplicity of Python tools like pandas and Scikit-learn, the operation-readiness of Kubernetes, and the scalability of Spark and Hadoop, so we just use all of them. What happens? Data scientists explore data using pandas, then data engineers use Spark to recode the same logic to scale or with live streams or operational databases.
Aug 10, 2020   |  By Sahar Dolev-Blitental
We are delighted to announce that Iguazio has been named a sample vendor in the 2020 Gartner Hype Cycle for Data Science and Machine Learning, as well as four additional Gartner Hype Cycles for Infrastructure Strategies, Compute Infrastructure, Hybrid Infrastructure Services, and Analytics and Business Intelligence, among industry leaders such as DataRobot, Amazon Web Services, Google Cloud Platform, IBM and Microsoft Azure (some of whom are also close partners of ours).
Sep 1, 2020   |  By Iguazio
We discuss constructing ML pipelines across federated data and computing environments, as well as building production-ready AI applications that work in hybrid environments.
Jul 29, 2020   |  By Iguazio
Michael Leznik - Head of Data Science Matthieu Glotz - Data Scientist Yaron Haviv - CTO & Co-Founder We discuss how technology and new work processes can help the gaming and mobile app industries predict and mitigate 1st-day (or D0) user churn in real time — down to minutes and seconds using modern streaming data architectures such as KAPPA. Also, we explore feature engineering improvements to the RFM (Recency, Frequency, and Monetary) churn prediction framework: The Discrete Wavelet Transform (DWT).
Jun 30, 2020   |  By Iguazio
Building scalable #AI applications that generate value in real business environments require not just advanced technologies, but also better processes for #datascience, #engineering and #devops teams to collaborate effectively. We will be deep diving into this topic on our next #MLOpsLive webinar with: Greg Hayes, Data Science Director at Ecolab and Yaron Haviv, our Co-Founder and CTO.
Jun 23, 2020   |  By Iguazio
MLRun is a generic and convenient mechanism for #data scientists and software developers to build, run, and monitor #machinelearning (ML) tasks and pipelines on a scalable cluster while automatically tracking executed code, metadata, inputs, and outputs. On-Premise or Barebone/Metal - including Edge AI / Analytics Customers include NetApp, Quadient, Payoneer (and many more).
Jun 22, 2020   |  By Iguazio
MLOps Live #5 - With NetApp - How to Build a Predictive Maintenance Solution at Scale
May 31, 2020   |  By Iguazio
The session — featuring Ganesh Nagarathnam, Director Analytics & ML Engineering at S & P Global Market Intelligence, and Yaron Haviv, Co-Founder and CTO at Iguazio — goes beyond theory, with industry leaders sharing challenges and practical solutions that involve running Al experiments at scale, versioning, delivery to production, reproducibility and data access.
May 31, 2020   |  By Iguazio
The session — featuring Jason Evans, Director of DXP Innovation at Quadient, and Yaron Haviv, Co-Founder and CTO at Iguazio — goes beyond theory, with industry leaders sharing challenges and practical solutions that involve running AI experiments at scale, versioning, delivery to production, reproducibility and data access.
May 31, 2020   |  By Iguazio
The session — featuring David Aronchick, Head of OSS ML Strategy at Microsoft; Marvin Buss, Azure Customer Engineer at Microsoft; Zander Matheson, Senior Data Scientist at GitHub; and Yaron Haviv, Co-Founder and CTO at Iguazio — goes beyond theory, with industry leaders sharing challenges and practical solutions that involve running AI experiments at scale, versioning, delivery to production, reproducibility, and data access.
May 31, 2020   |  By Iguazio
MLOps Live #4 - How to Detect & Remediate Drift in Production with MLOps Automation.

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.