Iguazio Receives Honorable Mention in Gartner MQ for Data Science and ML Platforms Second Year in a Row

MLOps Reimagined

The MLOps Challenge

According to industry reports, data science teams don’t do data science, they spend most of their time on data wrangling, data preparation, managing software packages and frameworks, configuring infrastructure, and integrating various components. Many organizations underestimate the amount of effort it takes to incorporate machine learning into production applications. This leads to entire projects being abandoning halfway (87% of data science projects never make it to production) or to the consumption of far more resources and time than first anticipated. MLOps addresses this challenge and as the name indicates, combines AI/ML practices with DevOps practices. Its goal is to create continuous development and delivery (CI/CD) of data and ML intensive applications.

MLOps Solution

The MLOps Solution

Iguazio simplifies the deployment of data science in production, by automating the processes associated with bringing AI applications in real-world environments.  Iguazio simplifies the process of deploying AI by enabling users to automate ML pipelines from data collection through preparation, training, testing and deployment.

This automates the manual procedure that many companies are using today, by leveraging Iguazio’s building blocks such as Kubeflow pipelines to run these ML steps automatically

Run automated end-to-end ML pipelines:

  • Prepare, train, test and deploy AI applications at scale
  • Monitor model drifting to ensure optimal accuracy levels
  • Eliminate the need to translate code – Use Python in production at scale
  • Benefit from an easy way to run different types of frameworks – Dask, Horovod, Spark, etc. without the need for devops, providing an abstraction layer to run containers at scale on a Kubernetes cluster
  • Track your ML / DL experiments with all the relevant metadata in once place
  • Leverage experiment reproducibility to cut dev time

Our ML pipeline approach is adopting the concept of serverless ML Functions. Serverless technologies allow you to write code and specification which automatically translate themselves to auto-scaling production workloads. Until recently, these were limited to stateless and event driver workloads, but now with the new open-source technologies(MLRun+Nuclio+KubeFlow), serverless functions can take on larger challenges of real-time, extreme scale data-analytics and machine learning. The steps of packaging, scaling, tuning, instrumentation, and continuous delivery are fully automated, addressing the two main challenges of every organization: time to market and resource management. ML Functions can easily be chained to produce ML pipelines (using Kubeflow).

MLOps Pipeline

MLOps Events

MLOps Live Webinar #14: Automating & Governing AI Over Production Data on Azure

MLOps Live Webinar #14: Automating & Governing AI Over Production Data on Azure

Monday, March 22 10AM PST
The MLOps Live Webinar Series is a complimentary webcast where you will learn how to manage and automate machine learning pipelines to bring your data science into real business applications.
Data Science Salon | Applying AI & ML to Healthcare, Finance & Technology

Data Science Salon | Applying AI & ML to Healthcare, Finance & Technology

The data science salon is a unique vertical focused conference which grew into a diverse community of senior data science, machine learning and other technical specialists.
MLOps Series Library

MLOps Series Library

Access all past MLOps Live webinars to learn all about MLOps from industry experts and thought leaders.