Iguazio Achieves AWS Outposts Ready Designation to Help Enterprises Accelerate AI Deployment
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.
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:
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).