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Best Practices for Succeeding with MLOps ft. Noah Gift - MLOps Live 18

As the MLOps practice matures, there is an accumulation of stories about what works well – and what doesn’t. If you’re building up your enterprise MLOps muscle, instead of trial and error, why not tap into the collective memory of thousands of organizations who have spent the last couple of years building their MLOps practices internally and learn from their experience?

From AutoML to AutoMLOps: Automated Logging & Tracking of ML - MLOps Live #19

In this session of the MLOps Live Webinar series, we discuss building services with ML baked-in, that continuously deliver bottom-line business value, by embracing AutoMLOps. AutoMLOps means automating engineering tasks so that your code is automatically ready for production. In this session, we outline the challenges, describe open-source tools available for Auto-MLOps, and finish off with a live demo.

Build an AI App in Under 20 Minutes

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.

MLOps World Toronto: MLOps Beyond Training Simplifying and Automating the Operational Pipeline

Most data science teams start with building AI models and only think about operationalization later. But taking a production-first approach and automating components is the key to generating measurable ROI for the business. In this talk, Iguazio’s co-founder and CTO, Yaron Haviv, explains how to simplify and automate your production pipeline to bring data science to production faster and more efficiently. He displays real live use cases while going through all the different steps in the process.

Top 27 Free Healthcare Datasets for Machine Learning

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.

Breaking AI Bottlenecks with NetApp + Iguazio + AWS FSx for NetApp ONTAP

Teams facing implementation challenges need a way to scale their operational pipelines, continuously roll out AI services faster, support real-time use cases and enable deployment in hybrid environments. The NetApp-AWS-Iguazio integrated FSx solution offers a one-stop-shop from storage to production, with full end-to-end MLOps capabilities—even at scale and in real-time.

Machine Learning Experiment Tracking from Zero to Hero in 2 Lines of Code

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.

Iguazio Recognized in Gartner's 2022 Market Guide for DSML Engineering Platforms

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.

The Easiest Way to Track Data Science Experiments with MLRun

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.

Best Practices for Succeeding with MLOps

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.