Machine Learning REPA (RU)

Школа Risoma - курсы по автоматизации и инженерным практикам в машинном обучении

Learn Git for code versioning, efficiently collaborating on projects, and ensuring reproducibility in ML and AI projects.

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In this code tutorial, you will learn how to run batch ML model inference, collect data and ML model quality monitoring metrics, and visualize them on a live dashboard.

Join the Machine Learning REPA community to learn and spread good practices in MLOps, Engineering, Reproducibility, and Automation! LinkedIn:

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Together with Alex Kim, Solutions and Sales Engineer at Iterative.ai we discuss how DVC helps in Image Segmentation projects! Talk details: Image segmentation plays a crucial role in computer vision projects, enabling accurate object recognition and scene comprehension. However, managing vast data volumes and complex machine learning pipelines can be challenging. This talk will demonstrate how DVC simplifies data versioning and ML pipeline management for image segmentation tasks. We will discuss the benefits of versioning systems like DVC. We'll highlight DVC's compatibility with popular tools like Git and various machine learning frameworks, presenting a case study to showcase the practical application of DVC in a computer vision project. Join us to explore how DVC streamlines collaboration, minimizes data redundancy, and enhances reproducibility, leading to improved project outcomes and faster, more accurate image segmentation in computer vision projects. Speaker: Alex Kim, Solutions and Sales Engineer at Iterative.ai As an experienced Solutions and Sales Engineer at Iterative.ai, Alex Kim has a strong background in software engineering and machine learning. He is passionate about bridging the gap between technology and business to help companies achieve their goals. With his expertise in technical sales and online course development, Alex is a dynamic speaker who brings a wealth of knowledge to any conference. LinkedIn:

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Tags: #mlrepa #evidentlyai #streamlit #mlops #machine_learning #data_science #community #monitoring

Join the Machine Learning REPA community to learn and spread good practices in MLOps, Engineering, Reproducibility, and Automation! LinkedIn:

Events:

GitHub:

Twitter:

Together with Bradley Munday, Head of ML Engineering at Modzy we discuss MLOps on Edge devices! Talk details: Computer vision has the potential to transform many applications of today into solutions of the future - from smart cameras in traffic, to MRI image processing, to monitoring quality for manufacturers, the possibilities are endless. But how can you set up a deployment pipeline that allows you to run your computer vision models anywhere? In this talk, we'll walk through steps that help you build an automated deployment pipeline. First, we will automatically package a pre-trained model into a container using Chassis.ml. Then, using Modzy, we'll show you how to deploy and run the model on a single board computer edge device. At the end of the talk, you'll walk away with a better understanding of what it takes to build an automated deployment pipeline that enables you to serve and scale your ML models anywhere. Speaker: Bradley Munday, Head of ML Engineering at Modzy As Head of ML Engineering at Modzy, Brad is an experienced technologist and open-source contributor with strong experience in Data Science, ML engineering, technical sales, and customer engagement. In this role, he spends time helping organizations unlock value in their enterprise and edge AI investment through robust MLOps pipelines. Additionally, he is one of the primary contributors and maintainers to Modzy's open source project, Chassis.ml. Links: Slides:

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Tags: #mlrepa #mlops #machine_learning #data_science #community #monitoring #chassisml

Speaker: Bradley Munday, Head of ML Engineering at Modzy