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    Azure ML: A Practical Introduction Into MLOps Services

    Duration: 60 minutes Host: Maksym Stec, AI and Machine Learning Engineer, AI Engineering Team
    Wiktor Hawrylik, AI and ML Senior Engineer, DS & AI Competency Center     This session is offered as a scheduled group session with our expert.
    Ask for available time slots
    Azure ML_ A practical introduction into MLOps services-1 (1)
    Azure ML_ A practical introduction into MLOps services-2

    What To Expect

    In this practical and interactive one-hour workshop, we’ll share our knowledge and real project experience on how to use Azure Machine Learning (AML) to effectively manage and optimize machine learning projects. This workshop aims to help data scientists, developers, and analysts in using Azure ML and adopting MLOps best practices and industry standards. We touch on the following topics: how to store and process data assets, manage compute, Azure ML python SDK and CLI, interactive modeling, orchestration and monitoring, pipelines, model registry and endpoints deployment and RAI dashboards.

    MLOps at a Glance

    Challenges in Adopting MLOps

    Azure Machine Learning Platform

    MLOps Use-case Walkthrough

    Review the fundamentals of MLOps and how it can benefit enterprises in their machine learning projects. 

    Understand the key challenges of adopting MLOps and using Azure ML to build, deploy, operationalize, and manage projects.

    Discover Azure ML functionalities and delve into topics like data management, environments, models, and endpoints.

    See real-life examples, scenarios, and projects that properly use the Azure ML platform and adopt MLOps.

    MLOps at a Glance

    Challenges in Adopting MLOps

    Review the fundamentals of MLOps and how it can benefit enterprises in their machine learning projects. 

    Understand the key challenges of adopting MLOps and using Azure ML to build, deploy, operationalize, and manage projects.

    Azure Machine Learning Platform

    MLOps Use-case Walkthrough

    Discover Azure ML functionalities and delve into topics like data management, environments, models, and endpoints.

    See real-life examples, scenarios, and projects that properly use the Azure ML platform and adopt MLOps.

    MLOps at a Glance

    Review the fundamentals of MLOps and how it can benefit enterprises in their machine learning projects. 

    Challenges in Adopting MLOps

    Understand the key challenges of adopting MLOps and using Azure ML to build, deploy, operationalize, and manage projects.

    Azure Machine Learning Platform

    Discover Azure ML functionalities and delve into topics like data management, environments, models, and endpoints.

    MLOps Use-case Walkthrough

    See real-life examples, scenarios, and projects that properly use the Azure ML platform and adopt MLOps.

    Madejski

    About our speaker

    Wiktor Hawrylik not only excels at complex Data Science and DevOps, but he also applies detailed algorithmic approaches to solving problems, handling performance-intensive and time-critical tasks and transforming regulatory requirements into functioning code. He matches his expertise with an unwavering dedication to solving tasks, which gave him a reputation for always leading teams to successful machine learning solutions deliveries.
    Madejski
    maksym

    About our speaker

    Maksym Stec is an engineer to the core. He successfully applies earned software development practices to Data Engineering and MLops area. On a daily basis he connects the world of modeling, data management, and cloud operations to deliver end-to-end solutions. Defining features of his work are attention to detail and focus on deep understanding, utilized not only during development but also while sharing his knowledge with others.

    maksym

    Ask for available time slots

    Fill in and submit this form to signify your interest. Within two days of registering, a representative will reach out to you to discuss your team’s knowledge needs and provide available time slots for you to choose from.
     

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