Who Should Attend this Train and Deploy a Machine Learning Model with Azure Machine Learning (DP-3007)?
This Train and Deploy a Machine Learning Model with Azure Machine Learning (DP-3007) course is ideal for professionals seeking to leverage Azure’s capabilities to enhance machine learning model development and deployment within their organisations. However, this training will be particularly beneficial for:
- Data Scientists
- Machine Learning Engineers
- Cloud Solutions Architects
- IT Managers
- Chief Technology Officers (CTO)
- Heads of Data Analytics
- Software Developers
- Project Managers
Prerequisites of the Train and deploy a machine learning model with Azure Machine Learning (DP-3007)
Delegate should possess a basic understanding of cloud computing, Microsoft Azure services, and Python programming. They also need familiarity with data science, machine learning concepts, and access to a Microsoft Azure account.
Train and deploy a machine learning model with Azure Machine Learning (DP-3007) Course Overview
Train and Deploy a Machine Learning Model with Azure Machine Learning (DP-3007) is a targeted training program designed to provide delegate with the necessary skills to effectively use Azure Machine Learning for creating, tracking, and deploying machine learning models. Through this training, organisations can expect significant enhancements in their ability to develop scalable machine learning solutions that drive innovation and operational efficiencies. For individuals, it offers comprehensive insights into the lifecycle of machine learning models, boosting their analytical capabilities and technical acumen.
During this training course, delegate will learn to navigate the complexities of Azure Machine Learning, from initial data handling and compute management to advanced model deployment. The course covers essential techniques such as setting up data stores, managing compute targets, and utilising Azure's environment for machine learning projects. This hands-on approach ensures mastery of each concept, facilitated by our experienced trainers who bring real-world expertise to the learning environment.
Course Objectives
- To understand and utilise URIs in Azure
- To create and manage Azure datastores
- To configure and deploy compute clusters
- To execute and monitor command jobs
- To track and log model metrics with MLflow
- To register and manage MLflow models
- To deploy models to managed online endpoints
After attending this training, delegates will be equipped with the skills necessary to independently manage and navigate the Azure Machine Learning platform. They will be able to set up data assets, manage compute resources effectively, and utilise various environments for machine learning projects. Furthermore, participants will gain the capability to track the performance of machine learning models, utilise MLflow for comprehensive model management, and deploy trained models to production using Azure's managed online endpoints.