726 Computing & Software Development Tools courses delivered Online

Duration 4 Days 24 CPD hours This course is intended for This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. Overview Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow. Prerequisites Creating cloud resources in Microsoft Azure. Using Python to explore and visualize data. Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow. Working with containers AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience. 1 - Design a data ingestion strategy for machine learning projects Identify your data source and format Choose how to serve data to machine learning workflows Design a data ingestion solution 2 - Design a machine learning model training solution Identify machine learning tasks Choose a service to train a machine learning model Decide between compute options 3 - Design a model deployment solution Understand how model will be consumed Decide on real-time or batch deployment 4 - Design a machine learning operations solution Explore an MLOps architecture Design for monitoring Design for retraining 5 - Explore Azure Machine Learning workspace resources and assets Create an Azure Machine Learning workspace Identify Azure Machine Learning resources Identify Azure Machine Learning assets Train models in the workspace 6 - Explore developer tools for workspace interaction Explore the studio Explore the Python SDK Explore the CLI 7 - Make data available in Azure Machine Learning Understand URIs Create a datastore Create a data asset 8 - Work with compute targets in Azure Machine Learning Choose the appropriate compute target Create and use a compute instance Create and use a compute cluster 9 - Work with environments in Azure Machine Learning Understand environments Explore and use curated environments Create and use custom environments 10 - Find the best classification model with Automated Machine Learning Preprocess data and configure featurization Run an Automated Machine Learning experiment Evaluate and compare models 11 - Track model training in Jupyter notebooks with MLflow Configure MLflow for model tracking in notebooks Train and track models in notebooks 12 - Run a training script as a command job in Azure Machine Learning Convert a notebook to a script Run a script as a command job Use parameters in a command job 13 - Track model training with MLflow in jobs Track metrics with MLflow View metrics and evaluate models 14 - Perform hyperparameter tuning with Azure Machine Learning Define a search space Configure a sampling method Configure early termination Use a sweep job for hyperparameter tuning 15 - Run pipelines in Azure Machine Learning Create components Create a pipeline Run a pipeline job 16 - Register an MLflow model in Azure Machine Learning Log models with MLflow Understand the MLflow model format Register an MLflow model 17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning Understand Responsible AI Create the Responsible AI dashboard Evaluate the Responsible AI dashboard 18 - Deploy a model to a managed online endpoint Explore managed online endpoints Deploy your MLflow model to a managed online endpoint Deploy a model to a managed online endpoint Test managed online endpoints 19 - Deploy a model to a batch endpoint Understand and create batch endpoints Deploy your MLflow model to a batch endpoint Deploy a custom model to a batch endpoint Invoke and troubleshoot batch endpoints

Duration 3 Days 18 CPD hours This course is intended for Senior Consultants (both functional and technical) that aspire to be Solution Architects, or current Solution Architects that are new to the role. The Solution Architect is responsible for the successful design, implementation, deployment and adoption of an overall solution. The Solution Architect ensures that the solution meets the customer?s needs now and in the future. In this course, students will learn about decisions a Solution Architect makes during an implementation, covering security, integrations, Power Apps architecture, Power Automate architecture, and more. This course is designed to give you an introduction to the Solution Architect role. Becoming a Solution Architect/Getting to know your customer Define a Solution Architect Role of a Solution Architect on projects Project Methodology Getting to know your customer Group exercise - Getting to know your customer Conceptualizing the design from requirements How to lead the requirement collection effort Using fit gap analysis Pillars of good architecture Blueprinting the solution architecture Group exercise - Design from requirements Project governance and working as a team Solution Architect's role in project governance Techniques for keeping a project on track Scenarios that could cause a project to fail Group exercise - Project governance and working as a team Power Platform Architecture Key Power Platform architecture components Understand how platform design and limits influence solution architectures Updates and feature releases Understand how to communicate how the platform meets customer needs Data Modeling Data model influences Data model strategy Data types Data relationships Group exercise - Data modeling Analytics and artificial intelligence Planning and evaluating requirements Operational reporting Power BI Enterprise BI Pre-built insights and custom AI Power Apps Architecture Discuss options for apps and how to choose where to start Discuss app composition options Using components as part of your app architecture Considerations for including Portals as an app in your architecture Group exercise - Power Apps Architecture topics Application Lifecycle Management (ALM) Microsoft vision and Solution Architect's role in ALM Environment strategies Defning a solution structure for your deliverable Power Automate Architecture Discuss options for automation and custom logic Review considerations for using triggers and common actions Explore using Business Process Flows (BPF) to guide users through business processes Group Exercise - Evaluate scenarios for Power Automate usage Security Modeling Solution Architect's role in security modeling Discovery and learning your client's environment Controlling access to environments and resources Controlling access to CDS Data Group Exercise - Security Modeling Integration Solution Architects role in Integrations What is an integration and why do we need it Platform features that enable integration CDS Event Publishing Scenarios for group discussion Dynamics 365 Applications Architecture Solution Architect's role when deploying Dynamics 365 apps Architecture Considerations for primary apps Group Exercise - App specific working groups evaluate requirements Power Virtual Agents architecture Introduction Chatbot options Chatbot concepts Best practices Integrate chatbots Power Virtual Agents in Microsoft Teams Robotic Process Automation Introduction Power Automate Desktop Recording and editing tasks Running desktop flows Process advisor Testing and Go Live Solution Architect's role with testing and go live Planning for testing Planning for go live

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The comprehensive Docker Training for .Net and Angular Developers has been designed by industry experts to provide learners with everything they need to enhance their skills and knowledge in their chosen area of study. Enrol on the Docker Training for .Net and Angular Developers today, and learn from the very best the industry has to offer! This best selling Docker Training for .Net and Angular Developers has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Docker Training for .Net and Angular Developers is suitable for anyone who wants to build their professional skill set and improve their expert knowledge. The Docker Training for .Net and Angular Developers is CPD-accredited, so you can be confident you're completing a quality training course which will boost your CV and enhance your career potential. The Docker Training for .Net and Angular Developers is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn. After successfully completing the Docker Training for .Net and Angular Developers , you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the Docker Training for .Net and Angular Developers will help you stand out from the crowd. You can also validate your certification on our website. We know that you are busy and that time is precious, so we have designed the Docker Training for .Net and Angular Developers to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device.  Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.

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