14806 Ear courses

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

Hypnotherapy Training Course

Earned Value Management: On-Demand: On-Demand Earned Value Management (EVM) incorporates a set of proven practices appropriate for project or program management methodologies. These include integration of program scope, schedule, and cost objectives, establishment of a baseline plan for accomplishment of program objectives and use of earned value techniques for performance measurement during the execution of a program. Earned Value Management (EVM) incorporates a set of proven practices appropriate for project or program management methodologies. These include integration of program scope, schedule, and cost objectives, establishment of a baseline plan for accomplishment of program objectives and use of earned value techniques for performance measurement during the execution of a program. EVM provides a solid platform for risk identification, corrective actions, and management re-planning as may be required over the life of a project or program. The course emphasis is on the latest EVM principles and concepts in accordance with changes and guidelines for Earned Value Management in The Guide to the Project Management Body of Knowledge (PMBOK® Guide) and The Practice Standard for Earned Value Management published by the Project Management Institute. What you Will Learn You'll learn how to: Develop a project baseline, using an effective WBS Record actual project performance Calculate EVM measures Evaluate project performance based on EVM measures Respond to project variances Integrate EVM and risk management Determine how EVM will add value to your organization Develop an EVM implementation plan for your organization Getting Started Introductions Course structure Course goals and objectives Expectations Foundation Concepts Introduction to Earned Value Management (EVM) Benefits of EVM EVM Process Overview Applications of EVM Creating a Work Breakdown Structure Reviewing WBS concepts Reviewing WBS development process (decomposition) Using a WBS to support EVM Building a Project Baseline Defining a project baseline Developing a project baseline Using a project baseline Recording Actuals Recording actuals overview Collecting data for actual project performance Determining earned value - various methods EVM Performance Measures Using current status measures Using forecasting measures Analyzing EVM measures EVM and Risk Management Integrating EVM and Risk Management Using EVM measures in the risk register Exploring how EVM can facilitate reserves management Drawing down contingency reserves Responding to Variances Introduction to variances Process for responding to variances Response options Reporting Project Performance EVM reporting overview Meeting EVM reporting needs Addressing EVM reporting challenges Implementing an EVMS Defining EVMS requirements EVM for Agile projects Tailoring the EVMS Summary and Next Steps Review of content Review of objectives / expectations Personal action plan

Earned Value Management: On-Demand Earned Value Management (EVM) incorporates a set of proven practices appropriate for project or program management methodologies. These include integration of program scope, schedule, and cost objectives, establishment of a baseline plan for accomplishment of program objectives and use of earned value techniques for performance measurement during the execution of a program. EVM provides a solid platform for risk identification, corrective actions, and management re-planning as may be required over the life of a project or program. The course emphasis is on the latest EVM principles and concepts in accordance with changes and guidelines for Earned Value Management in PMI's A Guide to the Project Management Body of Knowledge (PMBOK® Guide) and The Practice Standard for Earned Value Management. What You Will Learn You'll learn how to: Develop a project baseline, using an effective WBS Record actual project performance Calculate EVM measures Evaluate project performance based on EVM measures Respond to project variances Integrate EVM and risk management Determine how EVM will add value to your organization Develop an EVM implementation plan for your organization Getting Started Foundation Concepts Creating a Work Breakdown Structure Building a Project Baseline Recording Actuals EVM Performance Measures EVM and Risk Management Responding to Variances Reporting Project Performance Implementing an EVMS Summary and Next Steps

Certified Scrum Professional®-ScrumMaster® (CSP®-SM): In-House Training Certified Scrum Professionals challenge their teams to improve the way Scrum and Agile principles are applied. They have demonstrated experience, documented training, and proven knowledge in Scrum. Are you ready to take your knowledge and skillset in your role as Scrum Master to the next level? If so, it's time to elevate your career further by earning the Certified Scrum Professional®-ScrumMaster (CSP®-SM) certification. What you will Learn Learn to find practical solutions and improve your implementation of Scrum in the workplace. Aside from the pride gained and earning potential of attaining CSP® level, you can also: Attend exclusive CSP® events with other leaders in Scrum and Agile Attract more recruiters and command a higher rate of pay Establish a gateway and milestone toward becoming CST®, CEC, or CTC Receive a free premium subscription to the world's largest Agile assessment and continuous improvement platform, Comparative Agility®

The course covers research design principles and all main quantitative evaluation methods: randomised experiments, instrumental variables, sharp and fuzzy regression discontinuity designs, regression methods, matching methods and longitudinal methods (before-after, difference-in-differences and synthetic controls).

The course covers research design principles and all main quantitative evaluation methods: randomised experiments, instrumental variables, sharp and fuzzy regression discontinuity designs, regression methods, matching methods and longitudinal methods (before-after, difference-in-differences and synthetic controls).

The course covers research design principles and all main quantitative evaluation methods: randomised experiments, instrumental variables, sharp and fuzzy regression discontinuity designs, regression methods, matching methods and longitudinal methods (before-after, difference-in-differences and synthetic controls).

The course covers research design principles and all main quantitative evaluation methods: randomised experiments, instrumental variables, sharp and fuzzy regression discontinuity designs, regression methods, matching methods and longitudinal methods (before-after, difference-in-differences and synthetic controls).

The course covers research design principles and all main quantitative evaluation methods: randomised experiments, instrumental variables, sharp and fuzzy regression discontinuity designs, regression methods, matching methods and longitudinal methods (before-after, difference-in-differences and synthetic controls).