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
ISO 37001 Lead Implementer training enables you to develop the necessary expertise to support an organization in establishing, implementing, managing and maintaining an Anti-bribery Management System (ABMS) based on ISO 37001 About This Course During this training course, you will also gain a thorough understanding of the global anti-corruption best practices of Anti-bribery Management Systems and effectively prevent, detect and address bribery before it negatively impacts an organization's reputation and profits. After mastering all the necessary concepts of Anti-bribery Management Systems, you can sit for the exam and gain "Certified ISO 37001 Lead Implementer' credential. By holding this Certificate, you will be able to demonstrate that you have the practical knowledge and professional capabilities to implement ISO 37001 in an organization. Learning objectives Acknowledge the correlation between ISO 37001 and other standards and regulatory frameworks Master the concepts, approaches, methods and techniques used for the implementation and effective management of an ABMS Learn how to interpret the ISO 37001 requirements in the specific context of an organization Learn how to support an organization to effectively plan, implement, manage, monitor and maintain an ABMS Acquire the expertise to advise an organization in implementing Anti-bribery Management System best practices Educational approach This training is based on both theory and best practices used in the implementation of an ABMS Lecture sessions are illustrated with examples based on case studies Practical exercises are based on a case study which includes role playing and discussions Practice tests are similar to the Certification Exam Prerequisites A foundational understanding of ISO 37001 and knowledge of implementation principles. What's Included? Refreshments & Lunch (Classroom only) Course Slide Deck Official Study Guides CPD Certificate The Exam Accreditation Assessment Delegates sit a combined exam, consisting of in-course quizzes and exercises, as well as a final 12 question, essay type exam on Day 4 of the course. The overall passing score is 70%, to be achieved within the 150 minute time allowance. Exam results are provided within 24 hours, with both a Certificate and a digital badge provided as proof of success. Provided by This course is Accredited by NACS and Administered by the IECB. Who Should Attend? Professionals looking to implement and maintain an Anti-Bribery Management System Compliance officers
Root Cause Analysis (RCA) is used to analyse the root causes of focus events with both positive and negative outcomes, but it is most commonly used for the analysis of failures and incidents. Causes for such events can be varied in nature, including design processes and techniques, organizational characteristics, human aspects and external events. RCA can be used for investigating the causes of non-conformances in quality (and other) management systems as well as for failure analysis, for example in maintenance or equipment testing.
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).
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