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Enhance your energy industry knowledge with EnergyEdge's course on gas and LNG contracts, markets, and strategy. Enroll now to optimize value for your organization today!

About this training Decarbonization of the Upstream Oil & Gas sector has previously been based on inter-fuel competition. Market actions were seen as the most effective method for reducing the level of emissions. However, the pace of decarbonization is now being led by government policy acting in concert with a coalition of stakeholders such as investors and consumers. The primary focus of this pathway is on the management of carbon emissions to both mitigate and adapt to climate change. Some energy analysts have forecast that global production of oil and natural gas will have to decline annually by 4 to 6 percent in order to meet the global target of Net Zero Emissions by 2050. Oil and gas producers face a difficult challenge in deciding upon the strategy and measures that will best achieve targets set for them while maintaining supply, attracting investments and accessing markets. This 2-day training course will provide participants with an understanding of the strategies and measures for decarbonizing the Upstream Oil and Gas sector within the framework of measures implemented by individual governments through their respective commitments to reduce emissions to achieve their National Determined Contribution under the Paris Agreement. This course offers a unique opportunity to understand the rapidly increasing issues confronting the industry as well as the options for the management of carbon emissions to comply with corporate as well as national policies and the implementation of measures for controlling, reporting and verification. Training Objectives Upon completion of this course, participants will be better equipped to participate in the implementation of measures for the management of carbon emissions in the following areas: Implementation of measures for reducing carbon emissions Establishing systems for monitoring and reporting carbon emissions Evaluating the commerciality of discoveries Reviewing and strategizing future field development plans Meeting Environmental Obligations Target Audience This course has been specifically designed for professionals involved in the international oil and gas industry, whether employed a field operator, national oil company, or government. It offers a unique opportunity to rapidly increase your understanding of the issues confronting the industry as well as the options for the management of carbon emissions to comply with corporate as well as national policies and the implementation of measures for controlling, reporting and verification. Staff with the following roles will find this course particularly useful: Corporate Planners Project Engineers Financial Analysts Environmental Specialists Legal Advisors Regulatory & Compliance Officers Course Level Basic or Foundation Trainer Your expert course leader is an international legal expert in petroleum law who has been listed in the Guide to the World's Leading Energy and Natural Resources Lawyers. In his thirty years of practice, he has been the lead negotiator and acquisitions advisor for oil and gas companies in the US and the Asia-Pacific. These transactions have included both upstream (licences and leases) and downstream (refineries and pipelines) assets. He has been appointed as Distinguished Visiting Professor in Oil and Gas at the University of Wyoming and Honorary Professor at the Centre for Energy, Petroleum & Mineral Law & Policy (CEPMLP) at Dundee University. POST TRAINING COACHING SUPPORT (OPTIONAL) To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster. Request for further information post training support and fees applicable Accreditions And Affliations

SAFe® Agile Software Engineering: In-House Training The introduction of Lean-Agile and DevOps principles and practices into software engineering has sparked new skills and approaches that help organizations deliver higher-quality, software-centric solutions faster and more predictably. This workshop-oriented course explores foundational principles and practices and how continuous flow of value delivery and built-in quality are enabled by XP technical practices, Behavioral-Driven Development (BDD), and Test-Driven Development (TDD). Attendees will learn proven practices to detail, model, design, implement, verify, and validate stories in the SAFe® Continuous Delivery Pipeline, as well as the practices that build quality into code and designs. Attendees will also explore how software engineering fits into the larger solution context and understand their role in collaborating on intentional architecture and DevOps. What you will Learn To perform the role of a SAFe® Agile Software Engineer, you should be able to: Define Agile Software Engineering and the underlying values, principles, and practices Apply the Test-First principle to create alignment between tests and requirements Create shared understanding with Behavior-Driven Development (BDD) Communicate with Agile modeling Design from context for testability Build applications with code and design quality Utilize the test infrastructure for automated testing Collaborate on intentional architecture and emergent design Apply Lean-Agile principles to optimize the flow of value Create an Agile Software Engineering plan Introduction to Agile Software Engineering Connecting Principles and Practices to Built-in Quality Accelerating Flow Applying Intentional Architecture Thinking Test-First Discovering Story Details Creating a Shared Understanding with Behavior-Driven Development (BDD) Communicating with Models Building Systems with Code Quality Building Systems with Design Quality Implementing with Quality

SAFe® Agile Software Engineering The introduction of Lean-Agile and DevOps principles and practices into software engineering has sparked new skills and approaches that help organizations deliver higher-quality, software-centric solutions faster and more predictably. This workshop-oriented course explores foundational principles and practices and how continuous flow of value delivery and built-in quality are enabled by XP technical practices, Behavioral-Driven Development (BDD), and Test-Driven Development (TDD). Attendees will learn proven practices to detail, model, design, implement, verify, and validate stories in the SAFe® Continuous Delivery Pipeline, as well as the practices that build quality into code and designs. Attendees will also explore how software engineering fits into the larger solution context and understand their role in collaborating on intentional architecture and DevOps. What you will Learn To perform the role of a SAFe® Agile Software Engineer, you should be able to: Define Agile Software Engineering and the underlying values, principles, and practices Apply the Test-First principle to create alignment between tests and requirements Create shared understanding with Behavior-Driven Development (BDD) Communicate with Agile modeling Design from context for testability Build applications with code and design quality Utilize the test infrastructure for automated testing Collaborate on intentional architecture and emergent design Apply Lean-Agile principles to optimize the flow of value Create an Agile Software Engineering plan Introduction to Agile Software Engineering Connecting Principles and Practices to Built-in Quality Accelerating Flow Applying Intentional Architecture Thinking Test-First Discovering Story Details Creating a Shared Understanding with Behavior-Driven Development (BDD) Communicating with Models Building Systems with Code Quality Building Systems with Design Quality Implementing with Quality

Dive deep into LNG loading operations with EnergyEdge's master certification course. Gain valuable skills and knowledge. Enroll now!

Dive deep into port pricing and tariff charging for oil and gas terminals with EnergyEdge's immersive classroom training. Enroll now to excel in your field!

Enhance your knowledge with EnergyEdge's course on Floating Liquefied Natural Gas (FLNG) design technology. Sign up now!

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

Take your knowledge to the next level with EnergyEdge course on Layer of Protection Analysis (LOPA). Enroll now to advance your career.