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The role of an MLRO is one of high responsibility, with financial and personal consequences if something goes wrong. As an MLRO or Deputy MLRO, you need the confidence and practical skills to tackle the unique challenges these roles present.  Part of the ICA Practitioner Series, this unique certification is written by MLROs for MLROs and provides a practical framework and toolkit that gives you the knowledge and skills to: navigate the complex role of a Money Laundering Reporting Officer mitigate risks make changes and set goals challenge stakeholders make informed decisions and demonstrate the rationale manage relationships with boards and regulators effectively. This qualification covers the following topics: Interview questions and wider due diligence before taking up an MLRO / Deputy MLRO position Governance - senior management responsibilities - prescribed responsibilities Management information & report writing Risk assessment and risk appetite Policies, procedures & operational implementation Staff, training, leading and influencing/psychological models to develop interpersonal skills Quality of oversight - obtaining trusted data First 100 days - main risks and expectations Form A - FCA Registration, fit & proper interview Building Trust - stakeholder management, committees, management information, regulatory interaction, the MLRO Report Problem shooting and escalation pathways

This course will show you how to build Python-based web applications using Flask. You will cover the basics of the Flask framework and learn how to add functionality to your Flask applications using the popular extensions.

For over 30 years, the National Academy of Sports Medicine has developed the most successful personal trainers in the fitness industry. The NCCA-accredited personal trainer certification provides you with the most scientifically accurate information, all delivered in a revolutionary digital learning platform. If you want to become one of the best, most respected, and highest-paid fitness trainers, it’s as simple as getting certified online with NASM.

About this Virtual Instructor Led Training (VILT) Hydrogen will play an increasingly critical role in the future of energy system as it moves forward to supplement and potentially replace fossil fuels in the long run. Offshore wind offers a clean and sustainable renewable resource for green hydrogen production. However, it can also be volatile and presents inherent risks that need to be managed. Even though offshore production of hydrogen has yet to achieve a high state of maturity, many current projects are already dealing with the conditions and effects of offshore production of hydrogen and are grappling with the technological requirements and necessary gas transportation with grid integration. This 2 half-day Virtual Instructor Lead Training (VILT) course will examine the technological options for on-site production of hydrogen by electrolysis (onshore or offshore directly at the platform) as well as the transport of hydrogen (pipeline or ship). This VILT course will also explore the economic considerations and the outlook on future market opportunities. There will be exercises for the participants to work on over the two half-days. This course is delivered in partnership with Fraunhofer IEE. Training Objectives By the end of this VILT course, participants will be able to: Understand the technological attributes and options for green hydrogen production based on electricity from offshore wind. Explore the associated economic analysis for offshore wind hydrogen production, including CAPEX, OPEX, LCOE and LCOH Identify the critical infrastructure and technical configuration required for offshore green hydrogen including transportation networks and grid connectivity Learn from recent findings from current Research & Development projects concerning the differences between onshore and offshore hydrogen production. Target Audience This VILT course is intended: Renewable energy developers and operators Offshore oil & gas operators Energy transport and marine operators Energy policy makers and regulators IPPs and power utilities Training Methods The VILT course will be delivered online in 2 half-day sessions comprising 4 hours per day, including time for lectures, discussion, quizzes and short classroom exercises. Course Duration: 2 half-day sessions, 4 hours per session (8 hours in total). Trainer Trainer 1: Your expert course leader is Director of Energy Process Technology Division at the Fraunhofer Institute for Energy Economics and Energy System Technology, IEE. The research activities of the division link the areas of energy conversion processes and control engineering. The application fields covered are renewable energy technologies, energy storage systems and power to gas with a strong focus on green hydrogen. From 2006 - 2007, he worked as a research analyst of the German Advisory Council on Global Change, WBGU, Berlin. He has extensive training experience from Bachelor and Master courses at different universities as well as in the context of international training activities - recently on hydrogen and PtX for partners in the MENA region and South America. He holds a University degree (Diploma) in Physics, University of Karlsruhe (KIT). Trainer 2: Your expert course leader is Deputy Head of Energy Storage Department at Fraunhofer IEE. Prior to this, he was the director of the Grid Integration Department at SMA Solar Technology AG, one of the world's largest manufacturers of PV power converters. Before joining SMA, he was manager of the Front Office System Planning at Amprion GmbH (formerly RWE TSO), one of the four German transmission system operators. He holds a Degree of Electrical Engineering from the University of Kassel, Germany. In 2003, he finished his Ph.D. (Dr.-Ing.) on the topic of wind power forecasting at the Institute of Solar Energy Supply Technology (now known as Fraunhofer IEE) in Kassel. In 2004, he started his career at RWE TSO with a main focus on wind power integration and congestion management. He is Chairman of the IEC SC 8A 'Grid Integration of Large-capacity Renewable Energy (RE) Generation' and has published several papers about grid integration of renewable energy source and forecasting systems on books, magazines, international conferences and workshops. Trainer 3: Your expert course leader is Deputy Director of the Energy Process Technology division and Head of the Renewable Gases and Bio Energy Department at Fraunhofer IEE. His work is mainly focused on the integration of renewable gases and bioenergy systems into the energy supply structures. He has been working in this field since more than 20 years. He is a university lecturer in national and international master courses. He is member of the scientific advisory council of the European Biogas Association, member of the steering committee of the Association for Technology and Structures in Agriculture, member of the International Advisory Committee (ISAC) of the European Biomass Conference and member of the scientific committees of national bioenergy conferences. He studied mechanical engineering at the University of Darmstadt, Germany. He received his Doctoral degree on the topic of aerothermodynamics of gas turbine combustion chambers. He started his career in renewable energies in 2001, with the topic of biogas fired micro gas turbines. Trainer 4: Your expert course leader has an M. Sc. and she joined Fraunhofer IEE in 2018. In the Division of Energy Process Technology, she is currently working as a Research Associate on various projects related to techno-economic analysis of international PtX projects and advises KfW Development Bank on PtX projects in North Africa. Her focus is on the calculation of electricity, hydrogen and derivative production costs (LCOE, LCOH, LCOA, etc) based on various methods of dynamic investment costing. She also supervises the development of models that simulate different PtX plant configurations to analyze the influence of different parameters on the cost of the final product, and to find the configuration that gives the lowest production cost. She received her Bachelor's degree in Industrial Engineering at the HAWK in Göttingen and her Master's degree in renewable energy and energy efficiency at the University of Kassel. 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 about post training coaching support and fees applicable for this. Accreditions And Affliations

ITIL® 4 Specialist: High Velocity IT: Virtual In-House Training The ITIL® 4 Specialist: High-Velocity IT module is part of the Managing Professional stream for ITIL® 4. Candidates need to pass the related certification exam for working towards the Managing Professional (MP) designation. This course is based on the ITIL® 4 Specialist: High-Velocity IT exam specifications from AXELOS. With the help of ITIL® 4 concepts and terminology, exercises, and examples included in the course, candidates acquire the relevant knowledge required to pass the certification exam. This module addresses the specifics of digital transformation and helps organizations to evolve towards a convergence of business and technology, or to establish a new digital organization. It was designed to enable practitioners to explore the ways in which digital organizations and digital operating models function in high-velocity environments. Working practices such as Agile and Lean, and technical practices and technologies such as Cloud, Automation, and Automatic Testing are included. What You Will Learn At the end of this course, participants will be able to: Understand concepts regarding the high-velocity nature of the digital enterprise, including the demand it places on IT. Understand the digital product lifecycle in terms of the ITIL operating model. Understand the importance of the ITIL guiding principles and other fundamental concepts for delivering high-velocity IT. Know how to contribute to achieving value with digital products. Course Introduction Let's Get to Know Each Other Course Learning Objectives Target Audience Characteristics ITIL® 4 Certification Scheme Course Components Course Agenda Module-End Exercises Exam Details Introduction to High-Velocity IT High-Velocity IT Digital Technology Digital Organizations Digital Transformation High-Velocity IT Approaches Relevance of High-Velocity IT Approaches High-Velocity IT Approaches in Detail High-Velocity IT Operating Models Introduction ITIL® Perspective High-Velocity IT Aspects High-Velocity IT Applications ITIL® Building Blocks for High-Velocity IT Digital Product Lifecycle Service Value Streams Four Dimensions of Service Management ITIL® Management Practices High-Velocity IT Culture Key Behavior Patterns ITIL® Guiding Principles Supporting Models and Concepts for Purpose Ethics Design Thinking Supporting Models and Concepts for People Reconstructing for Service Agility Safety Culture Stress Prevention Supporting Models and Concepts for Progress Working in Complex Environments Lean Culture ITIL® Continual Improvement Model High-Velocity IT Objectives and Techniques High-Velocity IT Objectives High-Velocity IT Techniques Techniques for Valuable Investments Prioritization Techniques Minimum Viable Products and Services Product / Service Ownership A/B Testing Techniques for Fast Developments Basic Concepts Related to Fast Development Infrastructure as Code Reviews Continual Business Analysis Continuous Integration / Continuous Delivery (CI/CD) Continuous Testing Kanban Techniques for Resilient Operations Introduction to Resilient Operations Technical Debt Chaos Engineering Definition of Done Version Control Algorithmic IT Operations ChatOps Site Reliability Engineering (SRE) Techniques for Co-created Value Basic Concepts of Co-created Value Service Experience Techniques for Assured Conformance DevOps Audit Defense Toolkit DevSecOpsPeer Review

NFV training course description Network Functions Virtualization (NFV) brings many benefits, this training course cuts through the hype and looks at the technology, architecture and products available for NFV. What will you learn Explain how NFV works. Describe the architecture of NFV. Explain the relationship between NFV and SDN. Recognise the impact NFV will have on existing networks. NFV training course details Who will benefit: Anyone wishing to know more about NFV. Prerequisites: Introduction to Virtualization. Duration 2 days NFV training course content Introduction What is NfV? What are network Functions? NfV benefits, NfV market drivers. ETSI NfV framework. Virtualization review Server, storage and network virtualization and NfV. Virtual machines, containers and docker. Data centres, clouds, SaaS, IaaS, PaaS. Virtualization of Network Functions Network virtualization versus Network Function virtualization. ETSI NfV architecture ETSI documents, Architecture overview, compute domain, hypervisor domain, infrastructure network domain. IETF and NfV Creating services, Service Functions, Service Function Chaining. SPRING and source packet routing. YANG and NetConf. RESTCONF. VLANs, VPNs, VXLAN. MANO Management and Orchestration. OpenStack, OpenDaylight PaaS and NfV. The VNF domain. Service graphs, MANO descriptors, Open orchestration. The virtualization layer VM centric model, containers versus hypervisors, FD.io. Summary Deploying NfV, performance, testing. Futures.

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

Server Load Balancing course description This two-day Server Load Balancing course introduces the concepts of SLB from the reasons to implement, through the basics and then onto details studies of load distribution, health checks, layer 7 switching and Global SLB. What will you learn Explain packet paths when implementing SLB. Recognise the impact of different topologies. Evaluate SLB load distribution methods. Describe how load balancers can improve security. Explain how GSLB works. Server Load Balancing course details Who will benefit: Anyone working with SLB. Prerequisites: None. Duration 2 days Server Load Balancing course contents Introduction Concept, reasons, benefits, alternatives. Other features: Security, Caching. SLB concepts Architectures, Virtual servers, real servers, Virtual IP address, health checks. DNS load balancing. Packet walk using SLB. Load balancing 6 modes of bonding and load balancing without SLB. ISP load balancing. Health. Distribution policies: Round Robin, least connections, weighted distributions, response time, other variations. Persistent versus concurrent. Layer 4 switching L2 SLB, L3 SLB, single arm SLB, DSR, more packet walking, TCP versus UDP, Port numbers. Layer 7 switching Persistence. Cookie switching, Cookie hashing, Cookie insertion, URL switching, URL Hashing, SSL. Health checks Layer 3: ARP, ping. Layer 4: SYN, UDP. Layer 7: HTTP GET, Status codes, HTTP keepalives, content verification, SSL. Other application keepalives. What to do after failure and recovery. Security DOS attack protection, SYN attack protection, Rate limiting: connections, transactions. SSL offload. Redundancy Hot standby, Active standby, Active active. Stateful, stateless. VRRP, STP. GSLB Anycasting. DNS, TTL, DNS load balancing, problems with DNS load balancing,. HTTP redirect, health, thresholds, round trip times, location.

Network virtualization training course description This course covers network virtualization. It has been designed to enable network engineers to recognise and handle the requirements of networking Virtual Machines. Both internal and external network virtualization is covered along with the technologies used to map overlay networks on to the physical infrastructure. Hands on sessions are used to reinforce the theory rather than teach specific manufacturer implementations. What will you learn Evaluate network virtualization implementations and technologies. Connect Virtual Machines with virtual switches. Explain how overlay networks operate. Describe the technologies in overlay networks. Network virtualization training course details Who will benefit: Engineers networking virtual machines. Prerequisites: Introduction to virtualization. Duration 2 days Network virtualization training course contents Virtualization review Hypervisors, VMs, containers, migration issues, Data Centre network design. TOR and spine switches. VM IP addressing and MAC addresses. Hands on VM network configuration Network virtualization What is network virtualization, internal virtual networks, external virtual networks. Wireless network virtualization: spectrum, infrastructure, air interface. Implementations: Open vSwitch, NSX, Cisco, others. Hands on VM communication over the network. Single host network virtualization NICs, vNICs, resource allocation, vSwitches, tables, packet walks. vRouters. Hands on vSwitch configuration, MAC and ARP tables. Container networks Single host, network modes: Bridge, host, container, none. Hands on Docker networking. Multi host network virtualization Access control, path isolation, controllers, overlay networks. L2 extensions. NSX manager. OpenStack neutron. Packet walks. Distributed logical firewalls. Load balancing. Hands on Creating, configuring and using a distributed vSwitch. Mapping virtual to physical networks VXLAN, VTEP, VXLAN encapsulation, controllers, multicasts and VXLAN. VRF lite, GRE, MPLS VPN, 802.1x. Hands on VXLAN configuration. Orchestration vCenter, vagrant, OpenStack, Kubernetes, scheduling, service discovery, load balancing, plugins, CNI, Kubernetes architecture. Hands on Kubernetes networking. Summary Performance, NFV, automation. Monitoring in virtual networks.