3024 Engineering courses delivered Online

Develop the commercial awareness, financial knowledge and strategic thinking capabilities, to influence the direction of the business Course overview Duration: 2 days (13 hours) This course is aimed at managers who want to develop their commercial awareness, financial knowledge and strategic thinking capabilities, so that they can influence the direction of their business and deliver to their full potential. Day one of the course provides the skills and insights to make sense of your company’s financial position and performance. Day two helps delegates to consider the strategic thinking tools required to plot the forward course needed to maximise the potential of the business. As well as looking at how to make effective business decisions, this course gives a good grounding in finance and profitability. As a two day programme, day one provides the skills and insights to make sense of the company’s financial position and performance. Day two then considers the strategic thinking tools needed to plot the forward course needed to maximise the potential of the business. Objectives By the end of the course you will be able to: An understanding of the balance sheet, profit and loss account, cash flow and statutory and management accounts Learnt to correctly employment key financial ratios to analyse your business A practical definition of strategy analysis tools to examine the current environment and capabilities Steps to devise a mission and vision statement Recognition of the skills and resources needed to achieve the vision Generation of appropriate strategic and tactical commercial objectives Content What is Strategy Defining Strategy Strategic thinking Strategic models Commercial thinking – what is money? Where are we now STEEPLE analysis SWOT Analysis P&E forces at work Political distortions in capitalist markets Where are we trying to get to Setting the mission and vision Creating a BHAG Strategies for deflation and inflation The role of banks Commercial and investment banking Fractional reserving Securitisation How to get there Skill gap analysis Business Process Re engineering The role of creativity How to get there Getting the team on board Individual and team motivation The power of the brand Overcoming challenges Debt and deleveraging Change management Creating value Discounted Cash Flows Building the business case Asset Valuation techniques Making it happen Turning Strategic Thinking into Strategic Plans Scenario planning for an uncertain future Creating commitments and lock in Discussion and review Time will be set aside during the course for review sessions with time for questions, answers and action learning.

About this Virtual Instructor Led Certificate Training Course (VILT) Asset maintenance and equipment reliability teams play a significant role to ensure that there is no room for downtime and losses in production. They are often recognised for their contribution and ability to keep assets running productively in today's organisations. The Certificate in Asset Management Virtual Instructor Led Training (VILT) course will provide those involved in Asset Management with a full explanation of the key processes to manage assets across their lifecycle. This recognised VILT course has been designed to equip participants with practical skills to take back to work. This VILT course enables participants to ensure their organisation's assets are realising their full value in support of the organisation's objectives. Accredited by the Institute of Asset Management (IAM), this VILT course will prepare participants to sit for the IAM Asset Management Certificate qualification. The IAM exam is offered as an option for participants of this VILT course. Training Objectives By the end of this VILT course, participants will be able to: Understand the key principles, tools and terminology of Asset Management, and demonstrate how it will benefit your organisation Gain familiarity in the application of ISO 55000 in practice Access a range of models that will support the implementation of asset management in your organisation Assess your understanding of the current tools and concepts applied in Asset Management Capture new ideas and skills that will enhance performance and be better prepared for the Institute of Asset Management (IAM) Certificate Examination Target Audience This VILT course will benefit maintenance managers, operations managers, asset managers and reliability professionals, planners and functional specialists. It will also be useful for facilities engineers, supervisors/managers and structural engineers/supervisors/ and managers. IAM Qualifications Syllabi This document details the scope of the individual topics which comprise the examination modules, and how the exams are assessed. It is important that prospective candidates understand the scope of the modules to determine the preparation required. Download here IAM Qualifications Candidate Handbook This handbook provides more detailed information on registering as a candidate, learning resources, training courses, booking an exam, exam regulations and what happens after an exam - whether you are successful or unsuccessful. Download here Course Level Basic or Foundation Training Methods The VILT course will be delivered online in 5 half-day sessions comprising 4 hours per day, with 2 breaks of 15 minutes per day. Course Duration: 5 half-day sessions, 4 hours per session (20 hours in total) Other than world-class visuals and slides, this VILT course will include a high level of interaction between the facilitator and participants and group discussion among the participants themselves. There will be a number of exercises & quizzes to demonstrate key points and to give participants the chance to apply learning and appreciate key aspects of best practice. Participants will also have the chance to share examples from their own experience, discuss real problems they are facing and develop actions for improvement when they return to work. Examples of the exercises that are used in this VILT course are as follows: Exercises: Aligning Assets to Business Objectives, Planning for Contingencies, Understanding Function and Failure. Group exercises: Asset Management Decision Making, Incident Review & Operations Optimisation. The workshop content will be adjusted based on the discussions, interests and needs of the participants on the course. Trainer Your expert course leader is a is a highly experienced in maintenance and turnaround specialist. He is a Chartered Mechanical Engineer, having spent 19 years working for BP in engineering, maintenance and turnaround management roles. During this time, he worked on plants at all ages in the lifecycle, from construction, commissioning and operating new assets to maintaining aging assets and decommissioning. He has taken roles in Projects, Human Resources and Integrity Management which give real breadth to his approach. He also specialized in Continuous Improvement, gaining the award of International Petrochemical Coach of the year. He stays up to date with the latest industrial developments through his consulting support for major clients. He is also the Asset Management lead and a VILT specialist, having delivered over 70 days of VILT training in the last year. He has an engaging style and will bring his current industrial experience, proficiency of VILT techniques and diverse content, gathered from a comprehensive training portfolio, to deliver a distinctive training experience. 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

Register on the SQL NoSQL Big Data and Hadoop today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career. The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials. Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get a digital certificate as a proof of your course completion. The SQL NoSQL Big Data and Hadoop is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablet, and smartphones. The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly! What You Get With The SQL NoSQL Big Data and Hadoop Receive a e-certificate upon successful completion of the course Get taught by experienced, professional instructors Study at a time and pace that suits your learning style Get instant feedback on assessments 24/7 help and advice via email or live chat Get full tutor support on weekdays (Monday to Friday) Course Design The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace. You are taught through a combination of Video lessons Online study materials Certification Upon successful completion of the course, you will be able to obtain your course completion e-certificate free of cost. Print copy by post is also available at an additional cost of £9.99 and PDF Certificate at £4.99. Who Is This Course For: The course is ideal for those who already work in this sector or are an aspiring professional. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge. Requirements: The online training is open to all students and has no formal entry requirements. To study the SQL NoSQL Big Data and Hadoop, all your need is a passion for learning, a good understanding of English, numeracy, and IT skills. You must also be over the age of 16. Course Content Section 01: Introduction Introduction 00:07:00 Building a Data-driven Organization - Introduction 00:04:00 Data Engineering 00:06:00 Learning Environment & Course Material 00:04:00 Movielens Dataset 00:03:00 Section 02: Relational Database Systems Introduction to Relational Databases 00:09:00 SQL 00:05:00 Movielens Relational Model 00:15:00 Movielens Relational Model: Normalization vs Denormalization 00:16:00 MySQL 00:05:00 Movielens in MySQL: Database import 00:06:00 OLTP in RDBMS: CRUD Applications 00:17:00 Indexes 00:16:00 Data Warehousing 00:15:00 Analytical Processing 00:17:00 Transaction Logs 00:06:00 Relational Databases - Wrap Up 00:03:00 Section 03: Database Classification Distributed Databases 00:07:00 CAP Theorem 00:10:00 BASE 00:07:00 Other Classifications 00:07:00 Section 04: Key-Value Store Introduction to KV Stores 00:02:00 Redis 00:04:00 Install Redis 00:07:00 Time Complexity of Algorithm 00:05:00 Data Structures in Redis : Key & String 00:20:00 Data Structures in Redis II : Hash & List 00:18:00 Data structures in Redis III : Set & Sorted Set 00:21:00 Data structures in Redis IV : Geo & HyperLogLog 00:11:00 Data structures in Redis V : Pubsub & Transaction 00:08:00 Modelling Movielens in Redis 00:11:00 Redis Example in Application 00:29:00 KV Stores: Wrap Up 00:02:00 Section 05: Document-Oriented Databases Introduction to Document-Oriented Databases 00:05:00 MongoDB 00:04:00 MongoDB Installation 00:02:00 Movielens in MongoDB 00:13:00 Movielens in MongoDB: Normalization vs Denormalization 00:11:00 Movielens in MongoDB: Implementation 00:10:00 CRUD Operations in MongoDB 00:13:00 Indexes 00:16:00 MongoDB Aggregation Query - MapReduce function 00:09:00 MongoDB Aggregation Query - Aggregation Framework 00:16:00 Demo: MySQL vs MongoDB. Modeling with Spark 00:02:00 Document Stores: Wrap Up 00:03:00 Section 06: Search Engines Introduction to Search Engine Stores 00:05:00 Elasticsearch 00:09:00 Basic Terms Concepts and Description 00:13:00 Movielens in Elastisearch 00:12:00 CRUD in Elasticsearch 00:15:00 Search Queries in Elasticsearch 00:23:00 Aggregation Queries in Elasticsearch 00:23:00 The Elastic Stack (ELK) 00:12:00 Use case: UFO Sighting in ElasticSearch 00:29:00 Search Engines: Wrap Up 00:04:00 Section 07: Wide Column Store Introduction to Columnar databases 00:06:00 HBase 00:07:00 HBase Architecture 00:09:00 HBase Installation 00:09:00 Apache Zookeeper 00:06:00 Movielens Data in HBase 00:17:00 Performing CRUD in HBase 00:24:00 SQL on HBase - Apache Phoenix 00:14:00 SQL on HBase - Apache Phoenix - Movielens 00:10:00 Demo : GeoLife GPS Trajectories 00:02:00 Wide Column Store: Wrap Up 00:04:00 Section 08: Time Series Databases Introduction to Time Series 00:09:00 InfluxDB 00:03:00 InfluxDB Installation 00:07:00 InfluxDB Data Model 00:07:00 Data manipulation in InfluxDB 00:17:00 TICK Stack I 00:12:00 TICK Stack II 00:23:00 Time Series Databases: Wrap Up 00:04:00 Section 09: Graph Databases Introduction to Graph Databases 00:05:00 Modelling in Graph 00:14:00 Modelling Movielens as a Graph 00:10:00 Neo4J 00:04:00 Neo4J installation 00:08:00 Cypher 00:12:00 Cypher II 00:19:00 Movielens in Neo4J: Data Import 00:17:00 Movielens in Neo4J: Spring Application 00:12:00 Data Analysis in Graph Databases 00:05:00 Examples of Graph Algorithms in Neo4J 00:18:00 Graph Databases: Wrap Up 00:07:00 Section 10: Hadoop Platform Introduction to Big Data With Apache Hadoop 00:06:00 Big Data Storage in Hadoop (HDFS) 00:16:00 Big Data Processing : YARN 00:11:00 Installation 00:13:00 Data Processing in Hadoop (MapReduce) 00:14:00 Examples in MapReduce 00:25:00 Data Processing in Hadoop (Pig) 00:12:00 Examples in Pig 00:21:00 Data Processing in Hadoop (Spark) 00:23:00 Examples in Spark 00:23:00 Data Analytics with Apache Spark 00:09:00 Data Compression 00:06:00 Data serialization and storage formats 00:20:00 Hadoop: Wrap Up 00:07:00 Section 11: Big Data SQL Engines Introduction Big Data SQL Engines 00:03:00 Apache Hive 00:10:00 Apache Hive : Demonstration 00:20:00 MPP SQL-on-Hadoop: Introduction 00:03:00 Impala 00:06:00 Impala : Demonstration 00:18:00 PrestoDB 00:13:00 PrestoDB : Demonstration 00:14:00 SQL-on-Hadoop: Wrap Up 00:02:00 Section 12: Distributed Commit Log Data Architectures 00:05:00 Introduction to Distributed Commit Logs 00:07:00 Apache Kafka 00:03:00 Confluent Platform Installation 00:10:00 Data Modeling in Kafka I 00:13:00 Data Modeling in Kafka II 00:15:00 Data Generation for Testing 00:09:00 Use case: Toll fee Collection 00:04:00 Stream processing 00:11:00 Stream Processing II with Stream + Connect APIs 00:19:00 Example: Kafka Streams 00:15:00 KSQL : Streaming Processing in SQL 00:04:00 KSQL: Example 00:14:00 Demonstration: NYC Taxi and Fares 00:01:00 Streaming: Wrap Up 00:02:00 Section 13: Summary Database Polyglot 00:04:00 Extending your knowledge 00:08:00 Data Visualization 00:11:00 Building a Data-driven Organization - Conclusion 00:07:00 Conclusion 00:03:00 Resources Resources - SQL NoSQL Big Data And Hadoop 00:00:00

Overview: Rail Transportation Systems   Welcome to the future of transportation with our comprehensive suite of modules designed to equip professionals and enthusiasts alike with the knowledge and skills needed to excel in the dynamic world of rail transportation. Our product, 'Rail Transportation Systems,' offers a holistic approach to understanding and mastering every facet of rail systems, from operations and management to safety, sustainability, and beyond.   Module 1: Introduction to Rail Transportation Systems Embark on your journey into the world of rail transportation with Module 1. Gain a solid foundation by exploring the history, evolution, and fundamental principles of rail systems. Understand the key components of rail networks and their role in modern transportation infrastructure.   Module 2: Rail Operations and Management Dive deep into the intricacies of rail operations and management in Module 2. Learn about scheduling, routing, dispatching, and other essential operational aspects. Explore best practices for optimizing efficiency, reliability, and customer satisfaction in rail services.   Module 3: Rail Economics and Finance Unlock the economic engine driving rail systems in Module 3. Delve into the principles of rail economics, including cost analysis, pricing strategies, and revenue management. Gain insights into the financial models and investment decisions shaping the future of rail transportation.   Module 4: Rail Infrastructure Design and Maintenance Discover the backbone of rail systems with Module 4. Explore the planning, design, construction, and maintenance of rail infrastructure, including tracks, stations, and signaling systems. Master the techniques for ensuring the safety, durability, and sustainability of rail assets.   Module 5: Rail Safety and Security Prioritize safety and security in rail operations with Module 5. Learn about risk assessment, hazard mitigation, and emergency response protocols. Stay abreast of the latest technologies and regulations aimed at enhancing safety and security across the rail network.   Module 6: Rail Environmental Sustainability Embrace the imperative of sustainability in rail transportation with Module 6. Examine the environmental impact of rail systems and strategies for mitigating carbon emissions, reducing energy consumption, and promoting eco-friendly practices. Explore innovations in sustainable infrastructure, propulsion systems, and materials.   Each module features engaging content, interactive learning activities, and real-world case studies curated by industry experts. Whether you're a seasoned professional seeking to expand your expertise or a newcomer eager to explore the fascinating world of rail transportation, our product offers a comprehensive and accessible learning experience.   Join us in shaping the future of transportation with 'Rail Transportation Systems' - where knowledge meets opportunity on the tracks to success. Course Curriculum Module 1_ Introduction to Rail Transportation Systems Introduction to Rail Transportation Systems 00:00 Module 2_ Rail Operations and Management Rail Operations and Management 00:00 Module 3_ Rail Economics and Finance Rail Economics and Finance 00:00 Module 4_ Rail Infrastructure Design and Maintenance Rail Infrastructure Design and Maintenance 00:00 Module 5_ Rail Safety and Security Rail Safety and Security 00:00 Module 6_ Rail Environmental Sustainability Rail Environmental Sustainability 00:00

Learn core Apache Kafka features along with creating Java, Node.js and Python producers and consumers

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Duration 3 Days 18 CPD hours This course is intended for This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below Getting Started & Optional Python Quick Refresher Statistics and Probability Refresher and Python Practice Probability Density Function; Probability Mass Function; Naive Bayes Predictive Models Machine Learning with Python Recommender Systems KNN and PCA Reinforcement Learning Dealing with Real-World Data Experimental Design / ML in the Real World Time Permitting: Deep Learning and Neural Networks Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices. This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms Getting Started Installation: Getting Started and Overview LINUX jump start: Installing and Using Anaconda & Course Materials (or reference the default container) Python Refresher Introducing the Pandas, NumPy and Scikit-Learn Library Statistics and Probability Refresher and Python Practice Types of Data Mean, Median, Mode Using mean, median, and mode in Python Variation and Standard Deviation Probability Density Function; Probability Mass Function; Naive Bayes Common Data Distributions Percentiles and Moments A Crash Course in matplotlib Advanced Visualization with Seaborn Covariance and Correlation Conditional Probability Naive Bayes: Concepts Bayes? Theorem Naive Bayes Spam Classifier with Naive Bayes Predictive Models Linear Regression Polynomial Regression Multiple Regression, and Predicting Car Prices Logistic Regression Logistic Regression Machine Learning with Python Supervised vs. Unsupervised Learning, and Train/Test Using Train/Test to Prevent Overfitting Understanding a Confusion Matrix Measuring Classifiers (Precision, Recall, F1, AUC, ROC) K-Means Clustering K-Means: Clustering People Based on Age and Income Measuring Entropy LINUX: Installing GraphViz Decision Trees: Concepts Decision Trees: Predicting Hiring Decisions Ensemble Learning Support Vector Machines (SVM) Overview Using SVM to Cluster People using scikit-learn Recommender Systems User-Based Collaborative Filtering Item-Based Collaborative Filtering Finding Similar Movie Better Accuracy for Similar Movies Recommending movies to People Improving your recommendations KNN and PCA K-Nearest-Neighbors: Concepts Using KNN to Predict a Rating for a Movie Dimensionality Reduction; Principal Component Analysis (PCA) PCA with the Iris Data Set Reinforcement Learning Reinforcement Learning with Q-Learning and Gym Dealing with Real-World Data Bias / Variance Tradeoff K-Fold Cross-Validation Data Cleaning and Normalization Cleaning Web Log Data Normalizing Numerical Data Detecting Outliers Feature Engineering and the Curse of Dimensionality Imputation Techniques for Missing Data Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE Binning, Transforming, Encoding, Scaling, and Shuffling Experimental Design / ML in the Real World Deploying Models to Real-Time Systems A/B Testing Concepts T-Tests and P-Values Hands-on With T-Tests Determining How Long to Run an Experiment A/B Test Gotchas Capstone Project Group Project & Presentation or Review Deep Learning and Neural Networks Deep Learning Prerequisites The History of Artificial Neural Networks Deep Learning in the TensorFlow Playground Deep Learning Details Introducing TensorFlow Using TensorFlow Introducing Keras Using Keras to Predict Political Affiliations Convolutional Neural Networks (CNN?s) Using CNN?s for Handwriting Recognition Recurrent Neural Networks (RNN?s) Using an RNN for Sentiment Analysis Transfer Learning Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters Deep Learning Regularization with Dropout and Early Stopping The Ethics of Deep Learning Learning More about Deep Learning Additional course details: Nexus Humans Machine Learning Essentials with Python (TTML5506-P) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Machine Learning Essentials with Python (TTML5506-P) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 4 Days 24 CPD hours This course is intended for Students in this course are interested in implementing DevOps processes or in passing the Microsoft Azure DevOps Solutions certification exam. Overview After completing this course, students will be able to: Plan for the transformation with shared goals and timelines Select a project and identify project metrics and Key Performance Indicators (KPI's) Create a team and agile organizational structure Design a tool integration strategy Design a license management strategy (e.g., Azure DevOps and GitHub users) Design a strategy for end-to-end traceability from work items to working software Design an authentication and access strategy Design a strategy for integrating on-premises and cloud resources Describe the benefits of using Source Control Describe Azure Repos and GitHub Migrate from TFVC to Git Manage code quality including technical debt SonarCloud, and other tooling solutions Build organizational knowledge on code quality Explain how to structure Git repos Describe Git branching workflows Leverage pull requests for collaboration and code reviews Leverage Git hooks for automation Use Git to foster inner source across the organization Explain the role of Azure Pipelines and its components Configure Agents for use in Azure Pipelines Explain why continuous integration matters Implement continuous integration using Azure Pipelines Define Site Reliability Engineering Design processes to measure end-user satisfaction and analyze user feedback Design processes to automate application analytics Manage alerts and reduce meaningless and non-actionable alerts Carry out blameless retrospectives and create a just culture Define an infrastructure and configuration strategy and appropriate toolset for a release pipeline and application infrastructure Implement compliance and security in your application infrastructure Describe the potential challenges with integrating open-source software Inspect open-source software packages for security and license compliance Manage organizational security and compliance policies Integrate license and vulnerability scans into build and deployment pipelines Configure build pipelines to access package security and license ratings This course provides the knowledge and skills to design and implement DevOps processes and practices. Students will learn how to plan for DevOps, use source control, scale Git for an enterprise, consolidate artifacts, design a dependency management strategy, manage secrets, implement continuous integration, implement a container build strategy, design a release strategy, set up a release management workflow, implement a deployment pattern, and optimize feedback mechanisms. Module 1: Get started on a DevOps transformation journey Introduction to DevOps Choose the right project Describe team structures Choose the DevOps tools Plan Agile with GitHub Projects and Azure Boards Introduction to source control Describe types of source control systems Work with Azure Repos and GitHub Module 2: Development for enterprise DevOps Structure your Git Repo Manage Git branches and workflows Collaborate with pull requests in Azure Repos Explore Git hooks Plan foster inner source Manage Git repositories Identify technical debt Module 3: Implement CI with Azure Pipelines and GitHub Actions Explore Azure Pipelines Manage Azure Pipeline agents and pools Describe pipelines and concurrency Explore Continuous integration Implement a pipeline strategy Integrate with Azure Pipelines Introduction to GitHub Actions Learn continuous integration with GitHub Actions Design a container build strategy Module 4: Design and implement a release strategy Introduction to continuous delivery Explore release strategy recommendations Build a high-quality release pipeline Introduction to deployment patterns Implement blue-green deployment and feature toggles Implement canary releases and dark launching Implement A/B testing and progressive exposure deployment Module 5: Implement a secure continuous deployment using Azure Pipelines Create a release pipeline Provision and test environments Manage and modularize tasks and templates Automate inspection of health Manage application configuration data Integrate with identity management systems Implement application configuration Module 6: Manage infrastructure as code using Azure and DSC Explore infrastructure as code and configuration management Create Azure resources using Azure Resource Manager templates Create Azure resources by using Azure CLI Explore Azure Automation with DevOps Implement Desired State Configuration (DSC) Implement Bicep Module 7: Implement security and validate code bases for compliance Introduction to Secure DevOps Implement open-source software Software Composition Analysis Static analyzers OWASP and Dynamic Analyzers Security Monitoring and Governance Module 8: Design and implement a dependency management strategy Explore package dependencies Understand package management Migrate, consolidate, and secure artifacts Implement a versioning strategy Introduction to GitHub Packages Module 9: Implement continuous feedback Implement tools to track usage and flow Develop monitor and status dashboards Share knowledge within teams Design processes to automate application analytics Manage alerts, Blameless retrospectives and a just culture Additional course details: Nexus Humans AZ-400 Designing and Implementing Microsoft DevOps Solutions training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the AZ-400 Designing and Implementing Microsoft DevOps Solutions course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.