707 Microsoft courses in March delivered Live Online

Duration 1 Days 6 CPD hours This course is intended for Software Engineers Overview The objective of this course is to learn the key language concepts to machine learning, Spark MLlib, and Spark ML. This course will teach you the key language concepts to machine learning, Spark MLlib, and Spark ML. The course includes coverage of collaborative filtering, clustering, classification, algorithms, and data volume. This course will teach you the key language concepts to machine learning, Spark MLlib, and Spark ML. The course includes coverage of collaborative filtering, clustering, classification, algorithms, and data volume.

Duration 3 Days 18 CPD hours This course is intended for This course is geared for experienced Scala developers who are new to the world of machine learning and are eager to expand their skillset. Professionals such as data engineers, data scientists, and software engineers who want to harness the power of machine learning in their Scala-based projects will greatly benefit from attending. Additionally, team leads and technical managers who oversee Scala development projects and want to integrate machine learning capabilities into their workflows can gain valuable insights from this course Overview Working in a hands-on learning environment led by our expert instructor you'll: Grasp the fundamentals of machine learning and its various categories, empowering you to make informed decisions about which techniques to apply in different situations. Master the use of Scala-specific tools and libraries, such as Breeze, Saddle, and DeepLearning.scala, allowing you to efficiently process, analyze, and visualize data for machine learning projects. Develop a strong understanding of supervised and unsupervised learning algorithms, enabling you to confidently choose the right approach for your data and effectively build predictive models Gain hands-on experience with neural networks and deep learning, equipping you with the know-how to create advanced applications in areas like natural language processing and image recognition. Explore the world of generative AI and learn how to utilize GPT-Scala for creative text generation tasks, broadening your skill set and making you a more versatile developer. Conquer the realm of scalable machine learning with Scala, learning the secrets to tackling large-scale data processing and analysis challenges with ease. Sharpen your skills in model evaluation, validation, and optimization, ensuring that your machine learning models perform reliably and effectively in any situation. Machine Learning Essentials for Scala Developers is a three-day course designed to provide a solid introduction to the world of machine learning using the Scala language. Throughout the hands-on course, you?ll explore a range of machine learning algorithms and techniques, from supervised and unsupervised learning to neural networks and deep learning, all specifically crafted for Scala developers. Our expert trainer will guide you through real-world, focused hands-on labs designed to help you apply the knowledge you gain in real-world scenarios, giving you the confidence to tackle machine learning challenges in your own projects. You'll dive into innovative tools and libraries such as Breeze, Saddle, DeepLearning.scala, GPT-Scala (and Generative AI with Scala), and TensorFlow-Scala. These cutting-edge resources will enable you to build and deploy machine learning models for a wide range of projects, including data analysis, natural language processing, image recognition and more. Upon completing this course, you'll have the skills required to tackle complex projects and confidently develop intelligent applications. You?ll be able to drive business outcomes, optimize processes, and contribute to innovative projects that leverage the power of data-driven insights and predictions. Introduction to Machine Learning and Scala Learning Outcome: Understand the fundamentals of machine learning and Scala's role in this domain. What is Machine Learning? Machine Learning with Scala: Advantages and Use Cases Supervised Learning in Scala Learn the basics of supervised learning and how to apply it using Scala. Supervised Learning: Regression and Classification Linear Regression in Scala Logistic Regression in Scala Unsupervised Learning in Scala Understand unsupervised learning and how to apply it using Scala. Unsupervised Learning:Clustering and Dimensionality Reduction K-means Clustering in Scala Principal Component Analysis in Scala Neural Networks and Deep Learning in Scala Learning Outcome: Learn the basics of neural networks and deep learning with a focus on implementing them in Scala. Introduction to Neural Networks Feedforward Neural Networks in Scala Deep Learning and Convolutional Neural Networks Introduction to Generative AI and GPT in Scala Gain a basic understanding of generative AI and GPT, and how to utilize GPT-Scala for natural language tasks. Generative AI: Overview and Use Cases Introduction to GPT (Generative Pre-trained Transformer) GPT-Scala: A Library for GPT in Scala Reinforcement Learning in Scala Understand the basics of reinforcement learning and its implementation in Scala. Introduction to Reinforcement Learning Q-learning and Value Iteration Reinforcement Learning with Scala Time Series Analysis using Scala Learn time series analysis techniques and how to apply them in Scala. Introduction to Time Series Analysis Autoregressive Integrated Moving Average (ARIMA) Models Time Series Analysis in Scala Natural Language Processing (NLP) with Scala Gain an understanding of natural language processing techniques and their application in Scala. Introduction to NLP: Techniques and Applications Text Processing and Feature Extraction NLP Libraries and Tools for Scala Image Processing and Computer Vision with Scala Learn image processing techniques and computer vision concepts with a focus on implementing them in Scala. Introduction to Image Processing and Computer Vision Feature Extraction and Image Classification Image Processing Libraries for Scala Model Evaluation and Validation Understand the importance of model evaluation and validation, and how to apply these concepts using Scala. Model Evaluation Metrics Cross-Validation Techniques Model Selection and Tuning in Scala Scalable Machine Learning with Scala Learn how to handle large-scale machine learning problems using Scala. Challenges of Large-Scale Machine Learning Data Partitioning and Parallelization Distributed Machine Learning with Scala Machine Learning Deployment and Production Understand the process of deploying machine learning models into production using Scala. Deployment Challenges and Best Practices Model Serialization and Deserialization Monitoring and Updating Models in Production Ensemble Learning Techniques in Scala Discover ensemble learning techniques and their implementation in Scala. Introduction to Ensemble Learning Bagging and Boosting Techniques Implementing Ensemble Models in Scala Feature Engineering for Machine Learning in Scala Learn advanced feature engineering techniques to improve machine learning model performance in Scala. Importance of Feature Engineering in Machine Learning Feature Scaling and Normalization Techniques Handling Missing Data and Categorical Features Advanced Optimization Techniques for Machine Learning Understand advanced optimization techniques for machine learning models and their application in Scala. Gradient Descent and Variants Regularization Techniques (L1 and L2) Hyperparameter Tuning Strategies

Duration 1 Days 6 CPD hours This course is intended for This course is intended for SharePoint administrators who work in a Windows network environment. At least one year of experience managing SharePoint servers and using PowerShell is recommended. Overview At Course Completion?Configure SharePoint Servers using PowerShell?Manage SharePoint Server Administration with PowerShell?Manage SharePoint Server Maintenance with PowerShell This course provides students with the knowledge and skills needed to use PowerShell to administer SharePoint Servers. Students will learn how to manage day-to-day and automated tasks carried out by SharePoint Administrators. Install and Configure SharePoint Servers with PowerShell Overview Preparing the Server Configure Server Features Configure Prerequisite Applications Install SharePoint Software Configure SharePoint Farm Review Lab 1: Configure SharePoint Servers with PowerShell Administering SharePoint Servers with PowerShell Overview Administering Users and Groups Administering the Farm Administering Shared Services & Features Administering Sites Administering Databases Review Lab 1: Administering SharePoint Servers with PowerShell Maintaining SharePoint Servers with PowerShell Overview Managing Backups / Restores Monitoring and Auditing Managing Notifications Scheduling Tasks Review Lab 1: Maintaining SharePoint Servers with PowerShell

Duration 2 Days 12 CPD hours This course is intended for Participants who have actual experience in the data centre and/or IT infrastructures are best suited. Attendance of the CDCP© course is recommended but not a requirement. Overview After completion of the course the participant will be able to: 1. Develop and review their data centre strategy 2. Use different risk assessment methodologies together with practical tips specifically for data centre migrations to reduce the risk during a data centre migration 3. Understand different migration strategies 4. Understand the legal aspects when migrating a data centre 5. Understand the importance of Business Service Reviews and Service Level Objectives 6. Size and design the target data centre 7. Understand the importance of detailed discovery and how dependencies influence migration waves 8. Understand the safety requirements during migration 19. Get lots of practical tips when moving to another data centre This course is designed to expose participants to a step-by-step methodology which will enable them to reduce the risks involved when undertaking a data centre migration. It will also give participants a lot of valuable practical hints and tips by trainers having extensive experience in moving and consolidating mission critical data centre. Data Centre Strategy Data centre lifecycle Reasons to migrate a data centre Alternatives to data centre migration Consolidation Outsourcing Cloud computing Upgrade existing data centre or build new Project Management Project management and methods Scope statement Statement Of Work (SOW) Work Breakdown Structure (WBS) Allocate time to the project Cost and estimation methodology Project communication Risk Management Risk management and methods Risk identification Risk assessment methodologies Qualitative approach Semi-quantitative approach Quantitative approach Risk evaluation Risk treatment Risk in data centre migrations Migration Strategies Different data centre migration strategies Heterogeneous migration Homogeneous migration Physical migration Different IT transformations Pre-migration transformation Migration transformation Post-migration transformation Legal Aspects Regulatory requirements Contractual considerations Legal aspects when decommissioning High Level Discovery & Planning The importance of Business Service Reviews The concept of Availability The concept of Recoverability The importance of Service Level Objectives Requirements on designing the target IT architecture Information needed for high level planning Design Target Data Centre Requirements for the target data centre Sizing the data centre Architectural requirements Cooling requirements Power requirements Security Detailed Discovery and Planning The importance of discovery Automated discovery tools Asset management Network and system dependencies Detailed migration planning Migration waves Staffing Warranties and insurance Safety Safety precautions Technical safety review Electrical safety Lifting Personal safety during migration Fire safety during migration Security Controversy between access and security Access control Managing security during migration Security during migration Key management Practical hints and tips Continuous improvement Implementation Rehearsal Route investigation Resourcing Logistics team Packing Transport Installing the equipment Post migration support End of Project Why project closure Lessons learned Phased completion of project Criteria for project closure The outcome of the project End of project Exam: Certified Data Centre Migration Specialist Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Duration 2 Days 12 CPD hours Overview Install and initialize WEM and integrate into Citrix Virtual Apps and Desktops and Citrix DaaS. Configure WEM features to improve the end user environment and virtual resource consumption. Migrate an on-premises WEM deployment to WEM service Designed for experienced IT professionals, you will discover why WEM is the go-to system optimization and logon optimization solution for a Citrix deployment's app and desktop workloads. You will learn how to plan, build, rollout, and manage on-premises WEM or WEM service and how to integrate it into Citrix Virtual Apps and Desktops or Citrix DaaS. You will leave this course with a good understanding of how to manage additional solutions and features in your Citrix Virtual Apps and Desktop or Citrix DaaS site Module 1: Introduction to Workspace Environment Management (WEM) WEM Features and Benefits Module 2: Planning ? WEM Architecture and Component Communications WEM On-Premises Components and Deployments WEM Service Components and Deployments WEM Component Communication Workflows Module 3: Planning - WEM On-Premises Deployment Installation On-Premises WEM: Leading Practice Installation Prerequisites and Steps On-Premises WEM: ADMX Template Configuration Choosing a Security Principal to run the WEM Infrastructure Service Creating the WEM Database Running the WEM Infrastructure Service Configuration Utility On-Premises WEM: Agent Installation Module 4: Planning ? WEM Service Deployment Installation WEM On-Premises vs WEM Service WEM Service: Leading Practice Installation Prerequisites and Steps WEM Service: ADMX Template Configuration WEM Service: Agent Installation Module 5: Planning ? WEM Consoles and Initial Setup On-Premises WEM and WEM Service Consoles WEM Initial Setup Migrating GPO settings to WEM Module 6: Planning ? WEM System and Log On Optimization WEM System Optimization Overview WEM CPU Management WEM Memory Management Additional System Optimization Features WEM Log On Optimization Overview WEM Assigned Actions WEM Environmental Settings Citrix Profile Management In WEM Module 7: Planning ? WEM Security and Lockdown Features WEM Security Management Features Privilege Elevation and Process Hierarchy Control WEM Transformer Module 8: Planning - The WEM Agent WEM Settings Processing and WEM Agent Caches WEM Agent Integration with Citrix Virtual Apps and Desktops and Citrix DaaS Module 9: Planning ? WEM Monitoring, Reporting, and Troubleshooting WEM Monitoring and Reporting WEM Agent Troubleshooting WEM Service Troubleshooting Module 10: Planning ? Upgrading WEM and Migration to WEM Service Upgrading Workspace Environment Management WEM On-Premises Migration to WEM Service Module 11: Rolling Out a WEM Deployment WEM Agent User Options on Windows Desktops Module 12: Managing a WEM Deployment Measuring WEM Success Additional course details: Nexus Humans CWS-220 Citrix Workspace Environment Management Deployment and Administration 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 CWS-220 Citrix Workspace Environment Management Deployment and Administration 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 2 Days 12 CPD hours This course is intended for System Engineers/Administrators; Backup/Virtualization Administrators; Solution Architects/Consultants Overview By the end of the course, students should be able to:Maximize your expertise on consulting/professional services for the Veeam Availability Suite solutionAim for the VMCA title and comply with some of the certifications requirements for Platinum ProPartners and Veeam Accredited Service Partners (VASPs)Gain professional advantage with the highest level of Veeam technical certification This course covers Veeam Availability Suite? design and optimization and is based on best practices from Veeam solutions architects. IntroductionDesign & Sizing DNS and name resolution Veeam backup server Backup and replication database Proxy server Transport modes Repository server WAN accelerator Interaction with hypervisors Infrastructure Stages of Proof of Concept Assessment using Veeam ONE? Important data to collect (Veeam ONE + infrastructure accounts) Security Permissions Design Scenario: Part 1 Discovery Create a design based on the customer environment Optimizations Backup and replication database Proxy server Repository server WAN accelerator Tape Veeam Backup Enterprise Manager Indexing Antivirus on Veeam servers and guest VM (if VSS is used) Protecting Veeam Backup & Replication? configuration Design Scenario: Part 2 Create a design based on the customer?s evolving environment Automation Veeam backup server VMware vSphere tags Audit & Compliance Auditing Compliance SureBackup© and SureReplica Troubleshooting Deep dive into reading log files Common issues Troubleshooting mode (SureBackup/SureReplica)

Duration 4.125 Days 24.75 CPD hours This course is intended for The job roles best suited to the material in this course are: Project managers and consultants involved in business continuity Expert advisors seeking to master the implementation of the business continuity management system Individuals responsible to maintain conformity with BCMS requirements within an organization Members of the BCMS team Overview Understand the concepts, approaches, methods, and techniques used for the implementation and effective management of a BCMS. Learn how to interpret and implement the requirements of ISO 22301 in the specific context of an organization. Understand the operation of the business continuity management system and its processes based on ISO 22301. Learn how to interpret and implement the requirements of ISO 22301 in the specific context of an organization. No two disasters in the world cause equal damage. Between the unpredictability of natural disasters, information security breaches, and incidents of different nature, preparedness can make you stand out in the crowd and predict the future of your business. In light of this, proper planning is essential to mitigating risks, avoiding consequences, coping with the negative effects of disasters and incidents, but at the same time, continuing your daily operations so that customer needs do not remain unfulfilled.This training course will prepare its participants to implement a business continuity management system (BCMS) in compliance with the requirements of ISO 22301. Attending this training course allows you to gain a comprehensive understanding of the best practices of the business continuity management system and to be able to establish a framework that allows the organization to continue operating efficiently during disruptive events Introduction to ISO 22301 and initiation of a BCMS Training course objectives and structure Standards and regulatory frameworks Business continuity management system (BCMS) Fundamental business continuity concepts and principles Initiation of the BCMS implementation Understanding the organization and its context BCMS scope Implementation plan of a BCMS Leadership and commitment Business continuity policy Risks, opportunities, and business continuity objectives Support for the BCMS Business impact analysis Risk assessment Implementation of a BCMS Business continuity strategies and solutions Business continuity plans and procedures Incident response and emergency response Crisis management Exercise programs Monitoring, measurement, analysis, and evaluation Internal audit BCMS monitoring, continual improvement, and preparation for the certification audi Management review Treatment of nonconformities Continual improvement Preparation for the certification audit Closing of the training course

Duration 1 Days 6 CPD hours This course is intended for This class is intended for the following: Data analysts, Data scientists, Business analysts getting started with Google Cloud Platform. Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results and creating reports. Executives and IT decision makers evaluating Google Cloud Platform for use by data scientists. Overview This course teaches students the following skills:Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform.Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform.Employ BigQuery and Cloud Datalab to carry out interactive data analysis.Train and use a neural network using TensorFlow.Employ ML APIs.Choose between different data processing products on the Google Cloud Platform. This course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities. Introducing Google Cloud Platform Google Platform Fundamentals Overview. Google Cloud Platform Big Data Products. Compute and Storage Fundamentals CPUs on demand (Compute Engine). A global filesystem (Cloud Storage). CloudShell. Lab: Set up a Ingest-Transform-Publish data processing pipeline. Data Analytics on the Cloud Stepping-stones to the cloud. Cloud SQL: your SQL database on the cloud. Lab: Importing data into CloudSQL and running queries. Spark on Dataproc. Lab: Machine Learning Recommendations with Spark on Dataproc. Scaling Data Analysis Fast random access. Datalab. BigQuery. Lab: Build machine learning dataset. Machine Learning Machine Learning with TensorFlow. Lab: Carry out ML with TensorFlow Pre-built models for common needs. Lab: Employ ML APIs. Data Processing Architectures Message-oriented architectures with Pub/Sub. Creating pipelines with Dataflow. Reference architecture for real-time and batch data processing. Summary Why GCP? Where to go from here Additional Resources Additional course details: Nexus Humans Google Cloud Platform Big Data and Machine Learning Fundamentals 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 Google Cloud Platform Big Data and Machine Learning Fundamentals 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 1 Days 6 CPD hours This course is intended for This course is intended for: Developers responsible for developing Deep Learning applications Developers who want to understand concepts behind Deep Learning and how to implement a Deep Learning solution on AWS Overview This course is designed to teach you how to: Define machine learning (ML) and deep learning Identify the concepts in a deep learning ecosystem Use Amazon SageMaker and the MXNet programming framework for deep learning workloads Fit AWS solutions for deep learning deployments In this course, you?ll learn about AWS?s deep learning solutions, including scenarios where deep learning makes sense and how deep learning works. You?ll learn how to run deep learning models on the cloud using Amazon SageMaker and the MXNet framework. You?ll also learn to deploy your deep learning models using services like AWS Lambda while designing intelligent systems on AWS. Module 1: Machine learning overview A brief history of AI, ML, and DL The business importance of ML Common challenges in ML Different types of ML problems and tasks AI on AWS Module 2: Introduction to deep learning Introduction to DL The DL concepts A summary of how to train DL models on AWS Introduction to Amazon SageMaker Hands-on lab: Spinning up an Amazon SageMaker notebook instance and running a multi-layer perceptron neural network model Module 3: Introduction to Apache MXNet The motivation for and benefits of using MXNet and Gluon Important terms and APIs used in MXNet Convolutional neural networks (CNN) architecture Hands-on lab: Training a CNN on a CIFAR-10 dataset Module 4: ML and DL architectures on AWS AWS services for deploying DL models (AWS Lambda, AWS IoT Greengrass, Amazon ECS, AWS Elastic Beanstalk) Introduction to AWS AI services that are based on DL (Amazon Polly, Amazon Lex, Amazon Rekognition) Hands-on lab: Deploying a trained model for prediction on AWS Lambda Additional course details: Nexus Humans Deep Learning on AWS 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 Deep Learning on AWS 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 This course is intended for: Developers Solutions Architects Data Engineers Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker Overview In this course, you will learn to: Select and justify the appropriate ML approach for a given business problem Use the ML pipeline to solve a specific business problem Train, evaluate, deploy, and tune an ML model using Amazon SageMaker Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS Apply machine learning to a real-life business problem after the course is complete This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem. Module 0: Introduction Pre-assessment Module 1: Introduction to Machine Learning and the ML Pipeline Overview of machine learning, including use cases, types of machine learning, and key concepts Overview of the ML pipeline Introduction to course projects and approach Module 2: Introduction to Amazon SageMaker Introduction to Amazon SageMaker Demo: Amazon SageMaker and Jupyter notebooks Hands-on: Amazon SageMaker and Jupyter notebooks Module 3: Problem Formulation Overview of problem formulation and deciding if ML is the right solution Converting a business problem into an ML problem Demo: Amazon SageMaker Ground Truth Hands-on: Amazon SageMaker Ground Truth Practice problem formulation Formulate problems for projects Module 4: Preprocessing Overview of data collection and integration, and techniques for data preprocessing and visualization Practice preprocessing Preprocess project data Class discussion about projects Module 5: Model Training Choosing the right algorithm Formatting and splitting your data for training Loss functions and gradient descent for improving your model Demo: Create a training job in Amazon SageMaker Module 6: Model Evaluation How to evaluate classification models How to evaluate regression models Practice model training and evaluation Train and evaluate project models Initial project presentations Module 7: Feature Engineering and Model Tuning Feature extraction, selection, creation, and transformation Hyperparameter tuning Demo: SageMaker hyperparameter optimization Practice feature engineering and model tuning Apply feature engineering and model tuning to projects Final project presentations Module 8: Deployment How to deploy, inference, and monitor your model on Amazon SageMaker Deploying ML at the edge Demo: Creating an Amazon SageMaker endpoint Post-assessment Course wrap-up Additional course details: Nexus Humans The Machine Learning Pipeline on AWS 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 The Machine Learning Pipeline on AWS 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.