30210 TEC courses

Duration 2 Days 12 CPD hours This course is intended for This is an advanced course for DBAs and technical individuals who plan, implement, and maintain Db2 11.1 databases Overview This course is designed to teach you how to: Perform advanced monitoring using the Db2 administrative views and routines in SQL queries. Manage the disk space assigned in Database Managed Storage (DMS) and Automatic Storage table spaces, including the activities of the rebalancer. Use SQL queries and Db2 commands to check the high water mark on table spaces and to monitor the rebalance operation. Utilize the REBUILD option of RESTORE, which can build a database copy with a subset of the tablespaces using database or tablespace backup images. Plan and execute the TRANSPORT option of RESTORE to copy schemas of objects between two Db2 databases. Create incremental database or tablespace level backups to reduce backup processing and backup image storage requirements. Implement automatic storage management for table spaces and storage groups or enable automatic resize options for DMS managed table spaces to reduce administration requirements and complexity. Describe the various types of database memory including buffer pools, sort memory, lock memory and utility processing memory. Adjust database or Db2 instance configuration options to improve application performance or processing efficiency. Implement Db2 Self Tuning Memory management for specific database memory areas. This course is designed to teach you how to Perform advanced monitoring using the Db2 administrative views and routines in SQL queries. Manage the disk space assigned in Database Managed Storage (DMS) and Automatic Storage table spaces, including the activities of the rebalancer. Use SQL queries and Db2 commands to check the high water mark on table spaces and to monitor the rebalance operation. Utilize the REBUILD option of RESTORE, which can build a database copy with a subset of the tablespaces using database or tablespace backup images. Plan and execute the TRANSPORT option of RESTORE to copy schemas of objects between two Db2 databases. Create incremental database or tablespace level backups to reduce backup processing and backup image storage requirements. Implement automatic storage management for table spaces and storage groups or enable automatic resize options for DMS managed table spaces to reduce administration requirements and complexity. Describe the various types of database memory including buffer pools, sort memory, lock memory and utility processing memory. Adjust database or Db2 instance configuration options to improve application performance or processing efficiency. Implement Db2 Self Tuning Memory management for specific database memory areas. Course Outline Advanced Monitoring Db2 Table Space Management Db2 Database Memory Management Database rebuild supportDb2 database and tablespace relocation Db2 Incremental Backup Additional course details: Nexus Humans CL464G IBM Db2 11.1 Advanced Database 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 CL464G IBM Db2 11.1 Advanced Database 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 5 Days 30 CPD hours This course is intended for For those seeing to prepare for CCIE Enterprise Infrastructure certification Overview This course will help prepare for CCIE Enterprise Infrastructure certification The new CCIE Enterprise Infrastructure certification program prepares you for today?s expert-level job roles in enterprise infrastructure technologies. CCIE Enterprise Infrastructure now includes automation and programmability to help you scale your enterprise infrastructure. VTP VTP and different versions Pruning EtherChannel LACP Layer 2 and Layer 3 Spanning Protocol 1d, 802.1w, and 802.1s SPAN, RSPAN, and ERSPAN DMVPN All Phases Redundancy: Two Clouds One Hub Two Hubs one Cloud Two hubs two Clouds Running Routing Protocols DMVPN over MPLS EIGRP RD, CD, S, FC, FS, and FD Configuration, and hidden debugging Authentications: MD5, and SHA, Summarization Load Balancing:Equal Cost, Unequal Cost, Add-Path, Filtering, Default Route Injection Optimization: Query Propagation Boundary, IP FRR, STUB routing (All Options) Metric: Classic, Wide Metric Route Tags: Decimal and Dotted-Decimal Notations, OTP OSPFv2 Overview and special cases GRE or Virtual-Links LSAs, FA, and RFCs (1583, 1587, 2328, 3101, 5185 and many more) Best Path Selection Network Types Area Types Optimization: GTSM, LFA, Default Route Injection Authentication: RFC 2328, RFC 5709, Summarization, Filtering BGP States Establishing a Peer Session: Regular method,Peer-Groups,Templates,Best Path Selection Attributes: Weight, AS-Path, Origin, Next-Hop, Local-Preference, Atomic-Aggregate Communities, Aggregator, and MED Load Balancing: Equal Cost,Unequal Cost,Conditional Advertisement,Out/In Bound Route Filtering and the order,ORF,Multihoming Scenarios AS-Path Manipulation: Regexp,Local-as,Allow-as,Remove-Private-as Convergence and Scalability: Route Reflectors,Confederation,Aggregation (All Options) Other BGP Features: MultiPath,Add-Path,Route-Refresh,Soft Reconfiguration IPv6 Acquiring an IPv6 Address: IPv6 General Prefix SLAAC DHCPv6 Rapid-Commit Relay Prefix Delegation IPv6 and DMVPN EIGRPv6 OSPFv3: Both flavors, LSAs, RFCs BGP for IPv6: IPv6 transport, and IPv4 route exchange Transitional Solutions: NAT-PT,6VPE,Multicast,MLD,Static RP,BSR,Embedded RP,IPv6 Traffic Filters,RA Guard,ND Inspection MPLS LDP, VRFs, RD, and RT L3VPNs Route Leaking PE to CE Routing Security Control Plane Policing VACLs Storm Control DHCP Snooping IP Source Guard DAI Private VLANs Port Security Access-lists uRPF Device Tracking IPsec Identity Use Case For FlexVPN: Site-to-Site, IKEv1, and IKEv2 Using Preshared Keys 1x Port Base Authentication : Device Roles,Port States,Authentication Process,Host Modes Network Services FHRP: HSRP, VRRP, and GLBP NAT: Static NAT, and PAT,Dynamic NAT,Policy-Base NAT,VRF-Aware NAT,VASI NAT Software Defined Infrastructure Cisco SD Access: Design a Cisco SD Access solution Underlay network (IS-IS, manual/PnP) Overlay fabric design (LISP, VXLAN, Cisco TrustSec) Fabric domains (single-site and multi-site using SD-WAN transit) Cisco SD Access deployment: Cisco DNA Center device discovery and device management Add fabric node devices to an existing fabric Host onboarding (wired endpoints only) Fabric border handoff Segmentation Macro-level segmentation using VNs Micro-level segmentation using SGTs (using Cisco ISE) Assurance Network and client health (360) Monitoring and troubleshooting Cisco SD-WAN: Design a Cisco SD-WAN solution Orchestration plane (vBond, NAT) Management Plane (vManage) Control Plane (vSmart, OMP) Data Plane (vEdge/cEdge) WAN edge deployment Onboarding new edge routers Orchestration with zero-touch provisioning/PnP OMP TLOC Configuration templates Localized policies (only QoS) Centralized policies Application aware Routing Topologies

Duration 3 Days 18 CPD hours This course is intended for This intermediate course is for application programmers who need to write embedded SQL COBOL or PL/I programs in either a DB2 9 or DB2 10 for z/OS environment. Overview Incorporate static SQL statements in an application program Prepare the program for execution Validate execution results are correct Produce code to support multiple rows being returned from the database manager using cursors Identify considerations regarding units of work, concurrency, and restart of programs Identify differences between static and dynamic SQL Provide test data for applications Discuss program and DB2 options relative to performance of static SQL This course enables you to acquire the skills necessary to produce application programs that manipulate DB2 databases. Emphasis is on embedding Structured Query Language (SQL) statements and preparing programs for execution. CV720G;CF82G;DB2 Concepts Identify DB2 family products Explain DB2 workstation component functions Identify DB2 objects Identify the key differences between static SQL and other application alternatives for accessing DB2 data Program Structure I Embed INSERT, UPDATE, DELETE and single-row SELECT statements in application programs Effectively communicate with DB2 when processing NULL values and determining success of statement execution Demonstrate use of DB2 coding aids Code CONNECT statements within an application program Identify connection types and impacts on a unit of work Program for the Call Attach Facility (CAF) Program Preparation Identify the additional steps necessary to prepare a program that contains embedded SQL for execution Describe the functions of the DB2 PRECOMPILE and BIND processes Describe factors relevant to the BIND process, including RUNSTATS positioning, package status, parameters, and authorization requirements Program Structure II Use DECLARE, OPEN, FETCH, and CLOSE CURSOR statements to handle select criteria that may return multiple rows in application programs Issue positioned UPDATE and DELETE statements Identify how scrollable cursors can be used Recovery and Locking Concepts Define a unit of recovery Identify the basic locking strategies used by DB2 Dynamic SQL Introduction Describe the difference between static and dynamic SQL List the types of dynamic statements Code dynamic SQL in a program Managing Test Data Identify methods to insert data into a table Use the LOAD or IMPORT utility Identify the purpose of the RUNSTATS utility Identify the purpose of the REORG utility Performance Considerations Use programming techniques that enhance DB2 application performance by following general guidelines, using indexable predicates, and avoiding unnecessary sorts Identify the access paths available to DB2 List common causes of deadlocks and avoid such causes when possible Use the EXPLAIN tools as aids to develop applications that emphasize performance Additional course details: Nexus Humans CV722 IBM DB2 11 for z/OS Application Programming Workshop 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 CV722 IBM DB2 11 for z/OS Application Programming Workshop 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 5 Days 30 CPD hours This course is intended for The skills covered in this course converge on four areas-software development, IT operations, applied math and statistics, and business analysis. Target students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems. So, the target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decisionmaking products that bring value to the business. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification Overview In this course, you will develop AI solutions for business problems. You will: Solve a given business problem using AI and ML. Prepare data for use in machine learning. Train, evaluate, and tune a machine learning model. Build linear regression models. Build forecasting models. Build classification models using logistic regression and k -nearest neighbor. Build clustering models. Build classification and regression models using decision trees and random forests. Build classification and regression models using support-vector machines (SVMs). Build artificial neural networks for deep learning. Put machine learning models into operation using automated processes. Maintain machine learning pipelines and models while they are in production Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions. Solving Business Problems Using AI and ML Topic A: Identify AI and ML Solutions for Business Problems Topic B: Formulate a Machine Learning Problem Topic C: Select Approaches to Machine Learning Preparing Data Topic A: Collect Data Topic B: Transform Data Topic C: Engineer Features Topic D: Work with Unstructured Data Training, Evaluating, and Tuning a Machine Learning Model Topic A: Train a Machine Learning Model Topic B: Evaluate and Tune a Machine Learning Model Building Linear Regression Models Topic A: Build Regression Models Using Linear Algebra Topic B: Build Regularized Linear Regression Models Topic C: Build Iterative Linear Regression Models Building Forecasting Models Topic A: Build Univariate Time Series Models Topic B: Build Multivariate Time Series Models Building Classification Models Using Logistic Regression and k-Nearest Neighbor Topic A: Train Binary Classification Models Using Logistic Regression Topic B: Train Binary Classification Models Using k-Nearest Neighbor Topic C: Train Multi-Class Classification Models Topic D: Evaluate Classification Models Topic E: Tune Classification Models Building Clustering Models Topic A: Build k-Means Clustering Models Topic B: Build Hierarchical Clustering Models Building Decision Trees and Random Forests Topic A: Build Decision Tree Models Topic B: Build Random Forest Models Building Support-Vector Machines Topic A: Build SVM Models for Classification Topic B: Build SVM Models for Regression Building Artificial Neural Networks Topic A: Build Multi-Layer Perceptrons (MLP) Topic B: Build Convolutional Neural Networks (CNN) Topic C: Build Recurrent Neural Networks (RNN) Operationalizing Machine Learning Models Topic A: Deploy Machine Learning Models Topic B: Automate the Machine Learning Process with MLOps Topic C: Integrate Models into Machine Learning Systems Maintaining Machine Learning Operations Topic A: Secure Machine Learning Pipelines Topic B: Maintain Models in Production

Duration 2 Days 12 CPD hours This course is intended for This class is intended for network engineers and network admins that are either using Google Cloud Platform or are planning to do so. The class is also for individuals that want to be exposed to software-defined networking solutions in the cloud. Overview Configure Google VPC networks, subnets, and routers Control administrative access to VPC objects Control network access to endpoints in VPCsInterconnect networks among GCP projects Interconnect networks among GCP VPC networks and on-premises or other-cloud networks Choose among GCP load balancer and proxy options and configure them Use Cloud CDN to reduce latency and save money Optimize network spend using Network TiersConfigure Cloud NAT or Private Google Access to provide instances without public IP addresses access to other services Deploy networks declaratively using Cloud Deployment Manager or Terraform Design networks to meet common customer requirements Configure monitoring and logging to troubleshoot networks problems Learn about the broad variety of networking options on Google Cloud. This course uses lectures, demos, and hands-on labs to help you explore and deploy Google Cloud networking technologies, including Virtual Private Cloud (VPC) networks, subnets, and firewalls; interconnection among networks; load balancing; Cloud DNS; Cloud CDN; and Cloud NAT. You'll also learn about common network design patterns and automated deployment using Cloud Deployment Manager or Terraform. Google Cloud VPC Networking Fundamentals Recall that networks belong to projects. Explain the differences among default, auto, and custom networks. Create networks and subnets. Explain how IPv4 addresses are assigned to Compute Engine instances. Publish domain names using Google Cloud DNS. Create Compute Engine instances with IP aliases. Create Compute Engine instances with multiple virtual network. Controlling Access to VPC Networks Outline how IAM policies affect VPC networks. Control access to network resources using service accounts. Control access to Compute Engine instances with tag-based firewall rules. Sharing Networks across Projects Outline the overall workflow for configuring Shared VPC. Differentiate between the IAM roles that allow network resources to be managed. Configure peering between unrelated VPC Networks. Recall when to use Shared VPC and when to use VPC Network Peering. Load Balancing Recall the various load balancing services. Configure Layer 7 HTTP(S) load balancing. Whitelist and blacklist IP traffic with Cloud Armor. Cache content with Cloud CDN. Explain Layer 4 TCP or SSL proxy load balancing. Explain regional network load balancing. Configure internal load balancing. Recall the choices for enabling IPv6 Internet connectivity for Google Cloud load balancers. Determine which Google Cloud load balancer to use when. Hybrid Connectivity Recall the Google Cloud interconnect and peering services available to connect your infrastructure to Google Cloud. Explain Dedicated Interconnect and Partner Interconnect. Describe the workflow for configuring a Dedicated Interconnect. Build a connection over a VPN with Cloud Router. Determine which Google Cloud interconnect service to use when. Explain Direct Peering and Partner Peering. Determine which Google Cloud peering service to use when. Networking Pricing and Billing Recognize how networking features are charged for. Use Network Service Tiers to optimize spend. Determine which Network Service Tier to use when. Recall that labels can be used to understand networking spend. Network Design and Deployment Explain common network design patterns. Configure Private Google Access to allow access to certain Google Cloud services from VM instances with only internal IP addresses. Configure Cloud NAT to provide your instances without public IP addresses access to the internet. Automate the deployment of networks using Deployment Manager or Terraform. Launch networking solutions using Cloud Marketplace. Network Monitoring and Troubleshooting Configure uptime checks, alerting policies and charts for your network services. Use VPC Flow Logs to log and analyze network traffic behavior.

Duration 1 Days 6 CPD hours This course is intended for This course is designed for individuals who may need to present information effectively in a professional environment. Overview Define what makes a presentation effective. Plan presentations. Design a presentation framework. Develop the presentation body. Create supporting materials. Prepare for your presentation. Deliver presentations. Conduct a question-and-answer session. Deliver group presentations and virtual presentations. The ability to deliver presentations is vital to achieving advancement for yourself and for your ideas. Few skills in life will contribute to your success as much as presentation skills. Without a dynamic and coherent presentation, even stellar ideas can fail to convince your audience. In this course, you will learn to organize your ideas to create coherent and convincing oral presentations, while also utilizing available visual aids and using public-speaking techniques to strengthen your delivery. Private classes on this topic are available. We can address your organization?s issues, time constraints, and save you money, too. Contact us to find out how. Prerequisites To ensure your success, you will need to have experience writing in a professional context and creating presentations using Microsoft Office PowerPoint. 1. Defining Presentation Effectiveness Identify Qualities of Effective Presentations Evaluate Yourself as a Presenter 2. Planning Presentations Analyze the Audience Establish Your Presentation\'s Objectives 3. Designing the Presentation Create the Presentation Structure Organize the Presentation Body Write the Conclusion First Write the Introduction 4. Developing the Presentation Body Select Evidence Write the Presentation Body Create Visuals 5. Creating Supporting Materials Create a Slide Deck Create Speaker Aids Create Audience Handouts 6. Preparing for Your Presentation Rehearse the Presentation Plan Event Logistics 7. Delivering Presentations Connect with Your Audience Present Powerfully Utilize a Slide Deck Effectively 8. Conducting a Question-and-Answer Session Answer Questions Handle Challenging Questions 9. Presenting in Common Business Scenarios Plan and Deliver a Virtual Presentation Plan and Deliver Group Presentations 10. Key Course Information This course focuses on the skills necessary to prepare and deliver an effective presentation; that being said, the learner will not be creating, delivering, or designing a specific presentation from start to finish in this course (this course only provides the foundational knowledge for doing this work back at the office). This course consists of instructor lecture along with course activities corresponding with the main course objectives. In terms of the course activities, 50% will be discussion based - 25% will be in a group-work format - and 25% will be hands-on/involve a digital tool, such as a PowerPoint or Word file. The intent is for students leaving this course to take the skills learned and apply them to their efforts of creating more effective presentations upon returning to the workplace. Additional course details: Nexus Humans Effective Presentations (Second Edition) 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 Effective Presentations (Second Edition) 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 3 Days 18 CPD hours This course is intended for This course is designed for network and software engineers who hold the following job roles: Network engineer Systems engineer Wireless engineer Consulting systems engineer Technical solutions architect Network administrator Wireless design engineer Network manager Site reliability engineer Deployment engineer Sales engineer Account manager Overview After taking this course, you should be able to: Leverage the tools and APIs to automate Cisco ACI powered data centers. Demonstrate workflows (configuration, verification, healthchecking, monitoring) using Python, Ansible, and Postman. Leverage the various models and APIs of the Cisco Nexus OS platform to perform day 0 operations, improve troubleshooting methodologies with custom tools, augment the CLI using scripts, and integrate various workflows using Ansible and Python. Describe the paradigm shift of Model Driven Telemetry and understand the building blocks of a working solution. Describe how the Cisco Data Center compute solutions can be managed and automated using API centric tooling, by using the Python SDK, PowerTool, and Ansible modules to implement various workflows on Cisco UCS, Cisco IMC, Cisco UCS Manager, Cisco UCS Director, and Cisco Intersight. The Implementing Automation for Cisco Data Center Solutions (DCAUI) v1.1 course teaches you how to implement Cisco© Data Center automated solutions including programming concepts, orchestration, and automation tools. Through a combination of lessons and hands-on practice, you will manage the tools and learn the benefits of programmability and automation in the Cisco-powered Data Center. You will examine Cisco Application Centric Infrastructure (Cisco ACI©), Software-Defined Networking (SDN) for data center and cloud networks, Cisco Nexus© (Cisco NX-OS) platforms for device-centric automation, and Cisco Unified Computing System (Cisco UCS©) for Data Center compute. You will study their current ecosystem of Application Programming Interfaces (APIs), software development toolkits, and relevant workflows along with open industry standards, tools, and APIs, such as Python, Ansible, Git, JavaScript Object Notation (JSON), Yaml Ain't Markup Language (YAML), Network Configuration Protocol (NETCONF), Representational State Transfer Configuration Protocol (RESTCONF), and Yet Another Generation (YANG).This course prepares you for the 300-635 Automating Cisco Data Center Solutions (DCAUTO) certification exam. Introducing Automation for Cisco Solutions (CSAU) is required prior to enrolling in Implementing Automation for Cisco Data Center Solutions (DCAUI) because it provides crucial foundational knowledge essential to success. This course also earns you 24 Continuing Education (CE) credits towards recertification. Course Outline Describing the Cisco ACI Policy Model Describing the Cisco APIC REST API Using Python to Interact with the ACI REST API Using Ansible to Automate Cisco ACI Introducing Cisco NX-OS Programmability Describing Day-Zero Provisioning with Cisco NX-OS Implementing On-Box Programmability and Automation with Cisco NX-OS Implementing Off-Box Programmability and Automation with Cisco NX-OS Automating Cisco UCS Using Developer Tools Implementing Workflows Using Cisco UCS Director Describing Cisco DCNM Describing Cisco Intersight Additional course details: Nexus Humans Cisco Implementing Automation for Cisco Data Center Solutions (DCAUI) v1.1 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 Cisco Implementing Automation for Cisco Data Center Solutions (DCAUI) v1.1 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.

Duration 5 Days 30 CPD hours This course is intended for Experienced system administrators System engineers System integrators Overview By the end of the course, you should be able to meet the following objectives: Introduce troubleshooting principles and procedures Practice Linux commands that aid in the troubleshooting process Use command-line interfaces, log files, and the vSphere Client to diagnose and resolve problems in the vSphere environment Explain the purpose of key vSphere log files Monitor and optimize compute, network, and storage performance on ESXi hosts Monitor and optimize vCenter Server performance Identify networking problems based on reported symptoms, validate and troubleshoot the reported problem, identify the root cause and implement the appropriate resolution Analyze storage failure scenarios using a logical troubleshooting methodology, identify the root cause, and apply the appropriate resolution to resolve the problem Troubleshoot vSphere cluster failure scenarios and analyze possible causes Diagnose common VMware vSphere High Availability problems and provide solutions Identify and validate VMware ESXi™ host and VMware vCenter Server problems, analyze failure scenarios, and select the correct resolution Troubleshoot virtual machine problems, including migration problems, snapshot problems, and connection problems Troubleshoot performance problems with vSphere components This five-day, accelerated, hands-on training course is a blend of the VMware vSphere: Optimize and Scale and VMware vSphere: Troubleshooting courses. This Fast Track course includes topics from each of these advanced courses to equip experienced VMware administrators with the knowledge and skills to effectively optimize and troubleshoot vSphere at an expert level. Course Introduction Introductions and course logistics Course objectives Introduction to Troubleshooting Define the scope of troubleshooting Use a structured approach to solve configuration and operational problems Apply a troubleshooting methodology to logically diagnose faults and improve troubleshooting efficiency Troubleshooting Tools Use command-line tools (such as Linux commands, vSphere CLI, ESXCLI) to identify and troubleshoot vSphere problems Identify important vSphere log files and interpret the log file contents Network Optimization Explain performance features of network adapters Explain the performance features of vSphere networking Use esxtop to monitor key network performance metrics Troubleshooting Virtual Networking Analyze and resolve standard switch and distributed switch problems Analyze virtual machine connectivity problems and fix them Examine common management network connectivity problems and restore configurations Storage Optimization Describe storage queue types and other factors that affect storage performance Use esxtop to monitor key storage performance metrics Troubleshooting Storage Troubleshoot and resolve storage (iSCSI, NFS, and VMware vSphere© VMFS) connectivity and configuration problems Analyze and resolve common VM snapshot problems Identify multipathing-related problems, including common causes of permanent device loss (PDL) and all paths down (APD) events and resolve these problems CPU Optimization Explain the CPU scheduler operation and other features that affect CPU performance Explain NUMA and vNUMA support Use esxtop to monitor key CPU performance metrics Memory Optimization Explain ballooning, memory compression, and host-swapping techniques for memory reclamation when memory is overcommitted Use esxtop to monitor key memory performance metrics Troubleshooting vSphere Clusters Identify and recover from problems related to vSphere HA Analyze and resolve VMware vSphere© vMotion© configuration and operational problems Analyze and resolve common VMware vSphere© Distributed Resource Scheduler? problems Troubleshooting Virtual Machines Identify possible causes and resolve virtual machine power-on problems Troubleshoot virtual machine connection state problems Resolve problems seen during VMware Tools? installations vCenter Server Performance Optimization Describe the factors that influence vCenter Server performance Use VMware vCenter© Server Appliance? tools to monitor resource use Troubleshooting vCenter Server and ESXi Analyze and fix problems with vCenter Server services Analyze and fix vCenter Server database problems Examine ESXi host and vCenter Server failure scenarios and resolve the problems

Duration 4 Days 24 CPD hours This course is intended for The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview Overview of data science and machine learning at scale Overview of the Hadoop ecosystem Working with HDFS data and Hive tables using Hue Introduction to Cloudera Data Science Workbench Overview of Apache Spark 2 Reading and writing data Inspecting data quality Cleansing and transforming data Summarizing and grouping data Combining, splitting, and reshaping data Exploring data Configuring, monitoring, and troubleshooting Spark applications Overview of machine learning in Spark MLlib Extracting, transforming, and selecting features Building and evaluating regression models Building and evaluating classification models Building and evaluating clustering models Cross-validating models and tuning hyperparameters Building machine learning pipelines Deploying machine learning models Spark, Spark SQL, and Spark MLlib PySpark and sparklyr Cloudera Data Science Workbench (CDSW) Hue This workshop covers data science and machine learning workflows at scale using Apache Spark 2 and other key components of the Hadoop ecosystem. The workshop emphasizes the use of data science and machine learning methods to address real-world business challenges. Using scenarios and datasets from a fictional technology company, students discover insights to support critical business decisions and develop data products to transform the business. The material is presented through a sequence of brief lectures, interactive demonstrations, extensive hands-on exercises, and discussions. The Apache Spark demonstrations and exercises are conducted in Python (with PySpark) and R (with sparklyr) using the Cloudera Data Science Workbench (CDSW) environment. The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview of data science and machine learning at scaleOverview of the Hadoop ecosystemWorking with HDFS data and Hive tables using HueIntroduction to Cloudera Data Science WorkbenchOverview of Apache Spark 2Reading and writing dataInspecting data qualityCleansing and transforming dataSummarizing and grouping dataCombining, splitting, and reshaping dataExploring dataConfiguring, monitoring, and troubleshooting Spark applicationsOverview of machine learning in Spark MLlibExtracting, transforming, and selecting featuresBuilding and evauating regression modelsBuilding and evaluating classification modelsBuilding and evaluating clustering modelsCross-validating models and tuning hyperparametersBuilding machine learning pipelinesDeploying machine learning models Additional course details: Nexus Humans Cloudera Data Scientist Training 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 Cloudera Data Scientist Training 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.