NEXT-GEN IT - arcitura.com · Attaining a passing grade on this exam achieves a specialist...

NEXT-GEN ITACADEMY

DevOpsBlockchainInternet of Things (IoT)ContainerizationMachine LearningArtificial Intelligence

2018COURSE CATALOG

Copyright © Arcitura Education Inc. www.arcitura.com 2

OVERVIEW

369

12151821

DEVOPS SPECIALIST

BLOCKCHAIN SPECIALIST

IoT SPECIALIST

CONTAINERIZATION SPECIALIST

MACHINE LEARNING SPECIALIST

ARTIFICIAL INTELLIGENCE SPECIALIST

CERTIFICATION TRACKS

Fundamental DevOps

DevOps in Practice

DevOps Lab

Fundamental Blockchain

Blockchain Technology Concepts

Blockchain Technology Lab

Fundamental IoT

IoT Technology and Architecture Concepts

IoT Technology Lab

Fundamental Containerization

Advanced Containerization

Containerization Lab

Fundamental Machine Learning

Advanced Machine Learning

Machine Learning Lab

Fundamental Artificial Intelligence

Advanced Artificial Intelligence

Artificial Intelligence Lab

The Next-Gen IT Academy from Arcitura is dedicated to providing an ever-growing variety of training courses and accreditations in contemporary technologies and fields of practice within the IT industry. Important and modern innovations that are redefining the IT landscape and that have reached a sufficient state of maturity are researched and documented into sets of courses that form the basis for formal certifications.

The Next-Gen IT Academy is a broad program that covers a range of diverse topic areas focused on next-generation information technology innovations, such as DevOps, Blockchain, the Internet of Things (IoT), Containerization, Machine Learning and Artificial Intelligence. For each topic area covered within the Next-Gen IT Academy, a set of three courses is developed. These courses can be taken purely for training purposes or they can also be taken for exam preparation purposes. For each topic area covered there is a single final exam. Attaining a passing grade on this exam achieves a specialist certification that is automatically issued by Arcitura.

The Next-Gen IT Academy will continue to introduce courses and certifications for new topic areas throughout the coming years.


STUDY KIT CONTENTSThis course is available as part of an Arcitura Study Kit in full-color printed and eLearning formats. In addition to the base course materials used during training workshops, additional materials designed for self-study purposes are also included.

This course provides a comprehensive overview of DevOps practices, models and techniques, along with coverage of DevOps benefits, challenges and business and technology drivers. Also explained is how DevOps compares to traditional solution development and release approaches and how the application of DevOps can be monitored and measured for concrete business value.

The following primary topics are covered:• Benefits, Challenges and Goals of DevOps• DevOps Compared to other Methodologies and Approaches• DevOps Rapid Delivery and Scalability• DevOps Lifecycle and Stages• DevOps Collaborative Practices• Continuous Integration (CI) and Continuous Delivery (CD)• Automated Configuration Management and• Infrastructure-as-Code (IaC) and Policy-as-Code (PaC)• DevOps Collaboration and Communication Practices• Value Stream Mapping and Preparing for Failure• Kanban and the Deming Cycle• DevOps Platforms and Toolchains• Measuring DevOps (Metrics and Monitoring)• DevOps and Microservices• Agile and Integrated Pipelines with DevOpsDuration: 1 Day

CERTIFICATIONThis course is part of an accreditation program through which the DevOps Specialist Certification can be achieved.

Fundamental DevOpsDEVOPSMODULE

01

EXAMAttaining the DevOps Specialist Certification requires a passing grade on a single exam that encompasses the topics covered in Modules 1, 2 and 3.

eLEARNINGThis course is available via on-line access as part of an eLearning Study Kit.

• Workbook • Presentation Booklet• Self-Study Guide• Mind Map Poster• Flashcards• Audio Tutor Recordings



This course delves into the application of DevOps practices and models by exploring how the DevOps lifecycle and its associated stages can be carried out and further identifying related challenges and considerations. In-depth coverage is provided for the application of Continuous Integration (CI) and Continuous Delivery (CD) approaches, along with an exploration of creating deployment pipelines and managing data flow, solution versions and tracking solution dependencies.

The following primary topics are covered:• DevOps and Organizational integration• DevOps Cultural Aspects• DevOps Roles and Responsibilities and Managing People through Change• DevOps Organizational Structure and Integration into Project Cycles• Creating Deployment Pipelines• Continuous Integration (CI) Drill-Down• Continuous Delivery (CD) Drill-Down• Configuration and Change Management / Continuous Configuration Management• Continuous Change and Managing Change• Applying the DevOps Lifecycle and Stages• Managing Continues Delivery and the Delivery Pipeline• Managing Data Flow and Version Control• Deployment and Release Management• Governing and Tracking Application and Software Dependencies• DevOps CI/CD Testing StrategyThis course further includes the DevOps Tool Vendor Supplement with descriptions of current toolsets, such as Ansible, Terraform, Cheff, Puppet, Jenkines, Kubernetes, AWS Code Pipeline and Elastic Beanstalk. This is an optional supplement with content that is not covered on the exam.

Duration: 1 Day


DevOps in PracticeDEVOPSMODULE

02CERTIFICATIONThis course is part of an accreditation program through which the DevOps Specialist Certification can be achieved.





This course module presents participants with a series of exercises and problems that are designed to test their ability to apply their knowledge of topics covered in previous courses. Completing this lab will help highlight areas that require further attention and will further prove hands-on proficiency in DevOps models, practices and strategies, as they are combined and applied to solve real-world problems.

For instructor-led delivery of this lab course, the Certified Trainer works closely with participants to ensure that all exercises are carried out completely and accurately. Attendees can voluntarily have exercises reviewed and graded as part of the class completion. For individual completion of this course as part of a study kit, a number of supplements are provided to help participants carry out exercises with guidance.

Duration: 1 Day

DEVOPSMODULE

03DevOps Lab

• Lab Exercises Booklet• Self-Study Guide• Mind Map Poster• Flashcards• Audio Tutor Recordings

CERTIFICATIONThis course is part of an accreditation program through which the DevOps Specialist Certification can be achieved.





This course provides end-to-end coverage of essential technology and business topics pertaining to Blockchain. The industry drivers and impacts of Blockchain are explained, followed by coverage of basic Blockchain components and artifacts. Various Blockchain technologies are discussed, along with security considerations and common applications and usage scenarios.

The following primary topics are covered:• Drivers, Benefits and Challenges of Blockchain• Blockchain Value Propositions• Industrial Impacts of Blockchain on Different Industry Verticals• Fundamental Components of a Blockchain Architecture• Blocks, Nodes, Verifiers/Verification and Chaining• Consensus and Group Consensus• Public vs. Private / Permissionless vs. Permissioned Blockchains• Coins, Tokens, Smart Contracts• Basics of Cryptography Algorithms (Crypto Hashing)• Centralized Ledger vs Decentralized Ledger • Decentralized Applications and Impacts of Decentralization• Fundamental Blockchain Security Considerations• Blockchain and Cryptocurrency• DevOps for Blockchain Development• Blockchain Value-Adds for IoT• Blockchain Availability, Scalability and Anonymity• On-Chain and Off-Chain TransactionsDuration: 1 Day

• Workbook • Presentation Booklet • Self-Study Guide• Mind Map Poster• Flashcards• Audio Tutor Recordings

Fundamental BlockchainBLOCK CHAIN

MODULE

01CERTIFICATIONThis course is part of an accreditation program through which the Blockchain Specialist Certification can be achieved.

EXAMAttaining the Blockchain Specialist Certification requires a passing grade on a single exam that encompasses the topics covered in Modules 1, 2 and 3.




This course delves into the technologies, artifacts and inner workings of blockchains and related solutions by exploring a series of key mechanisms, associated technologies and common architectural considerations. Also covered are common security concerns and mechanisms.

The following primary topics are covered:• Public and Private Blockchain Types• Architectural and Security Blockchain Mechanisms• Smart Contract, Distributed Ledgers and Consensus• Proof of Work (PoW) and Proof of Stake (PoS)• Delegated Proof of Stake (DPoS) and Leased Proof of Stake (LPoS)• Proof of Importance (PoI) and Proof of Elapsed Time (PoET)• Practical Byzantine Fault Tolerance (PBFT)• Decentralized Blockchain Solution Architectures• Event Chaining and Event Sourcing in Block Chain• Ethereum, Hyperledger and Enterprise Integration• Smart Contracts Drill-Down and Data Sovereignty• Blockchain Security Threats and Controls• Transaction Signing, Cryptography and Digital Signatures with Blockchain• Blockchain Integrity• Cloud Computing and Blockchain, Blockchain-as-a-Service• Blockchain Security and IoTDuration: 1 Day


Blockchain Technology Concepts

BLOCK CHAIN

MODULE

02CERTIFICATIONThis course is part of an accreditation program through which the Blockchain Specialist Certification can be achieved.





This course module presents participants with a series of exercises and problems that are designed to test their ability to apply their knowledge of topics covered in previous courses. Completing this lab will help highlight areas that require further attention and will further prove hands-on proficiency in blockchain technologies, mechanisms and security controls as they are applied and combined to solve real-world problems.


Duration: 1 Day


BLOCK CHAIN

MODULE

03Blockchain Technology Lab

CERTIFICATIONThis course is part of an accreditation program through which the Blockchain Specialist Certification can be achieved.





This course covers the spectrum of key topics pertaining to the Internet of Things (IoT) from both business and technical aspects. How IoT has impacted and is being utilized within different industries is explored and essential IoT concepts, models and technologies are introduced and related to fundamental IoT characteristics and functions.

The primary topics covered by this course are:• Business and Technology Drivers and Evolution of IoT• Understanding IoT Business Models and Design Processes• Any X-Point of View and IoT Characteristics• Intelligence model and Hybrid Architecture• Scalability and Unprecedent Events• Communication protocols and Localization• Everything-as-a-Service (XaaS)• IoT in Different Domains and Industries• IoT Enabling Technologies• End-User Devices, Sensors and Specialized Hardware Devices• Communication and Networking Technologies• IoT Software Technologies and Algorithms• IoT Data and Signal Processing Technologies• IoT and Big Data, Machine Learning and Artificial Intelligence• IoT High Level Architecture Overview• Node, Network, Communication and Access Layers• Operation and Management Layer• Service Enablement and Aggregation layer• IoT Security and Privacy ChallengesDuration: 1 Day

• Workbook • Presentation Booklet • Self-Study Guide• Mind Map Poster• Flashcards• Audio Tutor Recordings

Fundamental IoTIoTMODULE

01CERTIFICATIONThis course is part of an accreditation program through which the IoT Specialist Certification can be achieved.

EXAMAttaining the IoT Specialist Certification requires a passing grade on a single exam that encompasses the topics covered in Modules 1, 2 and 3.




This course provides a drill-down into key areas of IoT technology architecture and enabling technologies. A range of architectural models and layers are covered, along with core mechanisms pertaining to data collection, communication and device-related functions. Various relevant communication layers and protocols are also addressed, together with device and discovery functions.

The following primary topics are covered:• Understanding the IoT Architecture Approach• IoT Business, Functional and Technology Architectures• IoT Application, Protocol, Data and Analytics Architectures• IoT Device Specifications, Types, and Access• IoT Device Data Store and Transfer Capabilities • IoT Data Collection Mechanics• SQL RDBMS, NoSQL and Time Series Data• Heterogeneous, Cloud and External Data• IoT Communication Mechanisms• Human-to-Machine, Machine-to-Human, Machine-to-Machine Interactions• IoT Communication Channels and Protocol Technologies• Cellular, WiFi, LoRaWAN, Satellite and Geo-sensing • ZigBee LAN, Bluetooth, Z Wave, Gateways and Firewalls• IoT Device or Service Discovery• Bluetooth Beacon, WiFi Aware and Physical Web• Open Hybrid, Shazam and Chrips• IoT Communication and Networking Protocols• IoT Data Link Layer Protocols and OSI• IoT Network Layer Protocols and Encapsulations• IoT Session Layer and Management Protocols• IoT Learning Principles including Learning Lifecycle, Predictions, Patterns• Clustering and Dynamic Machine LearningDuration: 1 Day


IoT Technology and Architecture Concepts

IoTMODULE

02CERTIFICATIONThis course is part of an accreditation program through which the IoT Specialist Certification can be achieved.





This course module presents participants with a series of exercises and problems that are designed to test their ability to apply their knowledge of topics covered in previous courses. Completing this lab will help highlight areas that require further attention and will help prove hands-on proficiency in IoT concepts, technologies, architecture models and devices, as they are applied and combined to solve real-world problems.


Duration: 1 Day


IoTMODULE

03IoT Technology Lab

CERTIFICATIONThis course is part of an accreditation program through which the IoT Specialist Certification can be achieved.





This course provides comprehensive coverage of containerization models, technologies, mechanisms and environments. How the utilization of containers impacts both the technology and business of an organization are covered, along with many technical features, characteristics and deployment environments.

The primary topics covered by this course are:• A Brief History of Containerization• Traditional Linux Containers and the Evolution of Contemporary Containers• Containers vs. Virtual Machines and Server Virtualization• LXC/LDX, Docker and Kubernetes• Technical and Business Benefits and Challenges of using Containers• Fundamental Container Architectural Models• Container Engines, Build Files and Images• Cloud-based Containers and Container Pods• Fundamental Container Scalability and Availability• Container Configuration Management• Containers and Immutable Infrastructure Resources• Containers and Infrastructure as Code (IaC) and Configuration as Code (CaC)• Containerizing Stateful Applications• Containers and Namespaces• Fundamental Containerization Patterns and Mechanisms• Rich Containers and Serverless Deployment• Container Chains and Sidecars• Application Mobility with Containers• How Containers Relate to and Support Microservices and Machine Learning• Utilizing Containers with DevOps and CI/CDDuration: 1 Day

• Workbook • Presentation Booklet• Self-Study Guide • Mind Map Poster• Flashcards• Audio Tutor Recordings

CONTAINER IZATIONMODULE

01Fundamental Containerization

CERTIFICATIONThis course is part of an accreditation program through which the Containerization Specialist Certification can be achieved.

EXAMAttaining the Containerization Specialist Certification requires a passing grade on a single exam that encompasses the topics covered in Modules 1, 2 and 3.




This course provides a deep-dive into containerization architectures, hosting models, deployment models and utilization by services and applications. Numerous advanced topics are covered, including high performance requirements, clustering, security and lifecycle management.

The following primary topics are covered:• Hyper Containers and Containers Deployment Models• Customizing and Distributing Container Images• Container Image Version Control• Advanced Container Architectural Models• Container Execution Environments• Container Networking Model and Overlay Networking• Managing and Controlling Container Traffic Types• Container Storage Management and Shared Volume Management• Container Configuration Descriptor, Runtime Management and

Volatile Configurations• Container Clustering and Scalable Cluster Architectures• Container Proxies and APIs• Container Orchestration and Service Composition• High-Availability Containers and Advanced Container Scalability• Self-Healing Applications with Containers• Container Security Considerations and Digital Certificates• Container Lifecycle Management and Monitoring Containers• Container Backup and Recovery• Advanced Containerization Patterns and Mechanisms• Single-Node Multi-Containers and Multi-Container Isolation Control• Leader Node Election and Micro Scatter GatherDuration: 1 Day



02Advanced Containerization






This course module presents participants with a series of exercises and problems that are designed to test their ability to apply their knowledge of topics covered in previous courses. Completing this lab will help highlight areas that require further attention and will help prove hands-on proficiency in containerization concepts, technologies, architecture models and pattern application, as they are utilized and combined to solve real-world problems.


Duration: 1 Day



03Containerization Lab






This course provides a comprehensive overview of machine learning by covering key algorithms, functions and components of machine learning systems, along with common real-world demands, such as scalability, runtime processing requirements and utilization by search engines. Also covered are the relationships between machine learning systems and deep learning systems and artificial intelligence.

The primary topics covered by this course are:• A Brief History of Machine Learning• Understanding Machine Learning and Deep Learning• Benefits and Challenges of Machine Learning• Machine Learning Languages• Machine Learning and Data Science, Artificial Intelligence• Machine Learning for Data Mining and Pattern Recognition• Machine Learning for Recommendation Systems and Match Making• Natural Language Processing (NLP) and Search Engines• Supervised, Unsupervised and Semi-Supervised Learning• Reinforcement Learning• Open Source and Proprietary Machine Learning Frameworks• HPC (High Performance Computing)• Machine Learning Libraries and Scalability Dimensions• Machine Learning Architectures and Algorithms• Data Processing with Machine Learning• Decision Trees and Regression• Decision Tree Algorithm and Classification and Regression Tree (CART)• Iterative Dichotomiser 3 (ID3) and C4.5/C5.0• Chi-squared Automatic Interaction Detection (CHAID), Decision Stump and M5• Conditional Decision Trees• Linear, Logistic, Stepwise and Ordinary Least Squares Regression (OLSR)• Multivariate Adaptive Regression Splines (MARS) and Locally Estimated

Scatterplot Smoothing (LOESS)Duration: 1 Day

• Workbook • Presentation Booklet • Self-Study Guide • Mind Map Poster• Flashcards• Audio Tutor Recordings

MACHINELEARNING

MODULE

01Fundamental Machine Learning

CERTIFICATIONThis course is part of an accreditation program through which the Machine Learning Specialist Certification can be achieved.

EXAMAttaining the Machine Learning Specialist Certification requires a passing grade on a single exam that encompasses the topics covered in Modules 1, 2 and 3.




This course delves into the many aspects, algorithms and models of contemporary machine learning environments, including analysis techniques, system design and processing considerations, constructing and working with models and key principles.

The following primary topics are covered:• Understanding Machine Learning Algorithms• Key Machine Learning Principles• Machine Learning System Design• Classification (Logistic Regression, K-Nearest Neighbors (K-NN), Support Vector

Machine (SVM), Kernel SVM, Decision Tree and Random Forest Classification)• Clustering (K-Means, K-Medians, Expectation Maximization,

Hierarchical Clustering)• Rule Systems (OneR, ZeroR, Cubist)• Repeated Incremental Pruning to Procedure Error Reduction (RIPPER)• Improving Results Accuracy with Dimension Reduction (PCA, PCR, PLSR, Sammon

Mapping, Projection Pursuit and Multidimensional Scaling) (MDS)• Linear, Mixture, Quadratic and Flexible Discriminant Analyses• Solving Classification and Regression Problems using Bayesian Models (Naive

Bayes, Gaussian and Multinomial, AODE, BN and BBN)• Constructing Hypotheses using Instance-based Models (kNN, LVQ, SOM

and LWL)• Building Artificial Neural Network Constructs with Deep Learning (DBM, DBN,

CNN and Stacked Auto-Encoders)• Constructing Machine Learning Models using Neural Networks (Perceptron,

Back-Propagation, Hopfield Network, RBFN)• Combining Independently Trained Models and Generating Predictions using

Ensemble Models (Bagging, AdaBoost, Blending, GBM, GBRT and Random Forest)

• Solving Overfitting Problems with Regulation Models (Ridge Regression, LASSO, Elastic Net and LARS)

Duration: 1 Day


MACHINELEARNING

MODULE

02Advanced Machine Learning






This course module presents participants with a series of exercises and problems that are designed to test their ability to apply their knowledge of topics covered in previous courses. Completing this lab will help highlight areas that require further attention and will further prove proficiency in machine learning systems and techniques, as they are applied and combined to solve real-world problems.


Duration: 1 Day


MACHINELEARNING

MODULE

03Machine Learning Lab






This course covers foundational AI topics and concepts and provides an understanding of essential AI techniques, the basics of neural networks and fundamental neural network architectural layers.

The primary topics covered by this course are:• A Brief History of AI and Synthetic Intelligence• AI in Different Forms and Shapes• Modern AI and Cognitive AI• The Evolution of AI and Machine Learning• AI, Machine Learning and Deep Learning• Artificial Intelligence and Business Intelligence• Cybernetics and Brain Simulation• Symbolic, Sub-Symbolic and Statistical• Language Understanding, Learning and Adaptive Systems and Problem Solving• Computer Visions, Pattern Recognition, Expert Systems and Robotics• Natural Language Processing (NLP) and Speech Recognition• Hybrid Intelligent Systems• Key Principles of Artificial Intelligence• Frictionless Integration and Fault Tolerance Model Integration• Types of AI (Narrow AI, Artificial General Intelligence (AGI)

and Superintelligence)• Artificial Intelligence Techniques• Heuristics and Support Vector Machines• Artificial Neural Networks and Markov Decision Process• Understanding Artificial Neural Networks• Neural Network Layers (Input, Output and Hidden)• Artificial Neural Network Components (Neurons, Connections, Weights,

Propagation Functions and Learning Rules)• Training Neural Networks and Neural Network Activation FunctionsDuration: 1 Day


ARTIFICIALINTELLIGENCE

MODULE

01Fundamental Artificial Intelligence

CERTIFICATIONThis course is part of an accreditation program through which the Artificial Intelligence Specialist Certification can be achieved.

EXAMAttaining the Artificial Intelligence Specialist Certification requires a passing grade on a single exam that encompasses the topics covered in Modules 1, 2 and 3.




MODULE

02Advanced Artificial Intelligence


This course covers important areas of AI application that further delve into the relationships of AI with machine learning and deep learning, as well as the relationship between reinforcement learning and artificial learning. Also provided is comprehensive coverage of neural networks, including different neural network architectural models.

The following primary topics are covered:• Understanding the Inter-relationships of AI, Machine Learning and Deep Learning• Continuous Learning and Reinforcement Learning• Building AI, General Intelligence, Reasoning and Knowledge Representation• Motion and Manipulation, Social Intelligence and Creativity• AI Design (Value Creation, Value Realization and Defensibility)• AI Architecture Models and Design Patterns• AI Mechanisms (AI Complete, AI Box, Percept, Rule-based System, etc.)• Computational Humor, Soft Computing and Description Logic• Understanding and Working with Neural Network Architectures• Input/Output Cells, Key Cells and Architectural Layers• Fundamental Neural Network Architectures (P, FF, RBF, DFF, RBM, AE, SAE, etc.)• Recurrent Cell-based Architectures (RNN, ESN)• Influenced by Hidden Cells (VAE, DAE)• Memory-Influenced Architectures (LSTM, GRU)• Parabolicity Cell-Driven Architectures (MC, BM, DBN)• Backfed Cell-based Architecture (Hopfield Network)• Pool and Kernel Influenced Architectures (DCN, DN, DCIGN)• Influenced by Match Input (Generative Adversarial Networks)• Influenced by Spiking Hidden Cells (Liquid State Machine)• Influenced by Hidden Cells (ELM, DRN, KN, SVM, NTM, etc.)Duration: 1 Day







MODULE

03Artificial Intelligence Lab


This course module presents participants with a series of exercises and problems that are designed to test their ability to apply their knowledge of topics covered in previous courses. Completing this lab will help highlight areas that require further attention and will further prove proficiency in AI, machine learning and deep learning systems and neural network architectures, as they are applied and combined to solve real-world problems.


Duration: 1 Day





CERTIFICATION TRACKS

MODULE 01 Fundamental DevOps

MODULE 02 DevOps in Practice

MODULE 03 DevOps Lab

EXAM DO90.01

MODULE 01 Fundamental Blockchain

MODULE 02 Blockchain Technology Concepts

MODULE 03 Blockchain Technology Lab

EXAM BC90.01

MODULE 01 Fundamental IoT

MODULE 02 IoT Technology and Architecture Concepts

MODULE 03 IoT Technology Lab

EXAM IT90.01

EXAM CN90.01

MODULE 01 Fundamental Containerization

MODULE 02 Advanced Containerization

MODULE 03 Containerization Lab

EXAM ML90.01

MODULE 01 Fundamental Machine Learning

MODULE 02 Advanced Machine Learning

MODULE 03 Machine Learning Lab

EXAM AI90.01

MODULE 01 Fundamental Artificial Intelligence

MODULE 02 Advanced Artificial Intelligence

MODULE 03 Artificial Intelligence Lab

The Next-Gen IT Academy from Arcitura offers a specialist certification for each topic area in the curriculum. The specialist certification is achieved by passing a single exam. Honors certifications are issued to those who achieve a grade that is 10 percentage points above the passing grade for a given exam.

There are different options available to prepare for an exam. As shown below, each exam is associated with three one-day course modules. The courses can be completed by obtaining eLearning and full-color printed study kits from the Arcitura on-line store (www.arcitura.com/store). Alternatively, the courses can be completed via on-site and virtual workshops.

Both private and public workshops may be available. Private workshops are delivered by Certified Trainers on-site at your location and can include on-site exam delivery. Public workshops are open for public registrations and are also delivered by Certified Trainers. For private workshops that include the delivery of the exam on-site, the certification track can be completed and the attainment of the certification can be achieved upon completion of the workshop.

NEXT-GEN IT - arcitura.com · Attaining a passing grade on this exam achieves a specialist...

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