IBM Cognitive Class
Reactive Architecture: Reactive Microservices
When we use the term Reactive, we are usually talking about Reactive Microservices. But what is a microservice? And how can we make it Reactive? This course will explore the difference between monoliths and microservices and show the journey to making a system Reactive. About This Course When we use the term Reactive, we are usually talking about Reactive Microservices. But what is a microservice? And how can we make it Reactive? This course will explore the difference between monoliths and microservices and show the journey to making a system Reactive. What will I get after passing this course? You will receive a completion certificate. Course Syllabus The Software Spectrum An introduction the Monolith to Microservices sprectrum The bascis of choosing between a Monolith or Microservice software approach Monoliths Some Monoliths can be Big Balls of Mud Characteristics of Monoliths Advantages and Disadvantages of Monoliths Microservices Service Oriented Architecture Characteristics of Microservices Responsibilities of Microservices Advantages and Disadvantages of Microservices Decomposing a Monolith into Microservices Principles of Isolation State, Space, Time and Failure Monoliths and the Principles of Isolation Microservices and the Principles of Isolation Isolation Techniques Bulkheading Circuit Breakers Message Driven Architecture Autonomy Gateway Services General Information This course is self-paced. It can be taken at any time. It can be taken as many times as you wish. Recommended skills prior to taking this course Experience in the design of software systems. Grading scheme The minimum passing mark for the course is 75%, where the review questions are worth 40%, the case study exercises are worth 30% and the final exam is worth 30% of the course mark. You have 1 attempt to take the final exam with multiple attempts per question. Requirements You should either understand all the concepts or have taken Reactive Architecture: Introduction to Reactive Systems and Reactive Architecture: Domain Driven Design
GratisFundamentals of Encryption & Quantum-Safe Techniques
In this short course, learn about encryption, quantum computers, and quantum-safe cryptography to secure your IT infrastructure against emerging threats. Understand types of encryption, key management and consequences when encryption is cracked. Prepare for the quantum era and become ready to safeguard your data with IBM Quantum Safe technology. Welcome to our comprehensive course on Encryption and Quantum-Safe Cryptography! Understand the threats to your data in the imminent quantum computing revolution. This course will introduce you to the need for, and widespread use of encryption. It will examine the history of encryption (AKA cryptography), the types of encryption, and most importantly describe encryption keys and key management. It will then introduce Quantum computers and how they will change the world, for the good and bad, including how they are expected to break today’s encryption. It will explain the current state of, Quantum-safe encryption algorithms and then explain the steps needed, and best practices for organizations to become Quantum-safe. Course Syllabus Welcome Course introduction About the course Grading scheme Module 1: Introduction to encryption Securing your IT infrastructure against today’s and tomorrow’s computers and criminals Types of encryption The history of encryption and IBM's contributions The consequences when encryption is cracked Encryption keys and key management Module quiz Module 2: The Quantum era and Quantum computers The Quantum era has officially begun Quantum computers and their impact on the world Quantum-safe cryptography/encryption Get ready for the Quantum era Module quiz Module 3: Safeguard your data with Quantum-safe cryptography The time to start is now Prepare - Catalog and classify data Discovery - Build a cryptographic inventory Transform - Create cryptographic agility Visibility - Observe and adapt Module quiz IBM’s Quantum-safe solution Course Summary Final Quiz General Information This class contains videos and presentations with full, in-depth speaker notes for every section. There will be a short quiz at the end of each section so you can assess your knowledge prior to going to the next section. At the end there will be a final quiz covering the content in the decks, both the slides and speaker notes, as well as the videos. Recommended Skills Prior to Taking this Course This class is meant for those who are interested in learning more about the need for moving to Quantum-safe cryptography to keep their data safe into the future. A general knowledge of systems and applications is assumed, but the key concepts of encryption and Quantum computers are explained in the class.
GratisKubernetes Operators Advanced
This course covers Golang operator reconciliation, use of OLM, and use of Scorecard. Reconciliation Operators mimic the behavior of core Kubernetes components in many ways, chief among them the process of reconciliation. When a user creates an instance of your operator's type, one or more controllers is responsible for creating that desired state. This course covers the creation of a basic reconcile loop in Golang to create a simple Memcached operator, similar to the Golang operator from the Intermediate course. Operator Lifecycle Manager (OLM) OLM is a tool for installing and upgrading operators on a Kubernetes cluster. Operator authors create a Bundle for each version of their operator, which is then published and consumed by OLM. This course covers the creation of a bundle for the simple Memcached operator from above, and then covers the upgrade case by adding a new field, implements migration via conversion webhook, creates a new bundle, and upgrades the deployed operator in-place. Scorecard Scorecard is an Operator-sdk command for scaffolding and running test frameworks on a deployed operator. This course first covers using Scorecard to automatically generate and run a basic verification test suite. Then, it shows how to create your own custom test suite with a non-trivial unit test for the above simple Memcached operator. Learning Objectives In this course, you will learn about: - How reconciliation logic works in Kubernetes controllers - Constructing your own reconcile loop in a Golang operator - Getting an introduction to the Operator Lifecycle Manager (OLM) - Deploying and managing an operator using OLM - Using Scorecard to create a test suite for your operator Course Syllabus Module 1 - Golang Operator Reconciliation Overview of Kubernetes controller reconciliation Kubernetes' declarative API Explanation of Memcached operator reconciliation via finite state diagram Hands-on Lab Build a simple Golang operator with Operator SDK Write reconciliation loop using controller-runtime library Module 2 - Operator Lifecycle Manager Overview of Operator Lifecycle Manager (OLM) What is OLM? What is an operator bundle Hands-on Lab 1 Install OLM on your cluster Package a Golang operator with OLM Deploy it to the cluster using OLM Hands-on Lab 2 Update operator with a breaking change Create a conversion webhook to manage the upgrade Use OLM to upgrade operator in-place Module 3 - Testing your operator with Scorecard Overview of Scorecard, an Operator SDK tool for building test images for a deployed operator Hands-on Lab Build a Scorecard test suite for Memcached operator Write an integration test for Memcached operator Course Prerequisites Basic familiarity with Kubernetes, including using kubectl to create resources on a Kubernetes cluster. You should know how to create, update, and delete Kubernetes resources such as Pods and Services. Understanding of the basic construction, deployment, and use of operators Completion of the Intermediate course on Kubernetes operators The requirements of the hands-on labs are as follows: Requirements for all hands-on labs Operator SDK v1.5.0+ installed Kubectl v1.17.0+ installed Admin access to a Kubernetes cluster. Note that the version of Operator Lifecycle Manger currently(8/23/21) on IMB Cloud's Kubernetes Service is incompatible with this tutorial. For running a cluster locally, see the Minikube docs. Docker v3.2.2+ installed Access to a Docker image repository such as Docker Hub or quay.io Golang v1.16.0+ installed Module 1 Lab requirements No additional requirements Module 2 Lab requirements Operator Lifecycle Manager installed on your Kubernetes cluster. The Operator-sdk command operator-sdk olm install will install a compatible version of OLM on your cluster. Note that currently(8/23/21) the version of OLM installed on IBM Cloud IKS clusters is incompatible with this tutorial. Module 3 Lab requirements No additional requirements
GratisMathematical Optimization for Business Problems
This training provides the necessary fundamentals of mathematical programming and useful tips for good modelling practice in order to construct simple optimization models. LEARNING OBJECTIVES In this training, you will explore several aspects of mathematical programing to start learning more about constructing optimization models using IBM Decision Optimization technology, including: Basic terminology: operations research, mathematical optimization, and mathematical programming Basic elements of optimization models: data, decision variables, objective functions, and constraints Different types of solution: feasible, optimal, infeasible, and unbounded Mathematical programming techniques for optimization: Linear Programming, Integer Programming, Mixed Integer Programming, and Quadratic Programming Algorithms used for solving continuous linear programming problems: simplex, dual simplex, and barrier Important mathematical programming concepts: sparsity, uncertainty, periodicity, network structure, convexity, piecewise linear and nonlinear These concepts are illustrated by concrete examples, including a production problem and different network models. Syllabus Module 1 - The Big Picture What is Operations Research? What is Optimization? Optimization Models Module 2 - Linear Programming Introduction to Linear Programming A Production Problem : Part 1 - Writing the model A Production Problem : Part 2 - Finding a solution A Production Problem : Part 3 - From feasibility to unboundedness Algorithms for Solving Linear Programs : Part 1 - The Simplex and Dual Simplex Algorithm Algorithms for Solving Linear Programs : Part 2 - The Simplex and Barrier methods Module 3 - Network Models Introduction to Network Models The Transportation Problem The Transshipment Problem The Assignment Problem The Shortest Path Problem Critical Path Analysis Module 4 - Beyond Simple LP Nonlinearity and Convexity Piecewise Linear Programming Integer Programming The Branch and Bound Method Quadratic Programming Module 5 - Modelling Practice Modelling in the Real World The Importance of Sparsity Tips for Better Models
GratisIntroduction to Quantum Computing
This course introduces you to the strange world of quantum computing. You’ll learn about quantum concepts including superposition, entanglement, and uncertainty. You’ll also perform “labs” to get a first-hand look at these concepts. At the end, there’s a quiz to test your knowledge. Course Syllabus The Hall of Wonders The Superposition Lab The Entanglement Lab The Uncertainty Lab Return to the Hall of Wonders Show what you know! Recommended skills prior to taking this course This is a course for beginners. You do not need any special skills to succeed with this course. Grading scheme The minimum passing mark for the course is 80%, the final exam is worth 100% of the course mark. You have 1 attempt to take the exam with multiple attempts per question
GratisDigital Analytics & Regression
This course uses a case study approach to take you through the end to end process of identifying a business objective, designing the model to address it, sourcing the data and ultimately arriving at the insights. When you complete this course, you can apply these methods and principles in a variety of contexts, with big, medium or small data. About This Course Data Science is like triathlon. Programming is cycling, by far the biggest investment is required in hardware and software. Running is domain expertise and communication skills and, swimming is mathematics, statistics and modelling. There are competitions in each of these disciplines cycling, running and swimming (and there always will be), but the need for super athletes who can do all 3 is growing. An athlete who is brilliant at one discipline can learn the other two and succeed in triathlon. No matter your core discipline(s), this course will take you through a short triathlon. You will define a business problem, establish the data required to solve it, you will write scripts in R programming language to build a model to give you insights and you will learn to present your findings in a business format to a business audience. Requirements Students will benefit from having a basic understanding of Statistics. Students with a basic knowledge of how Search Engines work may have a better appreciation for the case study context however, this is not a requirement.
GratisMapReduce and YARN
Apache Hadoop is one of the most popular tools for big data processing. It has been successfully deployed in production by many companies for several years. Though Hadoop is considered a reliable, scalable, and cost-effective solution, it is constantly being improved by a large community of developers. As a result, the 2.0 version offers several revolutionary features, including Yet Another Resource Negotiator (YARN), HDFS Federation, and high availability, which make the Hadoop cluster much more efficient, powerful, and reliable. The most serious limitations of classical MapReduce are primarily related to scalability, resource utilization, and the support of workloads different from MapReduce. In the MapReduce framework, the job execution is controlled by two types of processes: a single master process called JobTracker and a number of subordinate processes called TaskTrackers. Apache Hadoop 2.0 includes YARN, which separates the resource management and processing components. The YARN-based architecture is not constrained to MapReduce. In YARN, MapReduce is simply degraded to a role of a distributed application (but still a very popular and useful one) and is now called MRv2. MRv2 is simply the re-implementation of the classic MapReduce engine, now called MRv1, which runs on top of YARN. The course reviews MapReduce and provides insight into the design and implementation of YARN: ResourceManager instead of a cluster manager, ApplicationMaster instead of a dedicated and short-lived JobTracker, NodeManager instead of TaskTracker, a distributed application instead of a MapReduce job. Course Syllabus Lesson 1: Introduction to MapReduce and YARN Describe the MapReduce model v1 — this is the “classic” version that comes with Hadoop 1 List the limitations of both Hadoop 1 and MapReduce 1 Review the Java code required to handle the Mapper class, the Reducer class, and the program driver needed to access MapReduce Describe the YARN model, including the features of YARN and how a YARN program is run, and Compare “YARN / Hadoop 2 / MR2” versus “Hadoop 1 with MR1” Lesson 2:Issues with/Limitations of Hadoop v1 & MapReduce v1 List the limitations of MapReduce v1 and the need for MR v2 / YARN Describe MR2 / YARN processing Lesson 3: The Architecture of YARN Understand the high level architecture of YARN Configuring, monitoring, and running applications in the YARN environment Recommended skills prior to taking this course Know some basic Linux administration and commands Grading scheme The minimum passing mark for the course is 60%, where the review questions are worth 40% and the final exam is worth 60% of the course mark. You have 1 attempt to take the exam with multiple attempts per question.
GratisAccessing Hadoop Data Using Hive
Hive is a data warehousing tool built on top of Hadoop. Learn how to easily query and analyze your Big Data projects with this course on Apache Hive. About This Course Writing MapReduce programs to analyze your Big Data can get complex. Hive can help make querying your data much easier. Apache Hive, first created at Facebook, is a data warehouse system for Hadoop that facilitates easy data summarization, ad-hoc queries, and the analysis of large datasets stored in Hadoop compatible file systems. Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL. This course will get you started so that you can use Hive for Data Warehousing tasks on your Big Data projects. What will I get after passing this course? You will receive a completion certificate. Course Syllabus Lesson 1 - Introduction to Hive Describe what Hive is, what it’s used for and how it compares to other similar technologies Describe the Hive architecture Describe the main components of Hive List interesting ways others are using Hive Lesson 2 - Hive DDL Create databases and tables in Hive, while using a variety of different Data Types Run a variety of different DDL commands Use Partitioning to improve performance of Hive queries Create Managed and External tables in Hive Lesson 3 - Hive DML Load data into Hive Export data out of Hive Run a variety of different HiveQL DML queries Lesson 4 - Hive Operators and Functions Use a variety of Hive Operators in your queries Utilize Hive’s Built-in Functions Explain ways to extend Hive functionality
GratisStatistics 101
Welcome to the Statistics 101 course, taught by Murtaza Haider, Assistant Professor at Ryerson University. Statistics is one of the most challenging topics to learn, but Murtaza brings a gentle introduction to statistics in practice. Learn about descriptive statistics, variance, probability, correlation, and data visualization. This course ends with a fully-guided statistics exercise exploring the “hot” topic of: do good looking professors get better teaching evaluations? A free trial of SPSS Statistics is included in this course. Split into five modules, this is a beginner's course covering the fundamentals of statistics. Start with mean, mode, and median. Then learn about standard deviation using examples from basketball. Learn about probability with dice. Learn what it means to group data by categorical variables, and how you can transform your data into appropriate graphs and charts. In the final module, using an open dataset, learn whether good looking professors indeed get better teaching evalutions. This course is taught using SPSS Statistics. No prior experience necesssary. A free trial is available through this course, available here: SPSS Statistics (Free Trial). COURSE SYLLABUS Module 1 - Welcome to Statistics! Welcome to Statistics Data visualization All about data SPSS Statistics SPSS Statistics in 5 minutes Lab exercises Module 2 - Basic Statistics Types of data Measures of dispersion Mean, median, mode Statistics by data type Probability Lab exercises Module 3 - Summarizing data Statistics by groups Visualization of group statistics Pivoting Cross-tabulations Correlation Lab exercises Module 4- Data Visualization Visualization fundamentals Descriptive and statistical charts Scatterplots Statistical charts Time series charts Lab exercises Module 5 - Does Beauty Pay? Does Beauty Pay? Weighted means, standard deviations Data wrangling Descriptive Statistics Reproducibility with syntax in SPSS Statistics Lab exercises
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