Installation scripts for new IBM Cloud Developer CLI Plugin and it's dependencies.

A curated list of awesome IBM Cloud SDKs, open source repositories, tools, blogs and other resources.

An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so

An Electron-based development tool for IBM Cloud Functions and Composer.

# Awesome Keycloak [](https://github.com/sindresorhus/awesome) # [<img src="https://www.keycloak.org/resources/images/keycloak_logo_480x108.png">](https://github.com/thomasdarimont/awesome-keycloak) > Carefully curated list of awesome Keycloak resources. A curated list of resources for learning about the Open Source Identity and Access Management solution Keycloak. Contains books, websites, blog posts, links to github Repositories. # Contributing Contributions welcome. Add links through pull requests or create an issue to start a discussion. [Please refer to the contributing guide for details](CONTRIBUTING.md). # Contents * [General](#general) * [Documentation](#docs) * [Keycloak Website](http://www.keycloak.org) * [Current Documentation](http://www.keycloak.org/documentation.html) * [Archived Documentation](http://www.keycloak.org/documentation-archive.html) * [Mailing Lists](#mailing-lists) * [User Mailing List](#user-mailing-list) * [Developer Mailing List](#dev-mailing-list) * [Mailing List Search](#mailing-list-search) * [Books](#books) * [Articles](#articles) * [Talks](#talks) * [Presentations](#presentations) * [Video Playlists](#video-playlists) * [Community Extensions](#community-extensions) * [Integrations](#integrations) * [Themes](#themes) * [Docker](#docker) * [Deployment Examples](#deployment-examples) * [Example Projects](#example-projects) * [Benchmarks](#benchmarks) * [Help](#help) * [Commercial Offerings](#commercial-offerings) * [Miscellaneous](#miscellaneous) # General ## Documentation * [Keycloak Website](http://www.keycloak.org/) * [Current Documentation](http://www.keycloak.org/documentation.html) * [Archived Documentation](http://www.keycloak.org/documentation-archive.html) * [Product Documentation for Red Hat Single Sign-On](https://access.redhat.com/documentation/en/red-hat-single-sign-on/) ## Discussion Groups and Mailing Lists * [Keycloak Users Google Group](https://groups.google.com/forum/#!forum/keycloak-user) * [Keycloak Developers Google Group](https://groups.google.com/forum/#!forum/keycloak-dev) * [Keycloak Discourse Group](https://keycloak.discourse.group/) * [Keycloak Developer Chat](https://keycloak.zulipchat.com) * [Inactive - User Mailing List](https://lists.jboss.org/mailman/listinfo/keycloak-user) * [Inactive - Developer Mailing List](https://lists.jboss.org/mailman/listinfo/keycloak-dev) * [Mailing List Search](http://www.keycloak.org/search) * [Keycloak Subreddit](https://www.reddit.com/r/keycloak) ## Books * [Keycloak - Identity and Access Management for Modern Applications](https://www.packtpub.com/product/keycloak-identity-and-access-management-for-modern-applications/9781800562493) ## Articles * [How to get Keycloak working with Docker](https://www.ivonet.nl/2015/05/23/Keycloak-Docker/) * [Single-Sign-On for Microservices and/or Java EE applications with Keycloak SSO](http://www.n-k.de/2016/06/keycloak-sso-for-microservices.html) * [Keycloak Admin Client(s) - multiple ways to manage your SSO system](http://www.n-k.de/2016/08/keycloak-admin-client.html) * [How to get the AccessToken of Keycloak in Spring Boot and/or Java EE](http://www.n-k.de/2016/05/how-to-get-accesstoken-from-keycloak-springboot-javaee.html) * [JWT authentication with Vert.x, Keycloak and Angular 2](http://paulbakker.io/java/jwt-keycloak-angular2/) * [Authenticating via Kerberos with Keycloak and Windows 2008 Active Directory](http://matthewcasperson.blogspot.de/2015/07/authenticating-via-kerberos-with.html) * [Deploying Keycloak with Ansible](https://adam.younglogic.com/2016/01/deploying-keycloak-via-ansible/) * [Easily secure your Spring Boot applications with Keycloak](https://developers.redhat.com/blog/2017/05/25/easily-secure-your-spring-boot-applications-with-keycloak/) * [How Red Hat re-designed its Single Sign On (SSO) architecture, and why](https://developers.redhat.com/blog/2016/10/04/how-red-hat-re-designed-its-single-sign-on-sso-architecture-and-why/) * [OAuth2, JWT, Open-ID Connect and other confusing things](http://giallone.blogspot.de/2017/06/oath2.html) * [X509 Authentication with Keycloak and JBoss Fuse](https://sjhiggs.github.io/fuse/sso/x509/smartcard/2017/03/29/fuse-hawtio-keycloak.html) * [Running Keycloak on OpenShift 3](https://medium.com/@sbose78/running-keycloak-on-openshift-3-8d195c0daaf6) * [Introducing Keycloak for Identity and Access Management](https://www.thomasvitale.com/introducing-keycloak-identity-access-management/) * [Keycloak Basic Configuration for Authentication and Authorisation](https://www.thomasvitale.com/keycloak-configuration-authentication-authorisation/) * [Keycloak on OpenShift Origin](https://medium.com/@james_devcomb/keycloak-on-openshift-origin-ee81d01dac97) * [Identity Management, One-Time-Passwords and Two-Factor-Auth with Spring Boot and Keycloak](http://www.hascode.com/2017/11/identity-management-one-time-passwords-and-two-factor-auth-with-spring-boot-and-keycloak/) * [Keycloak Identity Brokering with Openshift](https://developers.redhat.com/blog/2017/12/06/keycloak-identity-brokering-openshift/) * [OpenID Connect Identity Brokering with Red Hat Single Sign-On](https://developers.redhat.com/blog/2017/10/18/openid-connect-identity-brokering-red-hat-single-sign/) * [Authentication & user management is hard](https://eclipsesource.com/blogs/2018/01/11/authenticating-reverse-proxy-with-keycloak/) * [Securing Nginx with Keycloak](https://edhull.co.uk/blog/2018-06-06/keycloak-nginx) * [Secure kibana dashboards using keycloak](https://aboullaite.me/secure-kibana-keycloak/) * [Configuring NGINX for OAuth/OpenID Connect SSO with Keycloak/Red Hat SSO](https://developers.redhat.com/blog/2018/10/08/configuring-nginx-keycloak-oauth-oidc/) * [Keycloak Clustering Setup and Configuration Examples](https://github.com/fit2anything/keycloak-cluster-setup-and-configuration) * [MicroProfile JWT with Keycloak](https://kodnito.com/posts/microprofile-jwt-with-keycloak/) * [Keycloak Essentials](https://medium.com/keycloak/keycloak-essentials-86254b2f1872) * [SSO-session failover with Keycloak and AWS S3](https://medium.com/@georgijsr/sso-session-failover-with-keycloak-and-aws-s3-e0b1db985e12) * [KTOR and Keycloak: authentication with OpenId](https://medium.com/slickteam/ktor-and-keycloak-authentication-with-openid-ecd415d7a62e) * [Keycloak: Core concepts of open source identity and access management](https://developers.redhat.com/blog/2019/12/11/keycloak-core-concepts-of-open-source-identity-and-access-management) * [Who am I? Keycloak Impersonation API](https://blog.softwaremill.com/who-am-i-keycloak-impersonation-api-bfe7acaf051a) * [Setup Keycloak Server on Ubuntu 18.04](https://medium.com/@hasnat.saeed/setup-keycloak-server-on-ubuntu-18-04-ed8c7c79a2d9) * [Getting started with Keycloak](https://robferguson.org/blog/2019/12/24/getting-started-with-keycloak/) * [Angular, OpenID Connect and Keycloak](https://robferguson.org/blog/2019/12/29/angular-openid-connect-keycloak/) * [Angular, OAuth 2.0 Scopes and Keycloak](https://robferguson.org/blog/2019/12/31/angular-oauth2-keycloak/) * [Keycloak, Flowable and OpenLDAP](https://robferguson.org/blog/2020/01/03/keycloak-flowable-and-openldap/) * [How to exchange token from an external provider to a keycloak token](https://www.mathieupassenaud.fr/token-exchange-keycloak/) * [Building an Event Listener SPI (Plugin) for Keycloak](https://dev.to/adwaitthattey/building-an-event-listener-spi-plugin-for-keycloak-2044) * [Keycloak user migration – connect your legacy authentication system to Keycloak](https://codesoapbox.dev/keycloak-user-migration/) * [Keycloak Authentication and Authorization in GraphQL](https://medium.com/@darahayes/keycloak-authentication-and-authorization-in-graphql-ad0a1685f7da) * [Kong / Konga / Keycloak: securing API through OIDC](https://github.com/d4rkstar/kong-konga-keycloak) * [KeyCloak: Custom Login theme](https://codehumsafar.wordpress.com/2018/09/11/keycloak-custom-login-theme/) * [Keycloak: Use background color instead of background image in Custom Login theme](https://codehumsafar.wordpress.com/2018/09/21/keycloak-use-background-color-instead-of-background-image-in-custom-login-theme/) * [How to turn off the Keycloak theme cache](https://keycloakthemes.com/blog/how-to-turn-off-the-keycloak-theme-cache) * [How to add a custom field to the Keycloak registration page](https://keycloakthemes.com/blog/how-to-add-custom-field-keycloak-registration-page) * [How to setup Sign in with Google using Keycloak](https://keycloakthemes.com/blog/how-to-setup-sign-in-with-google-using-keycloak) * [How to sign in users on Keycloak using Github](https://keycloakthemes.com/blog/how-to-sign-in-users-on-keycloak-using-github) * [Extending Keycloak SSO Capabilities with IBM Security Verify](https://community.ibm.com/community/user/security/blogs/jason-choi1/2020/06/10/extending-keycloak-sso-capabilities-with-ibm-secur) * [AWS SAML based User Federation using Keycloak](https://medium.com/@karanbir.tech/aws-connect-saml-based-identity-provider-using-keycloak-9b3e6d0111e6) * [AWS user account OpenID federation using Keycloak](https://medium.com/@karanbir.tech/aws-account-openid-federation-using-keycloak-40d22b952a43) * [How to Run Keycloak in HA on Kubernetes](https://blog.sighup.io/keycloak-ha-on-kubernetes/) * [How to create a Keycloak authenticator as a microservice?](https://medium.com/application-security/how-to-create-a-keycloak-authenticator-as-a-microservice-ad332e287b58) * [keycloak.ch | Installing & Running Keycloak](https://keycloak.ch/keycloak-tutorials/tutorial-1-installing-and-running-keycloak/) * [keycloak.ch | Configuring Token Exchange using the CLI](https://keycloak.ch/keycloak-tutorials/tutorial-token-exchange/) * [keycloak.ch | Configuring WebAuthn](https://keycloak.ch/keycloak-tutorials/tutorial-webauthn/) * [keycloak.ch | Configuring a SwissID integration](https://keycloak.ch/keycloak-tutorials/tutorial-swissid/) * [Getting Started with Service Accounts in Keycloak](https://medium.com/@mihirrajdixit/getting-started-with-service-accounts-in-keycloak-c8f6798a0675) * [Building cloud native apps: Identity and Access Management](https://dev.to/lukaszbudnik/building-cloud-native-apps-identity-and-access-management-1e5m) * [X.509 user certificate authentication with Red Hat’s single sign-on technology](https://developers.redhat.com/blog/2021/02/19/x-509-user-certificate-authentication-with-red-hats-single-sign-on-technology) * [Grafana OAuth with Keycloak and how to validate a JWT token](https://janikvonrotz.ch/2020/08/27/grafana-oauth-with-keycloak-and-how-to-validate-a-jwt-token/) * [How to setup a Keycloak server with external MySQL database on AWS ECS Fargate in clustered mode](https://jbjerksetmyr.medium.com/how-to-setup-a-keycloak-server-with-external-mysql-database-on-aws-ecs-fargate-in-clustered-mode-9775d01cd317) * [Extending Keycloak: adding API key authentication](http://www.zakariaamine.com/2019-06-14/extending-keycloak) * [Extending Keycloak: using a custom email sender](http://www.zakariaamine.com/2019-07-14/extending-keycloak2) * [Integrating Keycloak and OPA with Confluent](https://goraft.tech/2021/03/17/integrating-keycloak-and-opa-with-confluent.html) * [UMA 2.0 : User Managed Access - how to use it with bash](https://blog.please-open.it/uma/) ## Talks * [JDD2015 - Keycloak Open Source Identity and Access Management Solution](https://www.youtube.com/watch?v=TuEkj25lbd0) * [2015 Using Tomcat and Keycloak in an iFrame](https://www.youtube.com/watch?v=nF_lw7uIxao) * [2016 You've Got Microservices Now Secure Them](https://www.youtube.com/watch?v=SfVhqf-rMQY) * [2016 Keycloak: Open Source Single Sign On - Sebastian Rose - AOE conf (german)](https://www.youtube.com/watch?v=wbKw0Bwyne4) * [2016 Sécuriser ses applications back et front facilement avec Keycloak (french)](https://www.youtube.com/watch?v=bVidgluUcg0) * [2016 Keycloak and Red Hat Mobile Application Platform](https://www.youtube.com/watch?v=4NBgiHM5aOA) * [2016 Easily secure your Front and back applications with KeyCloak](https://www.youtube.com/watch?v=RGp4HUKikts) * [2017 Easily secure your Spring Boot applications with Keycloak - Part 1](https://developers.redhat.com/video/youtube/vpgRTPFDHAw/) * [2017 Easily secure your Spring Boot applications with Keycloak - Part 2](https://developers.redhat.com/video/youtube/O5ePCWON08Y/) * [2018 How to secure your Spring Apps with Keycloak by Thomas Darimont @ Spring I/O 2018](https://www.youtube.com/watch?v=haHFoeWUj0w) * [2018 DevNation Live | A Deep Dive into Keycloak](https://www.youtube.com/watch?v=ZxpY_zZ52kU) * [2018 IDM Europe: WSO2 Identity Server vs. Keycloak (Dmitry Kann)](https://www.youtube.com/watch?v=hnjBiGsEDoU) * [2018 JPrime|Building an effective identity and access management architecture with Keycloak (Sebastien Blanc)](https://www.youtube.com/watch?v=bMqcGkCvUVQ) * [2018 WJAX| Sichere Spring-Anwendungen mit Keycloak](https://www.youtube.com/watch?v=6Z490EMcafs) * [2019 Spring I/O | Secure your Spring Apps with Keycloak](https://www.youtube.com/watch?v=KrOd5wIkqls) * [2019 DevoxxFR | Maitriser sa gestion de l'identité avec Keycloak (L. Benoit, T. Recloux, S. Blanc)](https://www.youtube.com/watch?v=0cziL__0-K8) * [2019 DevConf | Fine - Grained Authorization with Keycloak SSO (Marek Posolda)](https://www.youtube.com/watch?v=yosg4St0iUw) * [2019 VoxxedDays Minsk | Bilding an effective identity and access management architecture with Keycloak (Sebastien Blanc)](https://www.youtube.com/watch?v=RupQWmYhrLA) * [2019 Single-Sign-On Authentifizierung mit dem Keycloak Identity Provider | jambit CoffeeTalk](https://www.youtube.com/watch?v=dnY6ORaFNY8) * [2020 Keycloak Team | Keycloak Pitch](https://www.youtube.com/watch?v=GZTN_VXjoQw) * [2020 Keycloak Team | Keycloak Overview](https://www.youtube.com/watch?v=duawSV69LDI) * [2020 Please-open.it : oauth2 dans le monde des ops (french)](https://www.youtube.com/watch?v=S-9X50QajmY) ## Presentations * [Keycloak 101](https://stevenolen.github.io/kc101-talk/#1) ## Video Playlists * [Keycloak Identity and Access Management by Łukasz Budnik](https://www.youtube.com/playlist?list=PLPZal7ksxNs0mgScrJxrggEayV-TPZ9sA) * [Keycloak by Niko Köbler](https://www.youtube.com/playlist?list=PLNn3plN7ZiaowUvKzKiJjYfWpp86u98iY) * [Keycloak Playlist by hexaDefence](https://youtu.be/35bflT_zxXA) * [Keycloak Tutorial Series by CodeLens](https://www.youtube.com/watch?v=Lr9WeIMtFow&list=PLeGNmkzI56BTjRxNGxUhh4k30FD_gy0pC) ## Clients * [Official Keycloak Node.js Admin Client](https://github.com/keycloak/keycloak-admin-client/) ("Extremely Experimental") * [Keycloak Node.js TypeScript Admin Client by Canner](https://github.com/Canner/keycloak-admin/) * [Keycloak Go Client by Cloudtrust](https://github.com/cloudtrust/keycloak-client) * [Keycloak Nest.js Admin Client by Relevant Fruit](https://github.com/relevantfruit/nestjs-keycloak-admin) ## Community Extensions * [Keycloak Extensions List](https://www.keycloak.org/extensions.html) * [Keycloak Benchmark Project](https://github.com/keycloak/keycloak-benchmark) * [Keycloak: Link IdP Login with User Provider](https://github.com/ohioit/keycloak-link-idp-with-user) * [Client Owner Manager: Control who can edit a client](https://github.com/cyclone-project/cyclone-client-registration) * [Keyloak Proxy written in Go](https://github.com/gambol99/keycloak-proxy) * [Script based ProtocolMapper extension for SAML](https://github.com/cloudtrust/keycloak-client-mappers) * [Realm export REST resource by Cloudtrust](https://github.com/cloudtrust/keycloak-export) * [Keycloak JDBC Ping Setup by moremagic](https://github.com/moremagic/keycloak-jdbc-ping) * [SMS 2 Factor Authentication for Keycloak via AWS SNS](https://github.com/nickpack/keycloak-sms-authenticator-sns) * [SMS 2 Factor Authentiation for Keycloak via SMS by Alliander](https://github.com/Alliander/keycloak-sms-authenticator) * [Identity Provider for vk.com](https://github.com/mrk08/keycloak-vk) * [CAS Protocol Support](https://github.com/Doccrazy/keycloak-protocol-cas) * [WS-FED Support](https://github.com/cloudtrust/keycloak-wsfed) * [Keycloak Discord Support](https://github.com/wadahiro/keycloak-discord) * [Keycloak Login with User Attribute](https://github.com/cnieg/keycloak-login-attribute) * [zonaut/keycloak-extensions](https://github.com/zonaut/keycloak-extensions) * [leroyguillaume/keycloak-bcrypt](https://github.com/leroyguillaume/keycloak-bcrypt) * [SPI Authenticator in Nodejs](https://www.npmjs.com/package/keycloak-rest-authenticator) * [Have I Been Pwned? Keycloak Password Policy](https://github.com/alexashley/keycloak-password-policy-have-i-been-pwned) * [Keycloak Eventlistener for Google Cloud Pub Sub](https://github.com/acesso-io/keycloak-event-listener-gcpubsub) * [Enforcing Password policy based on attributes of User Groups](https://github.com/sayedcsekuet/keycloak-user-group-based-password-policy) * [Verify Email with Link or Code by hokumski](https://github.com/hokumski/keycloak-verifyemailwithcode) * [Role-based Docker registry authentication](https://github.com/lifs-tools/keycloak-docker-role-mapper) * [SCIM for keycloak](https://github.com/Captain-P-Goldfish/scim-for-keycloak) * [Keycloak Kafka Module](https://github.com/SnuK87/keycloak-kafka) ## Integrations * [Official Keycloak Node.js Connect Adapter](https://github.com/keycloak/keycloak-nodejs-connect) * [Keycloak support for Aurelia](https://github.com/waynepennington/aurelia-keycloak) * [Keycloak OAuth2 Auth for PHP](https://github.com/stevenmaguire/oauth2-keycloak) * [Jenkins Keycloak Authentication Plugin](https://github.com/jenkinsci/keycloak-plugin) * [Meteor Keycloak Accounts](https://github.com/mxab/meteor-keycloak) * [HapiJS Keycloak Auth](https://github.com/felixheck/hapi-auth-keycloak) * [zmartzone mod_auth_openidc for Apache 2.x](https://github.com/zmartzone/mod_auth_openidc) * [Duo Security MFA Authentication for Keycloak](https://github.com/mulesoft-labs/keycloak-duo-spi) * [Extension Keycloak facilitant l'utilisation de FranceConnect](https://github.com/InseeFr/Keycloak-FranceConnect) * [Ambassador Keycloak Support](https://www.getambassador.io/reference/idp-support/keycloak/) * [Keycloak Python Client](https://github.com/akhilputhiry/keycloak-client) * [Keycloak Terraform Provider](https://github.com/mrparkers/terraform-provider-keycloak) * [Keycloak ADFS OpenID Connect](https://www.michaelboeynaems.com/keycloak-ADFS-OIDC.html) * [React/NextJS Keycloak Bindings](https://github.com/panz3r/react-keycloak) * [Keycloak Open-Shift integration](https://github.com/keycloak/openshift-integration) * [Keycloak, Kong and Konga setup scripts (local development)](https://github.com/JaouherK/Kong-konga-Keycloak) * [SSO for Keycloak and Nextcloud with SAML](https://stackoverflow.com/questions/48400812/sso-with-saml-keycloak-and-nextcloud) * [Keycloak Connect GraphQL Adapter for Node.js](https://github.com/aerogear/keycloak-connect-graphql) * [python-keycloak](https://github.com/marcospereirampj/python-keycloak) * [Keycloak and PrivacyId3a docker-compose (local development)](https://github.com/JaouherK/keycloak-privacyIdea) * [Nerzal/gocloak Golang Keycloak API Package](https://github.com/Nerzal/gocloak) * [Apple Social Identity Provider for Keycloak](https://github.com/BenjaminFavre/keycloak-apple-social-identity-provider) ## Quick demo Videos * [Keycloak with istio envoy jwt-auth proxy](https://www.youtube.com/watch?v=wscX7JMfuBI) ## Themes * [Community Keycloak Ionic Theme](https://github.com/lfryc/keycloak-ionic-theme) * [A Keycloak theme based on the AdminLTE UI library](https://github.com/MAXIMUS-DeltaWare/adminlte-keycloak-theme) * [GOV.UK Theme](https://github.com/UKHomeOffice/keycloak-theme-govuk) * [Carbon Design](https://github.com/httpsOmkar/carbon-keycloak-theme) * [Modern](https://keycloakthemes.com/themes/modern) * [Adminlte](https://git.uptic.nl/uptic-public-projects/uptic-keyclock-theme-adminlte) * [keycloakify: Create Keycloak themes using React](https://github.com/InseeFrLab/keycloakify) ## Docker * [Official Keycloak Docker Images](https://github.com/jboss-dockerfiles/keycloak) * [Keycloak Examples as Docker Image](https://hub.docker.com/r/jboss/keycloak-examples) * [Keycloak Maven SDK for managing the entire lifecycle of your extensions with Docker](https://github.com/OpenPj/keycloak-docker-quickstart) ## Kubernetes * [Deprecated Keycloak Helm Chart](https://github.com/codecentric/helm-charts/tree/master/charts/keycloak) * [codecentric Keycloak Helm Chart](https://github.com/codecentric/helm-charts/tree/master/charts/keycloak) * [Import / Export Keycloak Config](https://gist.github.com/unguiculus/19618ef57b1863145262191944565c9d) * [keycloak-operator](https://github.com/keycloak/keycloak-operator) ## Tools * [keycloakmigration: Manage your Keycloak configuration with code](https://github.com/klg71/keycloakmigration) * [tool to autogenerate an OpenAPI Specification for Keycloak's Admin API](https://github.com/ccouzens/keycloak-openapi) * [oidc-bash-client](https://github.com/please-openit/oidc-bash-client) * [louketo-proxy (FKA Gatekeeper)](https://github.com/louketo/louketo-proxy) * [keycloak-config-cli: Configuration as Code for Keycloak](https://github.com/adorsys/keycloak-config-cli) * [Keycloak Pulumi](https://github.com/pulumi/pulumi-keycloak) * [Keycloak on AWS](https://github.com/aws-samples/keycloak-on-aws) * [aws-cdk construct library that allows you to create KeyCloak on AWS in TypeScript or Python](https://github.com/aws-samples/cdk-keycloak) * [keycloak-scanner Python CLI](https://github.com/NeuronAddict/keycloak-scanner) ## Deployment Examples * [Keycloak deployment with CDK on AWS with Fargate](https://github.com/aws-samples/cdk-keycloak) ## Example Projects * [Examples from Keycloak Book: Keycloak - Identity and Access Management for Modern Applications](https://github.com/PacktPublishing/Keycloak-Identity-and-Access-Management-for-Modern-Applications) * [Official Examples](https://github.com/keycloak/keycloak/tree/master/examples) * [Keycloak Quickstarts](https://github.com/keycloak/keycloak-quickstarts) * [Drupal 7.0 with Keycloak](https://gist.github.com/thomasdarimont/17fa146c4fb5440d7fc2ee6322ec392d) * [Securing Realm Resources With Custom Roles](https://github.com/dteleguin/custom-admin-roles) * [BeerCloak: a comprehensive KeyCloak extension example](https://github.com/dteleguin/beercloak) * [KeyCloak Extensions: Securing Realm Resources With Custom Roles](https://github.com/dteleguin/custom-admin-roles) * [Red Hat Single Sign-On Labs](https://github.com/RedHatWorkshops/red-hat-sso) * [Spring Boot Keycloak Tutorial](https://github.com/sebastienblanc/spring-boot-keycloak-tutorial) * [Custom Keycloak Docker Image of Computer Science House of RIT](https://github.com/ComputerScienceHouse/keycloak-docker) * [Example of custom password hash SPI for Keycloak](https://github.com/pavelbogomolenko/keycloak-custom-password-hash) * [Example for a custom http-client-provider with Proxy support](https://github.com/xiaoyvr/custom-http-client-provider) * [Monitor your keycloak with prometheus](https://github.com/larscheid-schmitzhermes/keycloak-monitoring-prometheus) * [Custom User Storage Provider .ear with jboss-cli setup](https://github.com/thomasdarimont/keycloak-user-storage-provider-demo) * [Keycloak - Experimental extensions by Stian Thorgersen/Keycloak](https://github.com/stianst/keycloak-experimental) * [Securing Spring Boot Admin & Actuator Endpoints with Keycloak](https://github.com/thomasdarimont/spring-boot-admin-keycloak-example) * [A Keycloak Mobile Implementation using Angular v4 and Ionic v3](https://github.com/tomjackman/keyonic-v2) * [Example for Securing Apps with Keycloak on Kubernetes](https://github.com/stianst/demo-kubernetes) * [Example for Securing AspDotNet Core Apps with Keycloak](https://github.com/thomasdarimont/kc-dnc-demo) * [Example for passing custom URL parameters to a Keycloak theme for dynamic branding](https://github.com/dteleguin/keycloak-dynamic-branding) * [Angular Webapp secured with Keycloak](https://github.com/CodepediaOrg/bookmarks.dev) * [Keycloak Theme Development Kit](https://github.com/anthonny/kit-keycloak-theme) * [Keycloak Clustering examples](https://github.com/ivangfr/keycloak-clustered) * [Keycloak Last Login Date Event Listener](https://github.com/ThoreKr/keycloak-last-login-event-listener) * [Keycloak Project Example (Customizations, Extensions, Configuration)](https://github.com/thomasdarimont/keycloak-project-example) * [Example of adding API Key authentication to Keycloak](https://github.com/zak905/keycloak-api-key-demo) ## Benchmarks * [Gatling based Benchmark by @rvansa](https://github.com/rvansa/keycloak-benchmark) ## Help * [Keycloak on Stackoverflow](https://stackoverflow.com/questions/tagged/keycloak) ## Commercial Offerings * [Red Hat Single Sign-On](https://access.redhat.com/products/red-hat-single-sign-on) * [INTEGSOFT UNIFIED USER CREDENTIALS WITH KEYCLOAK SSO](https://www.integsoft.cz/en/sso.html#what-is-sso) * [JIRA SSO Plugin by codecentric](https://marketplace.atlassian.com/plugins/de.codecentric.atlassian.oidc.jira-oidc-plugin/server/overview) * [Keycloak Competence Center by Inventage AG](https://keycloak.ch/) * [Keycloak as a Service](https://www.cloud-iam.com) ## Miscellaneous * [Find sites using Keycloak with google](https://www.google.de/search?q=inurl%3Aauth+inurl%3Arealms+inurl%3Aprotocol&oq=inurl%3A&client=ubuntu&sourceid=chrome&ie=UTF-8) * [Keycloak Dev Bookmarks](http://bookmarks.dev/search?q=keycloak) - Use the tag [keycloak](https://www.bookmarks.dev/tagged/keycloak) * [Use fail2ban to block brute-force attacks to keycloak server](https://gist.github.com/drmalex07/3eba8b98d0ac4a1e821e8e721b3e1816) * [Pentest-Report Keycloak 8.0 Audit & Pentest 11.2019 by Cure53](https://cure53.de/pentest-report_keycloak.pdf) * [Keycloak - CNCF Security SIG - Self Assesment](https://docs.google.com/document/d/14IIGliP3BWjdS-0wfOk3l_1AU8kyoSiLUzpPImsz4R0/edit#) # License [](https://creativecommons.org/publicdomain/zero/1.0/) To the extent possible under law, [Thomas Darimont](https://github.com/thomasdarimont) has waived all copyright and related or neighboring rights to this work.

Data Science has been ranked as one of the hottest professions and the demand for data practitioners is booming. This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Data Science or Machine Learning. This program consists of 9 courses providing you with latest job-ready skills and techniques covering a wide array of data science topics including: open source tools and libraries, methodologies, Python, databases, SQL, data visualization, data analysis, and machine learning. You will practice hands-on in the IBM Cloud using real data science tools and real-world data sets. It is a myth that to become a data scientist you need a Ph.D. This Professional Certificate is suitable for anyone who has some computer skills and a passion for self-learning. No prior computer science or programming knowledge is necessary. We start small, re-enforce applied learning, and build up to more complex topics. Upon successfully completing these courses you will have done several hands-on assignments and built a portfolio of data science projects to provide you with the confidence to plunge into an exciting profession in Data Science. In addition to earning a Professional Certificate from Coursera, you will also receive a digital Badge from IBM recognizing your proficiency in Data Science.

*Archived* - Introduction to Analyzing SETI Institute radio data using IBM Cloud database and analytic tools.

A Devops pipeline is set of automated processes and tools that the development (Dev) and operations (Ops) teams implement to build, test, and deploy software faster and easier. In this course you will complete DevOps pipeline generally consists of a set of tools which are normally broken down into the following categories: Plan Code Integrate Test Release Deploy Operate This learning path will cover: Git is an open-source and distributed version control system. Github is git repository hosting service used for code sharing, bug tracking, feature request and much more. PyCharm is an integrated development environment (IDE) for python programing language. Flask is a python web framework. HTML is the standard markup language for Web pages. CSS is a style sheet language use to style a HTML document. SQLAlchemy is an open-source SQL toolkit and object-relational mapper which gives full power and flexibility of SQL. Selenium is used to automate web browser interaction. Pytest is unit testing framework that allows users to write test codes. Ngrok allows to expose a web server running on your local machine to the internet. Github Action enables you to include Continues Integration (CI) and continuous deployment (CD) capabilities and many other features directly in your repository. Docker is an open source containerization platform enables developers to package applications into containers. Docker Hub is a cloud-based repository for finding and sharing container images with your team. Kubernetes is an open-source container orchestration for automating deployment, scaling, and management of containerized applications. This course is one stop shop where you will learn web development, continuous integration, continuous deployment, containerization, writing neat and quality code, devops concepts and much more with python programing language. What you’ll learn Learn to build Continuous Integration Continuous Deployment pipeline Build CI CD tool to update docker image after any update Learn to create dockerfile Learn the fundamental concepts of Docker Learn the fundamental concepts of Kubernetes Learn to create Kubernetes YAML files Learn to deploy high availability, fault tolerance, scalable application Learn all the basic and advanced git commands Learn different types of branches like master, developer, feature, release and hotfix branch Learn fundamental concepts of Version Control System Learn to use Github actions for CI CD pipeline Learn to build python flask web application Learn to use SQL Alchemy Lean to create HTML pages using HTML, CSS and bootstrap Are there any course requirements or prerequisites? Git installed Docker installed Kubernetes installed Any IDE Github account Docker hub account Who this course is for: Anyone who wants to Enhance their skills in DevOps domain Developers and IT Pros Instructor User photo Pranjal Srivastava Docker | Kubernetes | AWS | Azure | ML | Linux | Python I am an Instructor, Devops engineer, machine learning enthusiast, cloud expert and passionate developer. I have authored 60+ courses with over 50,000+ students worldwide across 175+ countries on wide array of technologies like containerization, machine learning, Linux, programming languages and cloud computing platforms like Microsoft Azure, Amazon Web Service and IBM Cloud.

Tooling and controllers to enable hosted control plane OpenShift clusters running on IBM Cloud.

Python-based command line tool to manage workspaces and test conversations for IBM Watson Assistant on IBM Cloud

This repo consists of all courses of IBM - Data Science Professional Certificate, providing with techniques covering a wide array of data science topics including open source tools and libraries, methodologies, Python, databases, SQL, data visualization, data analysis, and machine learning. You will practice hands-on in the IBM Cloud using real data science tools and real-world data sets.

The IBM Cloud Pak Operations CLI extends the capabilities of the OpenShift (oc) and Cloud Pak for Data (cpd-cli) command-line tools by offering additional commands for managing Red Hat OpenShift clusters and IBM Software Hub. It addresses gaps in existing tooling, enabling advanced operational tasks and simplifying complex administrative workflows.

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A tool for experimenting with evolutionary optimization methods for machine learning algorithms, by distributing the workload over a large number of compute nodes on the IBM Cloud. For now, it only includes an implementation of [Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning](https://arxiv.org/abs/1712.06567).

This project will provide DevOps automation in the form of snippets, sample apps, and plugins in support of integrating with IBM Application Security on Cloud for automated security scans of software projects using popular tools and frameworks across the DevOps landscape.

:open_file_folder: Databases is a collection of information that is organized so that it can easily be accessed, managed, and updated. In one view, databases can be classified according to types of content: bibliographic, full-text, numeric, and images. DEFINITION database Posted by: Margaret Rouse WhatIs.com Contributor(s): Allan Leake Sponsored News Using Automation to Solve Data Management Challenges –Veritas Avoid the Pain of Cloud Silos With Unified Management and Visibility –Splunk See More Vendor Resources Guide to Consolidating SQL Server 2000 and SQL Server 2005 Databases to SQL ... –Dell and Microsoft SQL Zero-Time Upgrades to Oracle Database 11g Using Oracle GoldenGate –Oracle Corporation A database is a collection of information that is organized so that it can easily be accessed, managed, and updated. In one view, databases can be classified according to types of content: bibliographic, full-text, numeric, and images. Download this free guide Download Our Exclusive Big Data Analytics Guide An unbiased look at real-life analytics success stories, including a Time Warner Cable case study, and tips on how to evaluate big data tools. This guide will benefit BI and analytics pros, data scientists, business execs and project managers. Start Download In computing, databases are sometimes classified according to their organizational approach. The most prevalent approach is the relational database, a tabular database in which data is defined so that it can be reorganized and accessed in a number of different ways. A distributed database is one that can be dispersed or replicated among different points in a network. An object-oriented programming database is one that is congruent with the data defined in object classes and subclasses. Computer databases typically contain aggregations of data records or files, such as sales transactions, product catalogs and inventories, and customer profiles. Typically, a database manager provides users the capabilities of controlling read/write access, specifying report generation, and analyzing usage. Databases and database managers are prevalent in large mainframe systems, but are also present in smaller distributed workstation and mid-range systems such as the AS/400 and on personal computers. SQL (Structured Query Language) is a standard language for making interactive queries from and updating a database such as IBM's DB2, Microsoft's SQL Server, and database products from Oracle, Sybase, and Computer Associates.

The web client for Boomerang Flow, a no-code, cloud-native workflow automation tool. Built in React using IBM Carbon components

All the IBM Cloud tools I need in one Docker image and a few shell scripts

The IBM Operator Collection SDK provides the resources and tools that are needed to develop Operator Collections against the IBM® z/OS® Cloud Broker as part of the IBM Z® and Cloud Modernization Stack.

For this project, you will assume the role of a Data Scientist / Data Analyst working for a new startup investment firm that helps customers invest their money in stocks. Your job is to extract financial data like historical share price and quarterly revenue reportings from various sources using Python libraries and webscraping on popular stocks. After collecting this data you will visualize it in a dashboard to identify patterns or trends. The stocks we will work with are Tesla, Amazon, AMD, and GameStop. Dashboard Analytics Displayed A dashboard often provides a view of key performance indicators in a clear way. Analyzing a data set and extracting key performance indicators will be practiced. Prompts will be used to support learning in accessing and displaying data in dashboards. Learning how to display key performance indicators on a dashboard will be included in this assignment. We will be using Plotly in this course for data visualization and is not a requirement to take this course. Watson Studio In the Python for Data Science, AI and Development course you utilized Skills Network Labs for hands-on labs. For this project you will use Skills Network Labs and Watson Studio. Skills Network Labs is a sandbox environment for learning and completing labs in courses. Whereas Watson Studio, a component of IBM Cloud Pak for Data, is a suite of tools and a collaborative environment for data scientists, data analysts, AI and machine learning engineers and domain experts to develop and deploy your projects. Review criteria There are two hands-on labs on Extracting Stock Data and one assignment to complete. You will be judged by completing two quizzes and one peer review assignment. The quizzes will test you based on the output of the hands-on labs. In the peer review assignment you will share and take screen shots of the outcomes of your assignment.

COMPLEX ENTERPRISE APPLICATIONS USUALLY COME WITH VARYING BUSINESS LOGIC. SUCH CONDITIONS AND SUBSEQUENT SYSTEM ACTIONS, KNOWN AS RULES, ARE EVER VARYING AND DEMAND INVOLVEMENT OF DOMAIN SPECIFIC KNOWLEDGE MORE THAN TECHNOLOGY AND PROGRAMMING. THE RULES, THEREFORE, MUST RESIDE OUTSIDE THE CODEBASE AND ARE TO BE AUTHORED BY PEOPLE WITH CORE DOMAIN EXPERTISE WITH LITTLE TO NO- TECHNICAL /PROGRAMMING KNOWLEDGE. A SPECIFIC TYPE OF SOFTWARE TOOLS, KNOWN AS RULE ENGINES, CATER TO THIS CONTEXT. WHILE WE HAVE MANY SUCH RULE ENGINES AROUND, SUCH AS DROOLS (WHICH IS A FEATURE-RICH BRMS), EASY RULES, RULE BOOK, ORACLE RULES SDK, BLAZE (FICO), IBM DECISION MANAGER ETC. ENABLING RULE MANAGEMENT DECLARATIVELY WITH THEIR RESPECTIVE RICH FEATURES INCLUDING VERSION CONTROL ARE OFTEN VERY USEFUL FOR LOT OF THE APPLICATIONS. HOWEVER, IN CERTAIN LESSER COMPLEX SOLUTIONS, THEY OFTEN TURN OUT TO BE OVERKILL, AND REMAIN UNDERUTILISED. MAINTENANCE OF AN ADDITIONAL COMPONENT BECOMES MORE OF A LIABILITY THAN ASSET. IN THIS ARTICLE WE ATTEMPT TO ILLUSTRATE HOW INHERENT FEATURES OF JAVA CAN BE LEVERAGED TO EXTERNALIZE RULES IN THE SIMPLEST POSSIBLE WAY, WITHOUT NARROWING DOWN TO ANY TRANSITIVE DEPENDENCY OF ADDITIONAL FRAMEWORKS. THE APPROACH IS USEFUL WHEN TECHNICAL RULES (SNIPPET WRITTEN IN JAVA) NEEDS TO BE EXTERNALIZED AND CAN CHANGE FREQUENTLY. THE APPROACH, THEREFORE, HOLDS EQUALLY WORTHY ACROSS ANY JAVA ECOSYSTEM, REGARDLESS OF FRAMEWORKS. PROVIDING A SIMPLE POJO BASED DECLARATIVE MODEL FOR RULES THAT ARE LOADED FROM EXTERNAL RESOURCE STREAMS, IT ALLOWS RULES TO BE AUTHORED OUTSIDE THE APPLICATION WITHOUT EVEN REQUIRING AN APPLICATION DOWNTIME. WE CAN INTEGRATE IT EASILY WITH SPRING MICRO SERVICES AND CLOUD CONFIGURATION WITH AND WITHOUT CLOUD BUS. THE APPROACH PROVIDES ENCRYPTION-AT-REST TO ENSURE SAFETY (CONFIDENTIALITY AND INTEGRITY) OF BUSINESS RULES. PLUS, APART FROM SUPPORTING JASYPT AND SPRING CONFIG CIPHERING, ANY CUSTOM SECURITY CAN BE PLUGGED-IN TO IT.

Cloud Pak for Applications provides a complete and consistent experience to speed development of applications built for Kubernetes, using agile DevOps processes. You can easily modernize your existing applications with IBM’s integrated tools and develop new cloud-native applications faster for deployment on any cloud. I have provided a few labs for this platform.

IBM Z & Cloud Modernization Stack deployment tools

Tools to support cloud guardrails implementation and compliance checks for IBM Cloud.

Tool to backup specific services from IBM Cloud

The goal of this project is to automate CIS integration for IBM Cloud application platforms. We will produce a command-line tool that customers can use to simplify this process.

This professional certificate has a strong emphasis on applied learning. Except for the first course, all other courses include a series of hands-on labs and are performed in the IBM Cloud (without any cost to you). Throughout this Professional Certificate you are exposed to a series of tools, libraries, cloud services, datasets, algorithms, assignments and projects that will provide you with practical skills with applicability to real jobs that employers value, including: Tools: Jupyter / JupyterLab, Zeppelin notebooks, R Studio, and Watson Studio Libraries: Pandas, NumPy, Matplotlib, Seaborn, Folium, ipython-sql, Scikit-learn, ScipPy, etc. Projects: random album generator, predict housing prices, best classifier model, battle of neighborhoods

Infrastructure reverse engineering tool for IBM Cloud - Beta Release

No description available

Develop and Delay a webside using cloud foundry tool, supported by a cloud vendor (ex. IBM Cloud)