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The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
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Scaling API-first – The story of a global engineering organization Ian Reasor, Senior Computer Scientist - Adobe Radu Cotescu, Senior Computer Scientist - Adobe Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/
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MySQL Webinar, presented on the 25th of April, 2024. Summary: MySQL solutions enable the deployment of diverse Database Architectures tailored to specific needs, including High Availability, Disaster Recovery, and Read Scale-Out. With MySQL Shell's AdminAPI, administrators can seamlessly set up, manage, and monitor these solutions, ensuring efficiency and ease of use in their administration. MySQL Router, on the other hand, provides transparent routing from the application traffic to the backend servers in the architectures, requiring minimal configuration. Completely built in-house and supported by Oracle, these solutions have been adopted by enterprises of all sizes for their business-critical applications. In this presentation, we'll delve into various database architecture solutions to help you choose the right one based on your business requirements. Focusing on technical details and the latest features to maximize the potential of these solutions.
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Puppet conf 2013
1.
CONTINUOUSLY INTEGRATED PUPPET IN A DYNAMIC ENVIRONMENT SAM BASHTON, BASHTON LTD
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CONTINUOUSLY INTEGRATED PUPPET IN A DYNAMIC ENVIRONMENT MASTERLESS PUPPET: WHY AND HOW SAM BASHTON, BASHTON LTD
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ABOUT ME Linux guy since Slackware, floppy disks and root + boot Using Puppet since 2007 Run a company in Manchester, North West England We provide outsourced ops for other companies
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OUR FULLY MANAGED ENVIRONMENTS Primarily transactional websites (ecommerce) Majority (70%+) on Amazon Web Services (AWS) Majority using CentOS
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HOW WE WORK Simple is better than complex Complexity is worth adding only if it provides obvious functional benefits Reusability Resilience
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WHY DID WE PICK AWS? Featureset and toolset massively in advance of any other cloud provider, public or private #1 customer reason for switching to AWS? The ability to scale on demand
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TOOLS WE USE FOR BUILDING AND MANAGING Do one thing and do it well CloudFormation Amazon tool to manage infrastructure Puppet Manage system configuration Pulp centralised repository, manages package revisions Jenkins
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HOW WE USE PUPPET No Puppetmaster Puppet manifests, hieradata and modules distributed to all machines via RPM All machines boot with a common, blank image and get configured at first boot
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WHAT'S WRONG WITH MASTER BASED PUPPET? Pets vs Cattle Puppet designed for a world of servers as pets We do not live in that world
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PUPPET DESIGNED FOR PETS Many assumptions in Puppet presume that your servers are pets Some of these work against us when managing a herd
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MANUAL CERTIFICATE SIGNING Clearly unsuitable when machines are automatically provisioned
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POTENTIAL WORKAROUNDS: Autosign Use/write another automated certificate generation mechanism Possibly tied in with autoscaling
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NO MECHANISM FOR CLEANING OLD HOSTS Likely to have hostnames reused, causing machines to fail to configure Puppetmaster will fill with certificates for machines that ran for a few hours and went away again
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POTENTIAL WORKAROUNDS: Use UUID certificates Agree not to look in the certificate directory Write mechanism for cleaning up old certificates
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HOSTS CONFIGURED BASED ON HOSTNAME Our machines have names like ip172265123 How does Puppet know what type of machine this is?
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POTENTIAL WORKAROUNDS Use an external node classifier Use some mechanism for giving a better hostname, eg web172265123 and use regex for node names
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PUPPETMASTER IS A SINGLE POINT OF FAILURE If the Puppetmaster fails, we can no longer autoscale up In particular, this could be a problem if there is availability zone failure
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POTENTIAL WORKAROUNDS Clustered Puppetmasters
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WORKAROUND RECAP Use/write alternative certificate management software Write an external node classifier / mechanism for setting hostname appropriately Cluster multiple Puppetmasters
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WHAT WE DID INSTEAD Decided using a Puppetmaster was trying to fit a square peg into a round hole Instead, decided to run Puppet without a master
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APPLYING LOCAL PUPPET MANIFESTS puppet apply --modulepath=/etc/puppet/modules
example.pp
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DISTRIBUTING MANIFESTS Use RPM Distribute full set of manifests/modules to each machine Apply only the manifest relevant to that machine
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PACKING PUPPET MANIFESTS IN RPM Build an RPM containing everything under /etc/puppet Make files readable only by root
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APPLY PUPPET MANIFESTS Have an RPM %postinst command apply the Puppet config This isn't as straightforward as running the puppet apply from %postinst Puppet needs to install packages via yum, but yum is running installing the Puppet package Instead, we work around with a dirty hack: have the %postinst create an at script which checks if yum has finished and then runs the puppet apply
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RPM INSTALLATION AND MANAGEMENT How do we get these RPMs on our machines?
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PULP We were already using Pulp Provides yum repository management Used for managing security updates and deploying application code http://pulpproject.org/
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WHAT IS PULP Repository manager Allows us to easily audit what packages and versions are installed where Allows us to push package installations Uses qpid message queue Has concept of 'content distrubtion servers' for easy replication and clustering
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HOW WE USE PULP Puppet contains details of what packages should be installed Pulp manages which version of the package should be installed Pulp allows us to clone repos and copy packages between them for easy qa>stage>live environment management
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DEPLOYING CONFIGURATION AS CODE Allows us to reuse our existing code deployment infrastructure Manage configuration deployment from Jenkins
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HOW WE DEPLOY CODE Everything managed via the Jenkins continuous integration server Jenkins uses Pulp to install code on remote machines
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DETAILS ON HOW WE DEPLOY CODE Jenkins fetches code from source control (git) An RPM is built Tests are run If tests pass, the RPM is added to the relevant Pulp repository RPM installed on the target machine(s)
33.
DEPLOYMENT LIFECYCLE Jenkins also manages deployment lifecycle RPMs are installed on staging Promoted Builds plugin then used to install the same RPMs on live once testing is complete
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PUPPET DEPLOYMENT PROCESS Puppet manifests are checked into git Lint tests via Jenkins pulls in modules with librarianpuppet, then builds an RPM Deployment to test environments, functional tests for wider codebase run Jenkins Warnings plugin
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PUTTING IT INTO PRODUCTION Once suitable tests (automated and manual) have been carried out, we promote Puppet config into production We use the Jenkins 'Promoted Builds' plugin for this
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JENKINS: PROMOTION
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EXCEPT.. How does a machine get from a bare image to the state where we can push packages to it from Pulp? How does a machine know what type of machine it is? How do we find other resources, eg database hostname?
38.
CLOUDFORMATION Amazon tool for specifying infrastructure Everything* we provision inside AWS is provisioned via CloudFormation JSON templates * Everything except for the things Amazon doesn't expose via CloudFormation..
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CLOUDINIT Works with multiple cloud types Sorts out things like SSH keys, allows us to configure host names Also allows us to provide a bash script to run on startup
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PROVISIONING A BARE INSTANCE cloudinit automatically manually adds the pulp repo which contains Pulp, Puppet and our Puppet manifests/modules Installs appropriate RPMs Puppet runs, subscribing the machine to the relevant Pulp repos, and installing packages in the usual Puppet way
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HOW DOES IT KNOW WHAT TYPE OF MACHINE IT IS? We tell it! Use an environmental variable $HOSTTYPE Simply run puppet apply --modulepath=/etc/puppet/modules
${HOSTTYPE}.pp
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EXTRA FACTS Custom facter facts Also specified in an environmental variable Data comes from within the CloudFormation template On our list of things to look at: FACTER_HOSTENVIRONMENT=live FACTER_STACKNAME=customer-web-live https://github.com/fanduel/hieracloudformation
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OTHER RESOURCES We either: Provide details as a facter fact `FACTER_DBHOST=xyz Also use this approach to limit distribution of secure details, eg DB passwords Discover via the EC2 API Eg Varnish servers discover web backends by calling API and finding hosts tagged appropriately
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FREE WINS!
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FREE WINS! Greater control over the timing of Puppet runs Improved visibility for ops and devs Configuration changes now have to be deployed to testing/staging first
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MORE FREE WINS! Puppet configs now have a version Easy to find config version on the machine itself Config changelogs accessible on every machine (Git changelog added to RPM)
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THE DOWNSIDES Puppet manifests and modules on all machines Potentially a security issue? Mitigated by CloudFormation holding most sensitive data
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ALTERNATIVE IMPLEMENTATIONS Don't want to use Pulp? Could do basically the same thing with yum s3 plugin Use mcollective to push package updates https://github.com/jbraeuer/yums3plugin
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FUTURE IMPROVEMENTS Build AMIs using Packer instead of configuring at boot time Decrease time to autoscale Would probably still need to run Puppet at first boot to configure machine specific settings
51.
QUESTIONS? COMMENTS? Sam Bashton Twitter: (Psst.. ) sam@bashton.com @bashtoni http://www.bashton.com/jobs/
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