In order to keep your company at the cutting edge of technology, you need to think like a software company. That's where DevOps comes in. With this course, you will learn the key attributes of software development methodology and how to apply these methods in a way that aligns with your most important business objectives. These attributes include: continuous development, continuous delivery, theory of constraints, value streams, telemetry, A/B testing, information security, change management, and compliance.
In 2011, Marc Andreessen famously wrote, “software is eating the world.” Seven years later, and the five largest companies in the world are all software companies.
Today, as technology becomes pervasive across industries and functions, companies from all different sectors are competing to become technology companies as well. Yet, there is a growing digital divide between digital ready companies and the traditional enterprise. Companies in the Fortune 500 deploy once every 1.5 months, digital ready companies using DevOps make over 1000 deployments per month.
DevOps is the combination of processes, technology, tools, and culture that increases an organization's ability to deliver applications and services at high velocity, with the goal of shortening the systems development life cycle while delivering features, fixes, and updates frequently in close alignment with business objectives. With this course, you will gain the knowledge necessary to keep your business on the cutting edge of technology while remaining competitive against other digital ready companies.
- You will able to describe DevOps and DevOps platforms key principles, the Theory of Constraints, how to use DevOps in cybersecurity, DevOps applications to fields other than software development, legal and regulatory aspects of DevOps, and the implementation of DevOps tools
- You will be able to describe and apply Systems Thinking, Feedback Loops, Containers, Container Orchestration, CI/CD, and Continual Learning & Experimentation
- You will be able to make effective decisions taking into account the key trends of DevOps implementations
- You will be able to create containers, CI/CD pipelines, kubernetes clusters, spinnaker deployments, and skaffold deployments
Who Should Attend:
This course is ideal for C-suite and strategic leaders and decision makers in a wide variety of industries including finance, law, education, and government.
Laptops or Chromebooks with a modern operating system for which you have administrator privileges are required. An updated version of Google Chrome is required as well. Tablets will not be sufficient for the computing activities in this course.
|Session Title||Session Description|
|Program Overview||DevOps trends, fundamentals, and course overview|
|One-hour DevOps Pipeline||Build a one-hour "DevOps" pipeline to explore components and principles|
|CI/CD Chronicle||We started with VMs in the cloud, then we added containers, then microservices and serverless architectures. The journey chronicles the pursuit of faster product development and agility in our platforms.|
|DevOps Platforms||Leading DevOps tools, open source, platforms, and ecosystems|
|DevOps in Financial Markets||Softwares, architectures, and applications of DevOps to financial markets|
|DevOps in Insurance Markets||Software, architectures, and applications of DevOps to insurance markets|
|Spinnaker||Open source, multi-cloud continuous delivery platforms for releasing software changes with high velocity and confidence|
|Kubernetes||Open-source systems for automating deployment, scaling, and management of containerized applications|
|Containers||A container is a standard unit of software that packages up code and all its dependencies so the application runs quickly and reliably from one computing environment to another|
|Skaffold||Platforms to facilitate continuous development for Kubernetes applications.|
|Intelligent Testing||Modern quality assurance (QA) groups struggle to play their role in a world of continuous delivery. Innovations in machine intelligence and test automation for a better model that will allow QA to keep pace with development, increase productivity|
|Data Science Acceleration||For a data scientist, moving a model from a laptop to a highly scalable cloud cluster is effectively impossible without significant re-architecture. Session presents Cloud Native alternatives. New building blocks that have the potential to accelerate data science across industry and academia|
|Theory of Constraints||Application of the theory of constraints helps to identify the organization's goal, the chain of interdependent activities that achieve it, and the weaknesses in the chain|
|A holistic approach to analysis that focuses on the way that a system's constituent parts interrelate and how systems work over time and within the context of larger systems|
|Amplify Feedback Loops||A core tenet of modern process improvement methodologies. The principle maximizes the opportunities for organizations to learn and improve|
|Continual Experimentation and Learning||The processes that enable continuous creation of individual knowledge, used to transform team and organization knowledge|
|Cybersecurity||Simultaneously achieve DevOps and information security goals. Create a high degree of assurance for confidentiality, integrity, and availability of services and data|
|Containerization||Any size application and dependencies can be containerized. Over time, companies should aspire towards splitting suitable applications and writing future functionality as microservices|
|CI/CD||Set up Continuous Integration/Continuous Delivery (CI/CD) so that changes to your source code automatically result in a new container being built, tested, and deployed to staging and eventually, perhaps, to production. Set up automated rollouts, rollbacks, and testing|
|Orchestration & Application Definition||Systems for automating deployment, scaling, and management of containers|
|Observability & Analysis||Monitoring, logging, and tracing|
Class runs 9:00 am - 5:00 pm each day.
Professor Williams holds a BA in physics from Oxford University, an M.Sc. in physics from UCLA, and a Ph.D. from Swansea University. His area of specialty is large scale computer analysis applied to both physical systems and to information.
Professor Williams is internationally recognized in the field of computational algorithms for large-scale particle simulators and has authored two books and over 100 publications. For the past eight years, his research has focused on architecting of large scale distributed simulation systems. He teaches graduate courses on Modern Software Development and on Web System Architecting.
Presently Professor Williams is Director of MIT's Auto-ID Laboratory and has strong involvement in the MIT Geonumerics group.
Abel Sanchez, Executive Director, Research Scientist, Laboratory for Manufacturing and Productivity, MIT
Dr. Abel Sanchez holds a Ph.D. from the Massachusetts Institute of Technology (MIT). His areas of expertise include the Internet of Things (IOT), radio-frequency identification (RFID), simulation, engineering complex software systems, and cyber-physical security. He teaches graduate courses in Information engineering, cybersecurity, and software architecture. For the past six years, his research has focused on architecting large scale distributed simulation systems.
This course takes place on the MIT campus in Cambridge, Massachusetts. We can also offer this course for groups of employees at your location. Please complete the Custom Programs request form for further details.
|Fundamentals: Core concepts, understandings, and tools||30|
|Latest Developments: Recent advances and future trends||30|
|Industry Applications: Linking theory and real-world||40|
|Lecture: Delivery of material in a lecture format||65|
|Labs: Demonstrations, experiments, simulations||35|
|Introductory: Appropriate for a general audience||50|
|Specialized: Assumes experience in practice area or field||50|