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Speed your DevOps implementation with custom DevOps training.
Instructor-Led | Role-Based | Tailored To Your Project
DevelopIntelligence helps your teams gain expertise in the core components of DevOps including Agile training, CI/CD excellence courses, Cloud Computing, and much more.
Becoming great at DevOps, however, is not a one-and-done event. Technologies and tools are constantly evolving. The best DevOps teams engage in continuous learning to stay current and are able to anticipate changes instead of playing catch-up.
A robust, ongoing DevOps training plan will ensure your employees have the skills they need to keep your organization competitive.
Our DevOps Training Team
Our experts are well-known industry practitioners. They have hands-on, in-the-trenches experience with the specific technologies they teach. When not training for DevelopIntelligence, they regularly author technical papers on the evolution of tools and languages in the field, speak at industry conferences, and contribute to open-source projects.
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Win at DevOps with DevelopIntelligence.
Learn how to effectively integrate today’s best software development practices.
Learn about how adopting Agile impacts an organization.
Learn how to effectively estimate for Agile projects.
Learn about an intensive exploration of Agile Testing roles and techniques.
Learn how to effectively capture business requirements for an Agile project.
Learn how to use Agile to develop software.
Learn the skills to become certified in Agile project management.
Learn how to become a Certified Scrum Practitioner and Certified Scrum Developer with hands-on experience.
Learn how to become a certified and successful Product Owner through in-depth study of Agile, Scrum, and Lean concepts.
Learn about how Agile Development can improve the process of writing software.
Learn how to successfully implement XP methodology in your organization.
Learn how to use Kanban to enhance and improve your project management.
Learn how to shift to the Lean process.
Learn how the Rational Unified Process can help you be more effective in the development of production-quality software.
Learn how to use SAFe effectively in an enterprise Agile transformation.
Learn about an in-depth exploration of Scrum and Lean through hands-on lab work.
Learn how to enhance Scrum learning with Lean thinking and software engineering practices.
Learn how to train your team to effectively implement Scrum.
Learn how to use Scrum-Kanban tools to improve work flow and agility.
Learn how to define the iterative and quality-focused software development methodology of test-driven development.
Learn how to create user stories as a means of tracking project requirements.
Learn how to effectively create user stories.
Learn to build end-to-end data applications using Microsoft Azure and understand which tools are best suited to certain problems and use-cases.
Learn how to deploy, configure and manage Azure using Chef.
Learn how to configure, maintain and monitor Azure Virtual Machines and Virtual Networks.
Learn how to build, deploy, and maintain applications in Azure Cloud.
Learn how to develop a globally distributed application using Azure and serverless concepts
Learn how to pass the "Implementing Microsoft Azure Infrastructure Solutions" certification exam.
Learn how to implement AI and ML techniques on Azure.
Learn how to build, deploy, secure, scale, monitor and maintain applications in Azure Cloud.
Learn how to plan and manage cloud team resources more effectively.
Learn how to deploy and manage Azure in private data centers.
Learn the information necessary to pass the Developing Microsoft Azure Solutions Certification Exam.
Learn how to use Big Data technologies on the Azure cloud.
Learn how to use Chef to automate the configuration, deployment, management and test of server infrastructure.
Learn how to use Chef-specific tools and tactics such as Ohai plugins, Chef handlers, ChefSpec, and Custom Light-Weight Resource Providers (LWRP's).
Learn how to configure and deploy Chef for server infrastructure.
Learn how to write cookbooks for Chef.
Learn about the key concepts and components of a DevOps environment.
Learn how to use Apache Airflow to manage data warehouses.
Learn the principles and practice of continuous integration using Travis CI on top of Github.
Learn how to adopt a continuous integration mindset to achieve better code quality.
Learn a comprehensive understanding of Spinnaker’s features and capabilities in relation to the standard CI/CD concepts it supports.
Learn how Nexus works, how to install/manage it, and how to make the most out of your investment in Nexus.
Learn how to utilize Docker for distributed apps.
Learn the basics about systems and application design using Docker.
Learn how to leverage the advanced parts of Git.
Learn the details and advanced usage of Git and review the Cloud-based tools and repositories.
Learn how Bitbucket can make teams more productive.
Learn how to adopt and use Git to manage your source control.
Learn how to utilize GitLab to manage the software development lifecycle.
Learn how to build a Chatbot on Google Cloud and deploy it standalone as well as on Facebook Messenger.
Learn about Data Warehousing from an AWS perspective examining tools specifically underneath AWS.
Learn how to perform insightful and responsive data analysis at scale and delight the consumers of the analysis with effective data visualizations.
Learn to build systems on Google Cloud to store and process batch or streaming data.
Learn how to create and deploy high-performance data science and machine learning systems on Google Cloud for regression and classification use cases leveraging both structured and unstructured datasets.
Learn to create and deploy software on Google Cloud to have secure and stable applications.
Learn about Google Cloud and its technical capabilities in the areas of IT infrastructure, operations, Big Data, and Machine Learning.
Learn how to analyze large scale, distributed, and real-time datasets with MapReduce and Apache Beam based capabilities of Google Cloud and practice identification and analysis of effective data features for predictive analytics with BigQuery ML and TensorFlow.
Learn how to use data engineering on Google Cloud to build specialized data pipelines for large scale streaming data analytics, machine learning, and recommendation systems.
Learn how to implement statistical and machine learning models using TensorFlow, for example for recommendation engines, and how to improve their performance based on the students’ understanding of underlying mathematical principles.
Learn how to utilize advanced processes and practices for the platform to improve function and security.
Learn to use Google's BigQuery to explore and gain insights from large datasets.
Learn about the different cloud providers and what they offer.
Learn how to use Java to set up a project with the Google App Engine and integrate it with other Google Cloud services.
Learn how to develop a platform-based infrastructure using Google Cloud.
Learn the fundamental skills needed to manage large scale infrastructure with SaltStack on major cloud providers (AWS, Google Cloud and Azure).
Learn how to create and configure Kubernetes clusters.
Learn the skills required to build, store, and deploy containers using Docker and Kubernetes.
Learn a deeper dive into Kubernetes architecture, concepts, and container management.
Learn about the key operations and configurations utilized when using Kubernetes to automate, deploy, and manage containerized applications.
Learn how to harden Kubernetes clusters and resolve security issues that may happen through misconfiguration or performance tuning.
Learn the skills required to build, store, and deploy containers using OpenShift.
Learn a comprehensive understanding of Helm and how to use it to package and manage Kubernetes workloads.
Learn advanced techniques for managing data and tuning models.
Learn how to become an expert at creating high-throughput, multithreaded, network oriented programs written in Python.
Learn intermediate data analysis techniques and how programming languages can be used to further analyze data.
Learn to design, implement and evaluate various recommendation engines.
Learn how to mature programming knowledge.
Learn how data can be gathered to improve the overall needs of the business.
Learn a working knowledge of graphics processing practices within Machine Learning using CUDA, PyCuda, OpenCL, Vulkan, and Tensorflow.
Learn various Machine Learning algorithms to evaluate and productize models.
Learn how to create basic programs using Python.
Learn how to develop simple programs using fundamental Python concepts.
Learn both R and Python programming languages by providing comparisons and recommendations between both.
Learn how to use MongoDB with Python.
Learn how to use Python to explore and analyze data, run basic regression models, visualize data, and apply some basic machine learning models to data.
Learn how to implement statistical and machine learning models using PyTorch and how to improve their performance based on an understanding of underlying mathematical principles.
Learn software engineering techniques using Python.