Tag: DevOps

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Getting Started with Cloud-Native App Development: Best Practices, Learning Materials, and Videos to Watch

As a new engineer, understanding the concept of cloud-native app development is important for several reasons.

First, cloud-native app development is a key aspect of building and deploying applications in the cloud. It is the practice of using technologies, tools, and best practices that are designed to work seamlessly with cloud environments. By understanding how cloud-native app development works, you will be able to build, deploy, and manage cloud-native applications more effectively.

Second, cloud-native app development allows for better scalability and cost efficiency. By using technologies, tools, and best practices that are designed to work seamlessly with cloud environments, it allows for resources to be automatically allocated and scaled up or down as needed, without incurring additional costs.

Third, cloud-native app development promotes better collaboration and DevOps culture. By using technologies, tools, and best practices that are designed to work seamlessly with cloud environments, it becomes easier for different teams and developers to work together on the same application.

Fourth, cloud-native app development allows for better security, by using technologies, tools, and best practices that are designed to work seamlessly with cloud environments, it ensures that the application and infrastructure are protected from threats and can continue to operate in case of an attack or failure.

In summary, as a new engineer, understanding the concept of cloud-native app development is important because it is a key aspect of building and deploying applications in the cloud, allows for better scalability and cost efficiency, promotes better collaboration and DevOps culture, and allows for better security.

Learning Materials

Here’s a list to get you started learning about cloud-native app development. Note that some of these links may not be free and may require a subscription or payment. I receive no affiliate payments for these links.

Beginner:

Intermediate:

Advanced:

Videos to Watch

Best practices in Kubernetes app development

This document outlines best practices for developing with Kubernetes, such as using a tailored logging interface, debugging with CLI commands, and creating project templates with Cloud Code. Google Cloud DevTools are introduced as a way to simplify the process of incorporating best practices into the Kubernetes development workflow.

A Possible Learning Path

Hands-on experience: Start by setting up a simple Kubernetes cluster and experimenting with different app development tools such as Kubernetes Deployments, Services, and ConfigMaps. This can be done by following tutorials and guides, and deploying a simple application on a cloud platform like AWS, Azure, or GCP.

Theoretical learning: Once you have a basic understanding of app development, you can begin to explore the underlying concepts and technologies such as Kubernetes pods, services, and volumes. This can be done through online resources such as tutorials, courses, and documentation provided by Kubernetes, as well as books and blogs on the topic.

Understanding the principles and best practices: App development is an important aspect of a microservices architecture, so it’s important to understand the key principles and best practices of app development such as containerization, scaling, and rolling updates.

Joining a community: Joining a community of Kubernetes enthusiasts will help you connect with other people who are learning and working with app development for Kubernetes. This can be done through online forums, meetups, and social media groups.

Practice, practice, practice: As with any new technology, the best way to learn is by doing. The more you practice deploying and using app development tools in a Kubernetes cluster, the more comfortable and proficient you will become with the technology.

A Note from the Architect

Hey there, let’s talk about cloud native application development. So, what is it exactly? Well, it’s all about developing applications that are specifically designed to run in a cloud environment, like Kubernetes, which is one of the most popular container orchestration platforms out there.

What makes cloud native application development different from other approaches is that it’s all about leveraging the benefits of the cloud, like scalability and flexibility, to create applications that can easily adapt to changing workloads and environments.

When it comes to developing applications for Kubernetes, there’s a typical software development workflow that you’ll need to follow. First, you’ll need to choose the right programming languages and frameworks. Some of the most popular languages for cloud native development are Go, Java, Python, and Node.js.

Once you’ve chosen your languages and frameworks, you’ll need to design your application architecture to work well with Kubernetes. Some of the best patterns for Kubernetes include using microservices, containerization, and service meshes. On the other hand, monolithic applications and stateful applications are not well suited for running on Kubernetes.

And finally, let me tell you, we really like VS Code around here. It’s one of the best tools for cloud native application development, especially when working with Kubernetes. It provides a lot of great features for working with containerization and orchestration, like excellent plugins for Kubernetes, debugging, and integration with other popular tools like Helm and Kustomize. So, if you haven’t already, give it a try, you might like it too.

Be sure to reach out on LinkedIn if you have any questions.

Connect with Shawn
Connect with Shawn

Connect with me on LinkedIn. It’s where I’m most active, and it’s the easiest way to connect with me.

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Master Cloud Native Governance: The Essential Guide for New Engineers

As a new engineer, understanding the concept of cloud-native governance is important for several reasons.

First, it is a key component of cloud native application development. It uses governance tools and technologies that are native to the cloud and designed to work seamlessly with cloud-native applications. This allows for better deployment and management of cloud-native applications.

Second, cloud-native governance ensures compliance and security. It ensures that the application and infrastructure meet compliance requirements and are protected from threats.

Third, it promotes better collaboration and DevOps culture. Different teams and developers can work together on the same application, and the organization’s policies and standards are followed.

Fourth, it allows for better cost management. Resources can be monitored and controlled, and the organization is not overspending on the cloud.

In summary, understanding the concept of cloud-native governance is important for any engineer working in the field today. It is a powerful tool for building and deploying applications in a cloud environment.

Learning Materials

Here’s a list to get you started learning about cloud-native governance. Note that some of these links may not be free and may require a subscription or payment. I receive no affiliate payments for these links.

Beginner:

Intermediate:

Advanced:

A Possible Learning Path

Hands-on experience: Start by setting up a simple Kubernetes cluster and experimenting with different governance tools such as Open Policy Agent (OPA), Kubernetes Policy Controller, and Kube-bench. This can be done by following tutorials and guides, and deploying these tools on a cloud platform like AWS, Azure, or GCP.

Theoretical learning: Once you have a basic understanding of governance, you can begin to explore the underlying concepts and technologies such as Kubernetes role-based access control (RBAC), Namespaces, and NetworkPolicies. This can be done through online resources such as tutorials, courses, and documentation provided by Kubernetes, as well as books and blogs on the topic.

Understanding the principles and best practices: Governance is an important aspect of a microservices architecture, so it’s important to understand the key principles and best practices of governance such as security, compliance, and auditing.

Joining a community: Joining a community of Kubernetes enthusiasts will help you connect with other people who are learning and working with governance for Kubernetes. This can be done through online forums, meetups, and social media groups.

Practice, practice, practice: As with any new technology, the best way to learn is by doing. The more you practice deploying and using governance tools in a Kubernetes cluster, the more comfortable and proficient you will become with the technology.

A Note from the Architect

Ok, let’s talk about cloud native governance. So, why do we need to practice it? Well, as we all know, the cloud is a constantly evolving landscape and it can be pretty overwhelming to keep up with all the new technologies and best practices. That’s where governance comes in – it’s all about making sure we’re using the cloud in a consistent and efficient way across our organization.

So what exactly is cloud native governance? It’s all about using policies and tools to manage the resources in our cloud environment. This includes things like setting up guidelines for how our teams use the cloud, automating tasks to keep our environment in check, and monitoring for any potential issues.

Now, you might be wondering why cloud native governance was created. Well, as organizations started moving more and more of their workloads to the cloud, they realized they needed a way to keep everything in check. Without governance in place, it can be easy for teams to create resources in an ad-hoc way, which can lead to wasted resources, security vulnerabilities, and inconsistencies in how the cloud is being used.

Now, let’s talk about the major tools on Kubernetes that help with cloud native governance. One of the most popular is Kubernetes itself, which provides a way to manage and scale containerized applications. Another popular tool is Helm, which helps with managing and deploying Kubernetes resources. There’s also Kustomize, which helps with creating and managing customized resources. And finally, there’s Open Policy Agent (OPA) which allows to define and enforce policies on k8s resources.

It’s important to note that governance is similar to security, and it requires a continuous practice. Governance policies and tools need to be regularly reviewed and updated to ensure they are still effective and aligned with the organization’s goals and requirements. It’s all about making sure we’re using the cloud in the best way possible.

Be sure to reach out on LinkedIn if you have any questions.

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Connect with Shawn

Connect with me on LinkedIn. It’s where I’m most active, and it’s the easiest way to connect with me.

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Unlocking the Benefits of Configuration Management in Kubernetes

As a new engineer, understanding the concept of Configuration Management (CM) is important for several reasons.

First, CM is a key component of cloud native application development. It is the process of maintaining consistent and accurate configurations across all components of an application, including servers, networks, and software. By understanding how CM works, you can build, deploy, and manage cloud-native applications more effectively.

Second, CM ensures greater consistency and reproducibility. By maintaining consistent configurations across all components of an application, it ensures the same configuration is used across different environments, and that the infrastructure can be easily recreated if necessary. This makes it easier to handle the increasing demand for more computing power and storage.

Third, CM promotes better collaboration and a DevOps culture. By maintaining consistent configurations across all components of an application, it becomes easier for different teams and developers to work together on the same application.

Fourth, CM allows for better tracking and version control of changes. By keeping the configurations in a CM tool, it allows for tracking changes and rollback if necessary.

In summary, as a new engineer, understanding the concept of CM is important because it is a key component of cloud native application development, ensures greater consistency and reproducibility, promotes better collaboration and a DevOps culture, and allows for better tracking and version control of changes. It is a powerful tool for building and deploying applications in a cloud environment, and is essential for any engineer working in the field today.

Learning Materials

Here’s a list to get you started learning about configuration management. Note that some of these links may not be free and may require a subscription or payment. I receive no affiliate payments for these links.

Beginner:

Intermediate:

Advanced:

Videos to Watch

Configuration Management in 2020 and Beyond – Eric Sorenson

Cloud Native concepts are based on good architecture principles, such as declarative automation, continuous deployment, and observability. These principles emphasize short life cycles, high cardinality, and immutability, and have changed the way systems administration is done.

Possible Learning Path

Hands-on experience: Start by setting up a simple Kubernetes cluster and experimenting with different configuration management tools such as Kubernetes Resource Configs, ConfigMaps, and Secrets. This can be done by following tutorials and guides, and deploying these tools on a cloud platform like AWS, Azure, or GCP.

Theoretical learning: Once you have a basic understanding of configuration management, you can begin to explore the underlying concepts and technologies such as Kubernetes API objects, YAML manifests, and declarative configuration. This can be done through online resources such as tutorials, courses, and documentation provided by Kubernetes, as well as books and blogs on the topic.

Understanding the principles and best practices: Configuration management is an important aspect of a microservices architecture, so it’s important to understand the key principles and best practices of configuration management such as separation of concerns, immutability, and version control.

Joining a community: Joining a community of Kubernetes enthusiasts will help you connect with other people who are learning and working with configuration management for Kubernetes. This can be done through online forums, meetups, and social media groups.

Practice, practice, practice: As with any new technology, the best way to learn is by doing. The more you practice deploying and using configuration management tools in a Kubernetes cluster, the more comfortable and proficient you will become with the technology.

A Note from the Architect

In traditional IT systems, configuration management is often done manually, which can be time-consuming and error-prone. It can be a real pain to keep track of all the different configurations and make sure they’re up-to-date across all the servers.

But in Kubernetes, configuration management is built into the platform and can be easily managed using Kubernetes resources like ConfigMaps and Secrets. Just keep in mind that Kubernetes does not encrypt secrets, so it is recommended to use Vault for this purpose. This makes it much more efficient and reliable.

One of the main advantages of using configuration management in Kubernetes is that it allows you to easily manage and update configurations across all the components of your application. This includes environment variables, database connection strings, and other sensitive information.

Moreover, version control can be used for configurations, which makes it possible to roll back changes if something goes wrong (again, secrets should not be stored in the source code repository). This is especially beneficial in a production environment, where mistakes can have serious consequences.

Additionally, by keeping configurations in code, the process of updating and deploying them can be automated, making it easier to manage and maintain over time.

In conclusion, configuration management in Kubernetes enables efficient and reliable management of configurations across all components of an application, as well as version control and automation of configurations. It is like having a personal assistant who keeps all configurations organized and up-to-date, without needing to manually update them.

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Connect with Shawn

Connect with me on LinkedIn. It’s where I’m most active, and it’s the easiest way to connect with me.

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Understand Auto-Scaling to Maximize Cloud Native Application Development

As a new engineer, understanding auto-scaling is important for several reasons. It is a key component of cloud native application development, allowing for greater scalability and cost efficiency, better collaboration and DevOps culture, and better availability and resilience. Auto-scaling automatically adjusts the number of resources used by an application based on current demand, ensuring the system can handle a large number of requests while minimizing cost. It also makes it easier for different teams and developers to work together on the same application. Auto-scaling is a powerful tool for building and deploying applications in a cloud environment, and is essential for any engineer working in the field today.

As a new engineer, understanding the concept of auto-scaling is important for several reasons.

First, auto-scaling is a key component of cloud native application development. It is the process of automatically adjusting the number of resources (such as servers) used by an application based on the current demand. By understanding how auto-scaling works, you will be able to build, deploy, and manage cloud-native applications more effectively.

Second, auto-scaling allows for greater scalability and cost efficiency. By automatically adjusting the number of resources used by an application, it ensures that the system can handle a large number of requests while minimizing the cost of running the application. This makes it easy to handle the increasing demand for more computing power and storage.

Third, auto-scaling allows for better collaboration and DevOps culture. By automating the process of scaling resources, it becomes easier for different teams and developers to work together on the same application.

Fourth, auto-scaling allows for better availability and resilience, by automatically adjusting resources when traffic increases, it ensures that the application is always available to handle requests, and when traffic decreases, it reduces the resources which allows to save cost.

In summary, as a new engineer, understanding the concept of auto-scaling is important because it is a key component of cloud native application development, allows for greater scalability and cost efficiency, better collaboration and DevOps culture, and better availability and resilience. It is a powerful tool for building and deploying applications in a cloud environment and is essential for any engineer working in the field today.

Learning Materials

Here’s a list to get you started learning about auto-scaling. Note that some of these links may not be free and may require a subscription or payment. I receive no affiliate payments for these links.

Beginner:

Intermediate:

Advanced:

Videos to Watch

Kubernetes cluster autoscaling for beginners

Kubernetes is a powerful tool for deploying applications, but it’s important to understand the resource requirements of the applications and set resource requests and limits to allow the scheduler to make informed decisions when placing pods onto nodes. This will help ensure that the nodes are not over-utilized and that applications are given the resources they need.

A Possible Learning Path

Hands-on experience: Start by setting up a simple Kubernetes cluster and experimenting with different auto-scaling mechanisms such as Kubernetes Horizontal Pod Autoscaler and Cluster Autoscaler. This can be done by following tutorials and guides and deploying these services on a cloud platform like AWS, Azure, or GCP.

Theoretical learning: Once you have a basic understanding of auto-scaling, you can begin to explore the underlying concepts and technologies such as Kubernetes Horizontal Pod Autoscaler and Cluster Autoscaler. This can be done through online resources such as tutorials, courses, and documentation provided by Kubernetes, as well as books and blogs on the topic.

Understanding the principles and best practices: Auto-scaling is an important aspect of a microservices architecture, so it’s important to understand the key principles and best practices of auto-scaling, such as scaling policies, monitoring, and alerting.

Joining a community: Joining a community of Kubernetes enthusiasts will help you connect with other people who are learning and working with auto-scaling for Kubernetes. This can be done through online forums, meetups, and social media groups.

Practice, practice, practice: As with any new technology, the best way to learn is by doing. The more you practice deploying and using auto-scaling mechanisms in a Kubernetes cluster, the more comfortable and proficient you will become with the technology.

A Note from the Architect

Before I explain an auto-scaler, let me quickly tell you the story of the Porsches crashing into walls every 15 minutes.

Back when I was a newer developer working for the e-commerce department of an electronics parts seller, we had a web outage. We had no idea why the webserver was down. This was in the olden days when the best monitoring tool we had was named ELMA. It was better for debugging and development than anything you would want to put into production.

The e-commerce website was down and we developers were scrambling to find out why. The president of the company was standing in our cubicles, shaking his head and looking at his watch. Every few minutes he would proclaim loudly, “There goes another one.”

After a while, one of our more senior developers asked, “’Another one,’ what, sir?”

He replied, “You just crashed a brand new Porsche into a brick wall.”

That’s how much money we were losing every 15 minutes while the site was down.

We found out that the problem had nothing to do with our code. It was an issue with our servers. We didn’t have enough. We had recently opened up to new markets overseas and this added traffic was crashing our old Internet Information Server systems. We didn’t have auto-scaling.

Auto-scaling is like having a personal trainer for your infrastructure. It makes sure that your infrastructure is always in tip-top shape by automatically adjusting the number of resources (like servers or containers) based on the current demand.

In traditional IT environments, auto-scaling is often done manually, which can be time-consuming and error-prone. But in a cloud-native environment like Kubernetes, auto-scaling is built into the platform and can be easily configured with Kubernetes resources. This makes it a lot more efficient and reliable.

With Kubernetes, you can set up auto-scaling rules that specify how many resources you want to have available for a given service. For example, you can set a rule that says “if the CPU usage goes above 80% for more than 5 minutes, then add another server.” And when the demand goes down, the system will also automatically scale down the resources. This way, you can ensure that you always have the right amount of resources to handle the current load and also save money by not having unnecessary resources running.

Another benefit of using auto-scaling in a cloud-native environment is that it allows you to handle unexpected traffic spikes, such as a viral social media post or a news article. This way, you don’t have to worry about your service going down because of a sudden increase in traffic.

So, using auto-scaling in a cloud-native environment has many benefits, including increased efficiency, reliability and cost savings. Plus, it’s like having a personal trainer that makes sure your infrastructure is always ready for action. It might also save on the number of Porsches you crash into brick walls.

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Connect with Shawn

Connect with me on LinkedIn. It’s where I’m most active, and it’s the easiest way to connect with me.

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Understanding Containerization: A Guide for New Engineers

As a new engineer, understanding the concept of containerization is important for several reasons.

First, containerization is a key component of cloud native application development. Containers are a lightweight and portable way to package software, making it easy to run and manage applications on cloud infrastructure. By understanding how containers work, you will be able to build, deploy, and manage cloud-native applications more effectively.

Second, containerization allows for greater consistency and portability across different environments. With containers, you can package an application and its dependencies together, ensuring that it will run the same way regardless of where it is deployed. This eliminates the “works on my machine” problem and makes it easier to move applications between different environments.

Third, containerization allows for greater scalability and resource efficiency. Containers use less resources than traditional virtual machines, and can be easily scaled up or down as needed. This makes it easier to handle the increasing demand for more computing power and storage.

Fourth, containerization also allows for better collaboration and DevOps culture, as containers can be easily shared and reused, making it easier for different teams and developers to work together on the same application.

In summary, as a new engineer, understanding the concept of containerization is important because it is a key component of cloud native application development, allows for greater consistency and portability across different environments, enables greater scalability and resource efficiency, and promotes collaboration and DevOps culture. It is a powerful tool for building and deploying applications in a cloud environment and is essential for any engineer working in the field today.

Learning Materials

Here’s a list to get you started learning about containerization. Note that some of these links may not be free and may require a subscription or payment. I receive no affiliate payments for these links.

Beginner:

Intermediate:

Advanced:

Videos to Watch

Kubernetes Crash Course for Absolute Beginners

Kubernetes is an open source container orchestration framework designed to manage applications made up of hundreds or thousands of containers across multiple environments. It offers features such as high availability, scalability, and disaster recovery, as well as a virtual network that enables communication between pods. This video provides an overview of the Kubernetes architecture and components, and a use case of a web application with a database to illustrate how it works.

Possible Learning Path

Hands-on experience: Start by installing Docker and Kubernetes on your local machine or in a virtual environment. This can be done by following the official documentation provided by Docker and Kubernetes. After that, you can follow tutorials and guides to build and deploy simple applications in containers using Docker and Kubernetes.

Theoretical learning: Once you have a basic understanding of Docker and Kubernetes, you can begin to explore the underlying concepts and technologies. This can be done through online resources such as tutorials, courses, and documentation provided by Docker and Kubernetes, as well as books and blogs on the topic.

Joining a community: Joining a community of Docker and Kubernetes enthusiasts will help you connect with other people who are learning and working with these technologies. This can be done through online forums, meetups, and social media groups.

Practice, practice, practice: As with any new technology, the best way to learn is by doing. The more you practice building and deploying containerized applications using Docker and Kubernetes, the more comfortable and proficient you will become with these technologies.

Specialize in a specific use case: Docker and Kubernetes can be used in a wide variety of scenarios and use cases, so it is beneficial to specialize in one or two that align with your business or career goals.

A Note from the Architect

I’m trying to think of the first time I worked with a Docker container. I believe it was almost four years ago, which in technology timeframes was forever. I was trying to decide if I wanted to use Angular or React to create components for a plugin framework running on SharePoint. It wasn’t the type of development I was used to doing at the time, but I knew the industry was heading away from Angular and more toward React. So, I installed Docker on my laptop, learned how to check out the images, and eventually started learning the basics of containers. I was hooked. Here was a great way to build apps, host them locally, and get the same experience locally that I could expect in production. No more, “It worked on my machine.”

In the last couple of years, I’ve used the remote connection capabilities of VS Code to run pretty much all of my development in containers. It gives me the freedom to try out different languages, frameworks, and libraries without ever needing to install those on my local operating system. I’m proud to say that I never get bugged for Java or .Net updates now. I just get the latest images and add a volume that connects to a local folder where I manage my Git repositories. It’s made my life as a developer much easier.

If you’re wondering, “What’s the big deal with containers? I just want to write code. Why do I need to use containers?” I’ll try to answer that question. Because we don’t just write code anymore. As developers and as operations engineers, we’re beginning to move into a phase where we are sharing the overall solution. This means that when I create something or have a hand in creating something, I have ownership over that thing. I’m responsible for it.

Now, you might work for an enterprise that’s still a bit behind the times. You may write code, and some other team might test that code, and then some other team might try to deploy that code. In that situation, you probably aren’t using containers or anything that looks like modern DevOps. And in that situation, the team between you and the customers who will derive value from your code is a bottleneck. If you rely on a QA team, they will find bugs, because that’s what they are incentivized to do. It’s their job to compare your code against some form of requirements and fail it if it doesn’t meet those requirements. Operations in this type of environment is incentivized to keep systems running smoothly, so they’ll look for any excuse to deny your code entry into production—that usually looks like a set of meetings designed to make sure you met all the criteria needed for something to go into production.

That is the old way of developing software. Let me tell you, if you’re working some place like that, get out. No. Seriously. Leave. Find a better job.

I believe this is the way software should be developed:

The Most Powerful Software Development Process Is The Easiest

In an ideal software development process, the only work done is understanding the problem, writing code to solve it, testing to confirm it is solved, and making progress in small steps to retain the ability to change the system when needed. The goal is to minimize work and maximize learning, allowing for changes to be made easily and with confidence.

Containers make this process easier. Your code remains modular, making it easier to version and manage libraries and dependencies. You can even build out the needed infrastructure for a container management system, such as Kubernetes, without involving operations in some cases.

As a developer and an architect, I have found that containers have improved the quality of my development life. They have allowed me to have more control over the solutions I deliver. I believe that if you start working regularly with containers, you will feel the same way too.

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Connect with Shawn

Connect with me on LinkedIn. It’s where I’m most active, and it’s the easiest way to connect with me.

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AI Edge Engineering at University of Oxford

This week I’m wrapping up a continuing education course from Oxford focused on the approaches and tools of the AI Edge Engineering discipline. I’m overwhelmed with how much I’ve learned and how much I still have to learn. Without a doubt, this course will help set the direction of my career moving forward.

I wasn’t new to the concept of AI Edge Engineering. Before entering the course I had already passed my AZ-220 and AI-100. Two certifications I saw as fundamental to practicing AEE on the Azure platform. These certifications focus on Azure’s IoT tools with the AZ-220 and Azure’s AI services and Machine Learning tools with AI-100. It’s important to note that the AI-100 primarily focuses on applying existing AI in solutions. It only covers Data Science from a high level; you might be expected to understand what clustering, categorizing, and regression are, but you aren’t expected to build them from scratch or use any DS tools to build them for the certification test. That’s appropriate for a 100 level certification. Despite having these certifications, I wasn’t ready for the depth we would take on in the course in such a short period.

Luckily, the course used cohort learning as a mechanism to complete some of the more challenging projects. Our group efforts afforded us the opportunity to trade opinions, approaches, and skills to achieve project deliverables. This is also a skill in the AEE field. Few people will have all the skills needed to apply artificial intelligence at the edge. This means that organizations who wish to use AEE will need to team members who have varied specialized skills and knowledge of the bigger picture of AEE. Our projects made this very clear to me.

I won’t go into detail on what we learned and how we learned it, because much of that is the IP of the course and Oxford, but I will say that we dived deep in the following general areas:

  • The basic concepts of delivering AI to the edge using IoT and Edge platforms
  • Cloud- all the clouds (Azure, AWS, GCS)
  • Cloud concepts like PaaS, SaaS, and Cloud Native Services
  • All the big pieces of Machine Learning and Machine Learning concepts
  • 5G networks
  • Device design and development
  • DevOps, MLOPS, and even AIOPs

That doesn’t even cover all the guest lectures and insights into AI and AEE application demonstrated. And without the fantastic instruction of course director Ajit Jaokar and his amazing team of tutors and instructors, we wouldn’t have been able to learn so much in such a short period of time. Ajit’s passion for this specialty was clear in every class, which made it a joy to attend. It was definitely worth waking up at 3 AM to attend a class remotely just to spend the time with others who have such a strong appreciation for this burgeoning discipline. This course succeeds because of the people behind it. I have to include the choice of students as well in that success. My study group was full of passionate, knowledgeable, curious, and delightful professionals. We plan to stay in contact well after the course ends. I expect to see amazing accomplishments from their careers.

We wrap up the course on Tuesday and submit all our final homework projects over the next couple of months. I won’t be officially done with the course until May. However, I won’t ever consider myself officially done with AEE, from a learning perspective. I’m taking what I’ve learned from Oxford and building a continuing learning track to master most of the concepts covered in the course.

It’s even clearer to me now that the engineering skills of delivering greater levels of intelligence closer to the source of events and data, so that they may act upon those events and data is a skillset that will be in higher demand in the coming years. I believe this course has helped me to begin building a better roadmap toward mastery of AI Edge Engineering.