In recent years, Kubernetes has emerged as the go-to solution for orchestrating containerized applications at scale. But when it comes to deploying AI workloads, does it offer the same level of efficiency and convenience? In this blog post, we delve into the types of AI workloads that are best suited for Kubernetes, and why you should consider it for your next AI project.
Model Training and Development
Batch Processing
When working with large datasets, batch processing becomes a necessity. Kubernetes can efficiently manage batch processing tasks, leveraging its abilities to orchestrate and scale workloads dynamically.
Example: A machine learning pipeline that processes terabytes of data overnight, utilizing idle resources to the fullest.
Hyperparameter Tuning
Hyperparameter tuning involves running numerous training jobs with different parameters to find the optimal configuration. Kubernetes can streamline this process by managing multiple parallel jobs effortlessly.
Example: An AI application that automatically tunes hyperparameters over a grid of values, reducing the time required to find the best model.
Model Deployment
Rolling Updates and Rollbacks
Deploying AI models into production environments requires a system that supports rolling updates and rollbacks. Kubernetes excels in this area, helping teams to maintain high availability even during updates.
Example: A recommendation system that undergoes frequent updates without experiencing downtime, ensuring a seamless user experience.
Auto-Scaling
AI applications often face variable traffic, requiring a system that can automatically scale resources. Kubernetes’ auto-scaling feature ensures that your application can handle spikes in usage without manual intervention.
Example: A voice recognition service that scales up during peak hours, accommodating a large number of simultaneous users without compromising on performance.
Data Engineering
Data Pipeline Orchestration
Managing data pipelines efficiently is critical in AI projects. Kubernetes can orchestrate complex data pipelines, ensuring that each component interacts seamlessly.
Example: A data ingestion pipeline that collects, processes, and stores data from various sources, running smoothly with the help of Kubernetes orchestration.
Stream Processing
For real-time AI applications, stream processing is a crucial component. Kubernetes facilitates the deployment and management of stream processing workloads, ensuring high availability and fault tolerance.
Example: A fraud detection system that analyzes transactions in real-time, leveraging Kubernetes to maintain a steady flow of data processing.
Conclusion
Kubernetes offers a robust solution for deploying and managing AI workloads at scale. Its features like auto-scaling, rolling updates, and efficient batch processing make it an excellent choice for AI practitioners aiming to streamline their operations and bring their solutions to market swiftly and efficiently.
Whether you are working on model training, deployment, or data engineering, Kubernetes provides the tools to orchestrate your workloads effectively, saving time and reducing complexity.
To get started with Kubernetes for your AI projects, consider exploring the rich ecosystem of tools and communities available to support you on your journey.
Automation in managing Kubernetes clusters has burgeoned into an essential practice that enhances efficiency, security, and the seamless deployment of applications. With the exponential growth in containerized applications, automation has facilitated streamlined operations, reducing the room for human error while significantly saving time. Let’s delve deeper into the crucial role automation plays in managing Kubernetes clusters.
Section 1: The Imperative of Automation in Kubernetes
1.1 The Kubernetes Landscape
Before delving into the nuances of automation, let’s briefly recapitulate the fundamental components of Kubernetes, encompassing pods, nodes, and clusters, and their symbiotic relationships facilitating a harmonious operational environment.
1.2 The Need for Automation
Automation emerges as a vanguard in managing complex environments effortlessly, fostering efficiency, reducing downtime, and ensuring the optimal utilization of resources.
1.2.1 Efficiency and Scalability
Automation in Kubernetes ensures that clusters can dynamically scale based on the workload, fostering efficiency, and resource optimization.
1.2.2 Reduced Human Error
Automating repetitive tasks curtails the scope of human error, facilitating seamless operations and mitigating security risks.
1.2.3 Cost Optimization
Through efficient resource management, automation aids in cost reduction by optimizing resource allocation dynamically.
Section 2: Automation Tools and Processes
2.1 CI/CD Pipelines
Continuous Integration and Continuous Deployment (CI/CD) pipelines are at the helm of automation, fostering swift and efficient deployment cycles.
Code snippet 3: Using Ansible for configuration management.
Section 3: Automation Use Cases in Kubernetes
3.1 Auto-scaling
Auto-scaling facilitates automatic adjustments to the system’s computational resources, optimizing performance and curtailing costs.
3.1.1 Horizontal Pod Autoscaler
Kubernetes’ Horizontal Pod Autoscaler automatically adjusts the number of pod replicas in a replication controller, deployment, or replica set based on observed CPU utilization.
Code snippet 5: Configuring a rolling update strategy in a Kubernetes deployment.
Conclusion: The Future of Kubernetes with Automation
As Kubernetes continues to be the frontrunner in orchestrating containerized applications, the automation integral to its ecosystem fosters efficiency, security, and scalability. Through a plethora of tools and evolving best practices, automation stands central in leveraging Kubernetes to its fullest potential, orchestrating seamless operations, and steering towards an era of self-healing systems and zero-downtime deployments.
In conclusion, the ever-evolving landscape of Kubernetes managed through automation guarantees a future where complex deployments are handled with increased efficiency and reduced manual intervention. Leveraging automation tools and practices ensures that Kubernetes clusters not only meet the current requirements but are also future-ready, paving the way for a robust, scalable, and secure operational environment.
Harnessing the Möbius Loop for a Revolutionary DevOps Process
In the world of DevOps, continual improvement and iteration are the name of the game. The Möbius loop, with its one-sided, one-boundary surface, can serve as a vivid metaphor and blueprint for establishing a DevOps process that is both unified and infinitely adaptable. Let’s delve into the Möbius loop concept and see how it beautifully intertwines with the principles of DevOps.
Understanding the Möbius Loop
The Möbius loop or Möbius strip is a remarkable mathematical concept — a surface with only one side and one boundary created through a half-twist of a strip of paper that then has its ends joined. This one-sided surface represents a continuous, never-ending cycle, illustrating an ever-continuous pathway that can epitomize the unceasing cycle of development in DevOps.
In the ecosystem of DevOps, the Möbius loop signifies a continuous cycle where one phase naturally transitions into the next, establishing a seamless feedback loop that fosters continuous growth and development. This philosophy lies at the heart of DevOps, promoting an environment of collaboration and iterative progress.
Building a DevOps process based on the Möbius loop principle means initiating a workflow where each development phase fuels the next, constituting a feedback loop that constantly evolves. Here is a step-by-step guide to create this iterative and robust system:
1. Define Objectives
Business Objectives: Set clear business goals and metrics.
User Objectives: Align the goals with user expectations.
2. Identify Outcomes
Expected Outcomes: Envision the desired outcomes for business and users.
Metrics: Design metrics to measure the effectiveness of strategies.
3. Discovery and Framing
Research: Invest time in understanding user preferences and pain points.
Hypothesis: Develop hypotheses to meet business and user objectives.
4. Develop and Deliver
Build: Employ agile methodologies to build solutions incrementally.
Deploy: Use CI/CD pipelines for continuous deployment.
Monitor: Utilize monitoring tools to collect data on system performance.
Feedback Loop: Establish channels to receive user feedback.
6. Learning and Iteration
Analyze: Scrutinize data and feedback from the operate and observe phase.
Learn: Adapt based on the insights acquired and enhance the solution.
7. Feedback and Adjust
Feedback: Facilitate feedback from all stakeholders.
Adjust: Revise goals, metrics, or the solution based on the feedback received.
8. Loop Back
Iterative Process: Reiterate the process, informed by the learning from previous cycles.
Continuous Improvement: Encourage a mindset of perpetual growth and improvement.
Tools to Embark on Your Möbius Loop Journey
Leveraging advanced tools and technologies is vital to facilitate this Möbius loop-founded DevOps process. Incorporate the following tools to set a strong foundation:
Version Control: Git for source code management.
CI/CD: Jenkins, Gitlab, or ArgoCD for automating deployment.
Containerization and Orchestration: Podman and Kubernetes to handle the orchestration of containers.
Monitoring and Logging: Tools like Prometheus for real-time monitoring.
Collaboration Tools: Slack or Rocket.Chat to foster communication and collaboration.
Embracing the Möbius loop in DevOps unveils a path to continuous improvement, aligning with the inherent nature of the development-operations ecosystem. It not only represents a physical manifestation of the infinite loop of innovation but also fosters a system that is robust, adaptable, and user-centric. As you craft your DevOps process rooted in the Möbius loop principle, remember that you are promoting a culture characterized by unending evolution and growth, bringing closer to your objectives with each cycle.
Feel inspired to set your Möbius loop DevOps process in motion? Share your thoughts and experiences in the comments below!
Recently there has been a shift in language surrounding System Reliability Engineering (SRE) and DevOps to Platform Engineering. Granted these terms have been used in various ways for a while, but how language terms are used gives way to how markets evolve. This post provides a few key areas of thought around ways to ultimately get products to production faster. Remember…code means nothing until it’s in production.
No matter the title, anyone in the pipeline touching production code is part of the team of ensuring success of critical applications in an enterprise. This is an important concept because everyone is part of the larger team and how teams work together ultimately determines the success of any project.
The focus here will be on the actual development team who is primarily writing the code. The code in question would be delivered as microservices running on a K8S cluster. Keep in mind the use of microservices will lend itself to multiple teams individually creating a service for other teams to consume. Already there is significant dependencies and a single line of code has yet to be written.
Each team ultimately needs to consume one or more code repositories, one or more “testing” systems, at least one pipeline for continuous integration, continuous delivery/deployment (CI/CD), and many other systems to get code to production.
The Platform Engineering team is ultimately responsible for ensuring the “platforms” are working in a way to support the developers. Ensuring a great experience is paramount.
The question is how do Platform Engineers continually improve the great developer experience? The answer many teams turn to is to create powerful systems with guardrails or opinions on how they are to be utilized based on the collective understanding of the teams modus operandi or how they work most effectively.
The key to how is reducing the repetitive work, the mundane menial tasks which take a toll on the cognitive workload of developers allowing them to be able to focus on writing good, clean code.
Giving the power to the developers to consume what is needed in a self-service fashion is one major step as is giving a limited set of choices in what toolsets to use. Make it easy for developers to build and deliver software without removing the useful capabilities of the core services.
In the ideal world, limit restrictions on the how allowing choices in using GitOps or ClickOps or using a API vs CLI vs UI. Use a “as a service” approach to create a system built iteratively by the entirety of the team based on direct feedback.
What it all comes down to is the fact that everyone has different ways they want to work. Its the platform engineering team who can help ensure all of the tools are available and functional to create a great developer experience which in turn will increase productivity and get new, shiny things to market faster.
In most enterprises on boarding new talent is typically left to the new employee. This is very unfortunate because the first 90 days of a new role will impact not only the new employee, but their immersion into the culture and their view of the company. Bottom line, in most cases it is up to the new employee to “learn the ropes” in navigating their new position.
Starting a new role? Maybe this is the first foray into DevOps or Platform Engineering? What is needed to “hit the ground running” in a new role? Leaders in high positions of a company typically have a “100 day rule” to prove themselves. Let’s round it out with 3 months of progress for success.
In most enterprises on boarding new talent is typically left to the new employee. This is very unfortunate because the first 90 days of a new role will impact not only the new employee, but their immersion into the culture and their view of the company. Bottom line, in most cases it is up to the new employee to “learn the ropes” in navigating their new position.
The first 30 days
This month is usually the most important for everyone. The first thing a new employee needs to do is find a good mentor especially if they are not assigned one. Seek out those with institutional knowledge who knows how to navigate the company politics. Find someone who knows how the systems work, how to gain the access needed to be successful in the role. The mentor would have knowledge of “how things work” and what is seen as best practice for accomplishing the tasks at hand.
Some things to know:
Who’s who in the organization? – an org chart
How mature are they as a development organization?
What are the processes to put code into production?
Are the processes manual or automated?
What is the expectation of you on a day to day basis?
There is plenty more to uncover, but this will help to get started. Once the processes are understood and access is granted to perform the role, find some quick wins. Listen closely to where the frustrations may lie within your organization. Maybe the previous employee in this role didn’t automate certain tasks…submit a small PR to help.
It’s important to find some quick wins for many reasons. First it helps “break the ice”. It also shows strengths. Maybe there’s a way to improve some docs. There may be some ideas brought in from previous experience to help with a particular pain point.
The first 30 days is important to uncover the expectations of the team. Talking to stakeholders and “the customer” is important to get a big picture of what works and what doesn’t in order to find quick wins to make an impact early.
Days 30-60
The first 4 weeks are usually greeted with firehose sessions daily. Take a bit to digest everything. Review notes, brainstorm ideas, understand how the team and the company works. Armed with the broader knowledge about the organization, the team, and how things work at a high level it’s time to dig deeper into where the biggest impacts can be achieved.
In this 30 day block uncover:
The maturity of the team?
What is the approval process for delivering code to production?
What steps are needed to approve PRs?
How does code flow through the various systems?
What amount of QA is performed?
Find ways to help the team be more efficient. Listen to the complaints and see where possible improvements could be made. Again, quick wins are key at this stage. As a fresh face, a lot of times gaining access to otherwise inaccessible groups within the organization is usually fairly easy. Keep an ear to the ground to find ways to create impactful suggestions
It is important to remember as people get to know a new employee the interactions have lasting impacts. Ensure there is adequate listening and relevant questions to get underneath a complaint. Avoid making off hand suggestions, but rather find some common issues. Start to tackle the common issues and socialize improvements. The key here to to avoid “calling the baby ugly”.
Days 60-90
This is where a new employee’s impact can accelerate. At this stage having the access needed to be successful would be complete. Hopefully there’s been a few quick wins, new co-workers are impressed, and there’s been positive impact on the team.
Regular interaction with your leader would have been established. A solid understanding of what is expected is created and the mentor has made an impact. Knowing where to go to get answers if there is a roadblock and knowing how to avoid the “potholes in the road” is key.
This stage is where the “rubber hits the road”. Gaining traction in the day to day and making regular impact to the business is routine at this point. This is where all of the knowledge gained in the first 60 days can be parlayed into a winning hand.
What success looks like
The first 3 months of any new position sets the stage for every new employee. Creating a positive impression on the team helps build credibility within the broader organization and is key to instilling the confidence needed to being successful overall.
It may take far more than 90 days to feel comfortable with the role and that is okay. As long as there is a consistent method for learning and mistakes are not repeated the impact new employees make is usually sustainable for a long time. Make the best of it and keep track of the wins and losses for the inevitable review with “the boss”.
Kubernetes is an open-source container orchestration system that automates the deployment, scaling, and management of containerized applications. It is widely used for its ability to manage containers at scale and is the de facto standard for container orchestration. However, despite its broad adoption, there are still a few missing pieces that need to be addressed to make it fully functional.
Kubernetes is an open-source container orchestration system that automates the deployment, scaling, and management of containerized applications. It is widely used for its ability to manage containers at scale and is the de facto standard for container orchestration. However, despite its broad adoption, there are still a few missing pieces that need to be addressed to make it fully functional.
Network Setup
One of the main missing pieces in Kubernetes is a proper network setup. Kubernetes allows for the creation of multiple clusters, each with its own set of nodes, but it requires a well-defined network setup to manage communication between these clusters.
Without proper network setup, nodes in the same cluster may not be able to communicate with each other, or there may be issues with cross-cluster communication. This can result in application downtime, loss of data, and other issues that can impact business operations.
One solution to this problem is to use a software-defined networking (SDN) approach that allows for the creation of a virtual network infrastructure. An SDN controller can be used to manage the virtual network infrastructure and provide network services such as load balancing, routing, and security. With SDN, Kubernetes clusters can be properly connected, and communication between clusters can be streamlined.
Security
Another missing piece in Kubernetes is security. Kubernetes provides some basic security features such as role-based access control (RBAC) and network policies, but these are not always enough to secure the entire system.
Security is a critical aspect of any container orchestration system, and Kubernetes is no exception. Kubernetes clusters are complex systems with many components, and securing them requires a multi-layered approach.
To enhance security, Kubernetes clusters should be set up with secure communication channels and encrypted data storage. Additionally, it is important to create and enforce security policies that prevent unauthorized access to the system. This includes implementing identity and access management (IAM) policies, network segmentation, and regular vulnerability scanning.
Monitoring and Logging
Kubernetes also lacks inbuilt monitoring and logging capabilities. While Kubernetes includes some basic monitoring features, such as health checks and resource usage metrics, it does not provide comprehensive monitoring and logging capabilities.
In a production environment, it is essential to have comprehensive monitoring and logging capabilities to ensure the health and availability of the system. Kubernetes clusters should be set up with a logging and monitoring stack that can collect and analyze logs and metrics from all nodes in the cluster. This can provide insights into the health and performance of the system, as well as help identify and troubleshoot issues.
Conclusion
Kubernetes is a powerful container orchestration system, but there are still a few missing pieces that need to be addressed to make it fully functional. A well-defined network setup, enhanced security, and proper monitoring and logging are all essential components of a fully functional Kubernetes environment.
With the increasing adoption of containers and cloud-native applications, Kubernetes is becoming more important than ever. As organizations continue to adopt Kubernetes, it is important to ensure that the missing pieces are addressed to provide a reliable and scalable platform for containerized applications. By addressing these missing pieces, Kubernetes can continue to evolve and improve, providing a robust and secure platform for developers and IT teams.
In working with a handful of customers the concept of GitOps continues to resonate more and more. Let us dive into a brain dump of some of the conversations related to GitOps and how these customers tackled the task at hand.
First thing to remember is these customers are not massive. They are rather common actually. A Gartner defined “medium-sized” enterprise. Keep in mind these customers have the same issues as the giant enterprises just at a different scale.
In every case there was a user story. At a high level, a common theme was the need to roll out updates to a specific application regularly enough to find ways to entice the consumer to purchase a widget of some sort. Ok, A/B testing. Simple enough.
Each of the customers were in different maturity levels when it came to development processes, kubernetes knowledge, and devops methods. However they all have one thing in common…the need to deliver an application to their customer base on a deadline and continuously improve the application based on user feedback. All three of them were successful in meeting their self imposed deadlines. How?
Simple. Every one of them came together, ironed out a plan, and implemented the plan. The interesting part, every one of them already knew how to get the product to market. All they needed was a bit of guidance on how to overcome obstacles and get shit done. How?
Step one. Define the top of the mountain, the finish line, the end result.
Step two. The project leads built out a high level timeline from end to beginning.
Step three. All of the team members came together to build out the task teams.
Step four. Each of the teams built out their respective timeline for contribution.
Step five. Build.
Now how does this relate to GitOps? GitOps was the pivotal methodology to get it done. The pipeline was built with all of the parts in mind. If you recall the DevOps “infinity loop“, the key is to use that and combine it with the OODA loop decision model. The combination creates a very powerful decision making framework facilitating agile development with constant improvement. Sound simple? It’s not. It is in theory, but the implementation is like a relationship. Everything is great when dating, but the hard work is when dating turns to marriage. Same goes for creating a product. Designing the product, what it needs to do, all of the moving parts is fun. The real work comes in when the first working build is complete.
This is where GitOps shines. The developers build things, test locally, and commit. The pipelines move it through the process and all of the other teams contribute to each part in this machine. If one part breaks down, the other work stops to crowdsource the problem. The problem is fixed and the machine continues on. GitOps is the magical fairy dust. What about the technology?
The technology is rather mundane actually. Git. A code repository. A CI/CD pipeline. A build system. A test harness. A deployment platform. Git…the tools of choice are Github or Gitlab. Github is pretty slick, but Gitlab will allow for running locally in small environments building closed source deliverables. Each has a pipeline mechanism or there are many other tools such as Texton, Argo, CircleCI and many others with various features depending on what was needed. For build systems, many exist and again each as features as needed. However the deployment platform consistently remains the same, Kubernetes.
Building deploy-able applications at scale is hard. There are many other moving parts, processes, tools, etc. in play. However one thing stands out in all of these engagements…give the right people who have the will to succeed the skills needed to succeed and the execution part will look easy.
It is always fun to be a part of something, but its most precious reward is being able to step away and watch the machine run on it’s own.
That’s the end of this spew. It went everywhere…maybe it’s more like a sneeze.
Kubernetes is a powerful tool for orchestrating containerized applications across a cluster of nodes. It provides users with two methods for managing the desired state of their applications: the Declarative and Imperative approaches.
The imperative approach
The Imperative approach requires users to manually issue commands to Kubernetes to manage the desired state of their applications. This approach gives users direct control over the state of their applications, but also requires more manual effort and expertise, as well as a more in-depth understanding of Kubernetes. Additionally, the Imperative approach does not provide any version control or rollback capabilities, meaning that users must be more mindful of any changes they make and take extra care to ensure they are not introducing any unintended consequences.
A simple set of imperative commands to create a deployment
To create a Kubernetes deployment using the Imperative approach, users must issue the following commands:
Create a new deployment named my-deployment and use the image my-image:
In the Declarative approach, users express their desired state in the form of Kubernetes objects such as Pods and Services. These objects are then managed by Kubernetes, which ensures that the actual state of the system matches the desired state without requiring users to manually issue commands. This approach also provides version control and rollback capabilities, allowing users to easily revert back to a previous state if necessary.
Below is an example Kubernetes deployment yaml (my-deployment.yaml) which can be used to create the same Kubernetes deployment:
To create or update the deployment using this yaml, use the following command:
kubectl apply -f my-deployment.yaml
Infrastructure as Code
The primary difference between the Declarative and Imperative approaches in Kubernetes is that the Declarative approach is a more automated and efficient way of managing applications, while the Imperative approach gives users more direct control over their applications. Using a Declarative approach to Kubernetes gives rise to managing Infrastructure as Code which is the secret sauce in being able to maintain version control and rollback capabilities.
In general, the Declarative approach is the preferred way to manage applications on Kubernetes as it is more efficient and reliable, allowing users to easily define their desired state and have Kubernetes manage the actual state. However, the Imperative approach can still be useful in certain situations where direct control of the application state is needed. Ultimately, it is up to the user to decide which approach is best for their needs.
Dev containers are a powerful tool for developers to use when coding, testing, and debugging applications. VSCode provides an integrated development environment (IDE) for developers to use when working with dev containers. This guide will show you how to get started with dev containers in VSCode and how to use them to your best advantage.
Install the Remote – Containers extension
Create a dev container configuration file
Launch the dev container
Connect to the dev container
Start coding!
Installing the Remote – Containers Extension
The first step to using dev containers is to install the Remote – Containers extension. This extension allows you to create dev container configurations and launch them from within VSCode. To install the extension, open the Extensions panel in VSCode and search for Remote – Containers. Click the ‘Install’ button to install the extension. After installation, you will need to restart VSCode for the extension to take effect.
Creating a Dev Container Configuration File
Once the Remote – Containers extension is installed, you can create a dev container configuration file. This file will define the environment for your dev container. For example, you can define the programming language, libraries, and other settings for your dev container. You can also specify a base image to be used by the dev container, such as a Linux or Windows image.
Example Dev Container Configuration File
Below is an example of a dev container configuration file. This configuration file specifies a base image of Ubuntu 18.04, a programming language of Python, and a library of TensorFlow.
Once your dev container configuration file is created, you can launch the dev container. To do this, open the Remote – Containers view in VSCode. You should see your dev container configuration file listed. Click the Launch button to start the dev container. Once the dev container is launched, you will be able to access a terminal window, allowing you to control the dev container.
Connecting to the Dev Container
Once the dev container is running, you can connect to it. To do this, open the Remote – SSH view in VSCode. You should see your dev container listed. Click the Connect button to connect to the dev container. Once connected, you will be able to access the dev container’s terminal window and run commands.
Start Coding!
Now that you’ve connected to the dev container, you can start coding! You can use the integrated development environment (IDE) to write, debug, and test your code. This allows you to work on your project within the dev container, without the need for additional setup. Once you’re done, you can close the dev container and move on to the next project.
As a platform engineer, you will be tasked with ensuring that software projects run smoothly and efficiently. This includes managing all of the various components needed to develop and launch an app or system.
This may include choosing your development platform, setting up servers for hosting, finding vendors for integrations, and more. Beyond just technology, there are also time-management strategies to manage your day.
As you can imagine, being a part of this team comes with its own set of challenges and responsibilities. Being a senior member of the team takes some work — but it is totally worth it!
Being a senior developer means that you know what’s going on technologically, and you’re able to teach others that knowledge. You’ll have the opportunity to make an impact by helping other engineers get things done effectively.
It’s also important to remember that not everyone has a career in tech like you do, so don’t feel like you need to take over every task.
Plan your career
As mentioned earlier, being a successful platform engineer is more than just knowing how to work in different environments having separate and distinctly different conversations with various people about what tools you use for each environment, and how to manage your time effectively between projects and departments. It also means planning your career ahead of time.
Most experienced devs will tell you that it’s impossible to become a senior developer without first becoming a team lead or manager. This makes sense, because you need to establish relationships within the department and organization that depend on you for leadership, guidance, and resources.
And since most tech companies are moving towards agile project management systems like Scrum, you need to be able to facilitate those meetings, talk about logistics, and make sure everyone has everything they need to complete their assignments on schedule.
All of these things require someone who leads by example, sets appropriate priorities, and keeps people motivated so that they feel comfortable coming to you for help when needed. You can’t expect anyone lower up the chain-of-command to do this if they don’t see you putting in the effort into developing your leadership skills.
It’s totally normal to want to move up the ladder, but making changes to your position requires proving that you’re capable of doing so while still keeping quality control over your tasks and setting realistic deadlines.
Create a good work-life balance
Being a platform engineer means having close relationships with other people in your organization that depend on you for success, so make sure you are not overworking yourself.
It is important to enjoy what you do for a living, and staying motivated requires finding time to unwind and relax. You can’t expect to be passionate about your job if you don’t take care of yourself outside of work.
You need to recognize that it takes a lot out of you, both physically and mentally, and give yourself breaks to keep up momentum. Take frequent vacations, organize social events outside of work, and try to limit how many hours you spend working every day.
In addition to taking care of yourself, use the same energy you put into your career on your personal life to promote healthy relationships and friendships.
Having friends who treat you well will help you feel more balanced and confident in your position, and they will help you stay happier at home. Try to meet someone new once a week, if possible, to maintain strong ties with family members and colleagues.
Be a creative thinker
As a platform engineer, you will be tasked with many different projects and responsibilities. This can make it hard to know what your next job is! As such, being able to think outside of the box and brainstorm new ideas and solutions to current issues is important.
You will have to use your creativity to figure out how to best solve these problems.
A great way to hone this skill is by taking part in various tech events or masterclasses. You may even get chance to present your own project or idea!
By attending events like these, not only do they give you knowledge about the field, but also lots of opportunities to improve yourself professionally.
Stay up-to-date with technology
As a platform engineer, you will be tasked with keeping up with new technologies. This is not only important for your career, but also to satisfy users’ demands for quality services they can rely on.
As more companies implement automation via software or robotics, there are always new tools being used for this process. You will need to know what these tools are and how to use them.
You will also have to stay up-to-date with the ever-evolving practices in both development and operations. For example, when developers adopt newer agile methodologies like scrum, you must understand why that approach is better than the traditional waterfall model.
Likewise, understanding lean startup methodology means knowing why it works and if it applies to your organization. These concepts help bring down the overhead of launching an app by reducing the number of steps needed to get things going.
By staying informed about such developments, you will make sure apps launch as quickly as possible while still meeting standards.
Be a good leader
As a platform engineer, you will be in charge of many different departments within your organization. You can expect that not every department in your own company or others you work with have strong leadership qualities.
If this is something that worries you, don’t worry! It’s totally normal.
It takes time to develop leadership skills, but you are never too young or old to learn them.
You can always look to those around you for examples of how to lead and learn from their mistakes. There are several books and courses available online and through educational institutions that focus on developing leadership abilities.
There are also mentorship programs where experienced leaders share knowledge and resources with younger people. By being part of these groups, you could gain some valuable lessons yourself.
Be a good team member
As a platform engineer, your success will depend on how well you collaborate with others. You can’t have successful collaborations if one party is constantly trying to get their way or take over the process.
This isn’t helpful for anyone involved in the project, and it won’t set well with leadership either. Make every effort to work with people around you using sound reasoning and evidence, and keep discussions focused on topics that are productive and positive.
Avoid gossip unless you want to deal with a lot of resentment later, and be willing to go beyond what is practical or appropriate to help someone feel better about themselves or the situation.
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The technical storage or access that is used exclusively for statistical purposes.The technical storage or access that is used exclusively for anonymous statistical purposes. Without a subpoena, voluntary compliance on the part of your Internet Service Provider, or additional records from a third party, information stored or retrieved for this purpose alone cannot usually be used to identify you.
Marketing
The technical storage or access is required to create user profiles to send advertising, or to track the user on a website or across several websites for similar marketing purposes.
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