Introduction
Creating a proof of concept is one thing, but being able to maintain it as a product and scale it in the cloud for clients is an entirely different skillset. Thus, there is an increasing demand for AI forward deployed engineering with cloud and DevOps, which covers the complete product lifecycle of an idea from inception to a reliable business-ready solution.
This article aims to give insight into how these areas tie together, what skills one should have, and how this discipline is right for an ambitious engineer.
What Exactly Is AI Forward Deployed Engineering?
Forward Deployed Engineer (FDE) is a person who works closely with a client to solve their business problem. Unlike a traditional software engineer, FDE does not rely solely on a specification document but rather learns the intricacies of the client’s operations to propose a solution.
However, when it comes to AI, customers usually do not know what a model can or cannot do, which means that the FDE has to guide them and ensure that expectations are not misplaced. Therefore, technical proficiency and communication skills are vital.
The Importance of Cloud Technology
An FDE’s creation will most likely never live on one’s personal computer, as customers will want it to be hosted in the cloud.
This practice allows clients to provide a convenient and reliable experience to their users 24/7. In addition, the cloud gives the FDE access to a wide range of infrastructure resources via platforms such as AWS, Azure, or Google Cloud. This means that one can create products with significant scalability and security without investing heavily in servers and data centers.
Thus, cloud technology is essential for an FDE, as it enables one to provide hosting services, store and secure data, scale systems up and down depending on demand, operate under a strict budget, and ensure that the product is reliable and hardened against attacks. However, to achieve these goals, one needs to know the tools that the cloud provides.
What Is DevOps, and Why Should An FDE Know It?
DevOps is the practice of standardizing the development cycle to ensure that projects go live smoothly and can be maintained effortlessly once they launch. In other words, it lets developers and operations personnel collaborate and automate repetitive tasks.
A common scenario that showcases the importance of DevOps is a situation where one has to push out a hotfix for a client’s issue. Without the proper DevOps tools, the FDE would have to manually reconfigure the code, which could lead to errors. Meanwhile, with automation, a working solution could be deployed within hours.
Moreover, DevOps ensures version control, continuous monitoring, and system integrity – all of which are essential for an FDE, who constructs a product based on a continuously evolving model.
Tools That Are Common in Cloud and DevOps Space
An FDE is not required to be an expert in all of the cloud and DevOps tools, but it is a good idea to learn the fundamentals and have a general understanding of what each one does. The following is a list of the most popular technologies:
Git and GitHub – version control and collaboration.
Docker – deploying applications in containers.
Kubernetes – operating in-container applications at scale.
CI/CD – automatic testing and deployment of code.
Terraform – cloud infrastructure as code.
Monitoring tools – keep track of everything.
In short, one should start learning Git and Docker and then move on to other tools that interest them.
How Does An FDE Deploy An AI-Based Solution?
Typically, such products are deployed on standard cloud accounts. To have a general idea of the process, one can imagine that there are stages that an FDE has to get through.
Understand the problem: communicate with the client and ensure that the product will meet their expectations.
Build the solution: construct the application, write the Python code, integrate it with APIs, and train the model.
Package the application in a Docker container.
Set up the CI/CD pipeline.
Launch the application on the cloud.
Monitor the performance.
Iterate on the product.
As can be seen from the above, the work of an FDE is not limited to simply training the model but spans across disciplines. After one builds the prototype, they have to make sure that it is production-ready and can operate reliably in the cloud 24/7. Moreover, the maintenance and constant improvements after the product launch are equally vital.
Examples of Real-World Applications of An FDE
Cloud and DevOps are essential for many AI products, and there are many real-world applications that one can consider. Here are some ideas:
Chatbot that utilizes the company’s documentation and is available 24/7 to answer clients’ questions.
Document search engine that is updated with new files automatically whenever they appear in a specific directory.
Sales assistant that can operate at scale during peak seasons.
Compliance checker that keeps logs of all actions performed by the model for auditing purposes.
In short, the FDE analyzes the client’s needs, determines the best possible solution, and deploys it in the cloud. This way, the client gets a reliable and scalable product that they can use in their day-to-day operations.
Skills That One Needs To Develop As An FDE
There are certain fundamentals that one needs to learn before going further and specializing in another area. They are as follows:
Python: the programming language that is used almost anywhere in the tech industry.
APIs: crucial for building products and working with databases.
Linux: basics of command line.
Cloud: at least one cloud platform.
DevOps: CI/CD pipelines, monitoring tools, and security audits.
Security: keeping encryption keys, data, and user credentials safe.
Communication: explaining things in simple terms to non-technical people.
Good news is that most engineers find that their communication skills could use improvement, and therefore, this discipline is relatively easy to work on. These competencies, in combination with technical skills, will enable one to become a great FDE.
Challenges That One Might Encounter While Being An FDE
Sometimes, the product launch may seem to have everything but the budget. Unfortunately, a number of FDEs face financial challenges due to cloud costs. However, one should always monitor the spending and set up a budget alert to ensure that they do not go over the yearly limit. Another common issue is that one update in production can break everything, which is why rigorous testing and hotfix procedures are necessary.
Subsequently, if the application is not set up for monitoring, there is not much that one can do while at the office to know that the app is slow or down. Lastly, there is the threat of breaches and unauthorized access, which means that FDEs should never underestimate security measures.
Jobs And Industries That Value An FDE With DevOps and Cloud Technologies Skills
There is a growing demand for professionals with cloud and DevOps skills, especially in AI. One can find such positions on job boards or reach out directly to companies to inquire about open positions. Some roles that one can fill as an FDE with cloud and DevOps skills are: Forward Deployed Engineer, Cloud AI Engineer, AI Solutions Engineer, MLOps Engineer, and others.
In addition, one can build a product and sell it as a service or work for a company that offers such services. From there, it is possible to move into a technical leadership position or a solutions architect role.
How To Get Into The FDE Space and Learn These Skills
The good news is that one does not need years of experience to become an FDE. There are multiple ways to get one started, such as learning the fundamentals of Python, building a simple AI application with an API, learning Git and Docker, deploying the application with cloud services, and then adding a CI/CD pipeline. With that being said, the best way to learn is with a good mentor who will help one through the process, practice as much as possible, and prepare for interviews.
It is also worth noting that one should look for programs with a strong practical focus and include projects, mentorship, and interview prep.
Conclusion
AI forward deployed engineering with cloud and DevOps is a growing field that enables one to become a valuable asset to the industry. In addition, FDEs can work in various industries and even pursue careers as independent contractors.
With demand for these skills on the rise, one should consider learning these skills and taking advantage of the opportunities ahead.