Best high paying opportunity for 2026 | Don't Miss

Summary

This video urges IT professionals to boost pay and future-proof careers by learning rare, high-growth skills—specifically MLOps and LLM ops. Using DevOps’ rise as a blueprint, the presenter explains how demand for model deployment, monitoring, and production workflows will create a talent shortage and premium salaries. DevOps practitioners can transition easily since core concepts (CI/CD, automation) remain similar, though tools differ. Viewers are advised to learn MLOps first, then LLM ops, and are pointed to free YouTube resources, an upcoming comprehensive Udemy course, and a roadmap link. Questions are welcomed in the comments.

Takeaways

• 😀 Learn rare skill sets that have future growth potential to get highly paid in IT.
• 😀 In the 2010s, DevOps became a high-demand field due to a shortage of qualified engineers, leading to high pay rates.
• 😀 MLOps and LLM ops are the next high-demand skills, similar to how DevOps gained prominence in the past decade.
• 😀 Machine Learning (ML) has gained massive traction in recent years, and more companies are adopting AI/ML solutions.
• 😀 As companies scale their ML models, there will be an increasing need for dedicated MLOps teams.
• 😀 LLM ops will also emerge as a specialized field as companies begin to require teams dedicated to managing large language models (LLMs).
• 😀 Professionals with experience in DevOps will find it easier to transition into MLOps due to overlapping concepts and skills.
• 😀 MLOps involves implementing CI/CD for ML models, which shares similarities with DevOps but requires a different toolchain and implementation.
• 😀 DevOps focuses on traditional software development, while MLOps is focused on managing ML models, and LLM ops is focused on large language models.
• 😀 Start learning MLOps as it is the foundational skill for later learning LLM ops, which is a subfield of MLOps.
• 😀 There are plenty of resources online, including free YouTube playlists and a future Udemy course, to help you learn MLOps from basics to real-world implementation.

Q & A

What is the key to getting highly paid in IT, according to the video?

The key to getting highly paid in IT is learning a rare skill set that has the potential to grow in the future, such as MLOps or LLM ops.

What skill set was highly in demand during the 2010s, and why?

In the 2010s, DevOps was in high demand because, although many people were focused on development or QA, few specialized in DevOps. The demand for DevOps engineers grew as companies started adopting DevOps practices during the late 2010s and the COVID era.

What is MLOps, and why is it predicted to become highly relevant?

MLOps is the practice of managing machine learning models in production, including deployment, monitoring, and scaling. It is expected to become highly relevant as more companies adopt AI and ML, leading to the need for dedicated teams for managing ML models, similar to how DevOps was needed for software development.

What is the role of LLM ops, and how is it connected to MLOps?

LLM ops refers to the management and operation of large language models (LLMs). It is closely related to MLOps, as both deal with the deployment and management of AI models. However, LLM ops specifically focuses on language models, whereas MLOps encompasses a broader range of machine learning models.

How can someone currently working as a DevOps engineer transition into MLOps?

A DevOps engineer can transition into MLOps relatively easily, as both roles share similar concepts, such as CI/CD and continuous training for models. The main difference is in the tools and implementation specific to machine learning models, rather than traditional software.

What is the connection between DevOps and MLOps in terms of skills and concepts?

DevOps and MLOps share many common concepts, such as automation, CI/CD, and monitoring. The key difference lies in the context: DevOps focuses on traditional software, while MLOps is centered around machine learning and model deployment.

How can someone get started with learning MLOps?

You can start learning MLOps through online resources, including YouTube channels offering free tutorials. The video also mentions a dedicated MLOps playlist on the speaker's YouTube channel and a future Udemy course that will cover MLOps from basics to real-world project implementation.

What is the suggested learning path for someone interested in LLM ops?

It is recommended to first learn MLOps before moving to LLM ops, as LLM ops is a submodule of MLOps. Building a strong foundation in MLOps will provide the necessary skills for working with large language models.

Why is it important to learn MLOps by 2026 or early 2026?

Learning MLOps by 2026 is important because the demand for MLOps professionals is expected to rise as companies scale their AI and ML operations. Professionals with expertise in MLOps will be in high demand, and early preparation can give individuals a competitive advantage in the job market.

What resources are available for learning MLOps?

Resources for learning MLOps include online platforms like YouTube (where the speaker has an MLOps playlist), and in the future, a comprehensive Udemy course. Additionally, a detailed roadmap link will be provided to guide learners on the concepts they need to understand to become an MLOps engineer.