Foundations of AI Engineering and LLMOps
The full LLMOps blueprint (with code).
One MCP server to access the web
When Agents use web-related tools, they run into issues like IP blocks, bot traffic, captcha solvers, etc.
Agents get rate-blocked or rate-limited.
Agents have to deal with JS-heavy or geo-restricted sites.
This hinders the Agent’s execution.
Bright Data MCP server gives you 30+ powerful tools that allow AI agents to access, search, crawl, and interact with the web without getting blocked.
The video below depicts the browser tool usage from Bright Data, where the Agent is autonomously navigating a web page.
Unlike typical scraping tools, this MCP server dynamically picks the most effective tool based on the structure of the target site.
These are some of the tools:
Browser tool
Web Unlocker API
Scraper API
Platform-specific scrapers for Instagram, LinkedIn, YouTube, etc.
SERP API, and more.
You can try the MCP server using this GitHub repo →
The steps are detailed in the GitHub repo.
Foundations of AI Engineering and LLMOps
Last week, we concluded the MLOps crash course with 18 parts.
Now we are moving to the full LLMOps crash course, and Part 1 is now available, which covers:
Fundamentals of AI engineering & LLMs
The shift from traditional ML models to foundation model engineering
Levers of AI engineering
MLOps vs. LLMOps key differences
While learning MLOps, we primarily explored traditional ML models and systems and learned how to take them from experimentation to production using the principles of MLOps.
But what happens when the “model” is no longer a custom-trained classifier, but a massive foundation model like Llama, GPT, or Claude?
Are the same principles enough?
Not quite.
Modern AI applications are increasingly powered by LLMs, which introduce an entirely new set of engineering challenges that traditional MLOps does not fully address.
This is where LLMOps come in.
It involves specialized practices for managing and maintaining LLMs and LLM-based applications in production, ensuring they remain reliable, accurate, secure, and cost-effective.
The aim is to provide you with a thorough explanation and systems-level thinking to build LLM apps for production settings.
Just like the MLOps crash course, each chapter will clearly explain necessary concepts, provide examples, diagrams, and implementations.
As we progress, we will see how we can develop the critical thinking required for taking our applications to the next stage and what exactly the framework should be for that.
Read Part 1 on fundamentals of LLMOps here →
👉 Over to you: What would you like to learn in the LLMOps crash course?
Thanks for reading!
This LLMOps series is incredibly timley given how fast everyone's pivoting to foundation models. The point about specialized practices for production LLMs really lands - I've seen teams try to just extend their MLOps pipelines and hit walls around prompt versioning and context management. The shift from custom classifiers to models like Llama changes basically evreything about monitoring and deployment. Looking forward to seeing how you tackle the cost vs reliability tradeoffs in later parts.