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AI for Go developers: the infrastructure models run on is your territory

Go is a language of infrastructure, and AI needs infrastructure: model gateways, inference servers, agents that run as services, operations assisted by AI. OpenAI and Anthropic publish official Go SDKs and the Model Context Protocol has an official Go SDK. None of the courses here is written in Go: their examples are mostly Python. They are on this page because they describe the layer above the one you run, and how AI enters operations.

Why you are relevant and where the risk is

Your advantage

A Go developer brings what AI projects often lack: small, fast, observable services that survive real traffic. Gateways that route between models, cost limits per client, response caching, low-latency MCP tools, agents that run as services: all of these are systems problems, not data science problems.

Where the risk is

The risk is staying at the infrastructure level without understanding what runs on it. Someone who does not know how a model behaves, what RAG means or how answer quality is evaluated cannot design the layer underneath correctly. The courses below give you that mental model and tie it to operations, where you are already at home.

What changes in your work with Go

  1. Model gateways become standard components

    Routing between providers, retries, streaming, cost limits, caching and compliant logging: a typical Go service, with the twist that the answer is non-deterministic and cost is per token.

  2. Platform tools are exposed to agents through MCP

    Internal services become tools for agents and assistants, with authentication and least-privilege permissions. The protocol is language-agnostic and has an official Go SDK.

  3. Operations are done with AI

    Alert triage, root cause analysis, log and trace analysis, ChatOps and Kubernetes operations. Platform teams need to know both what these tools can do and where they must stop.

  4. Self-hosted inference is a systems problem

    Quantization, hardware sizing and OpenAI-compatible endpoints. Whoever operates these services decides the cost and the confidentiality of the whole platform.

Each of these changes has at least one course in the track below. See the track

A Certificate of Completion for every course in the Go track

When you have gone through all the lessons of a course and have an average of at least 70% on its quizzes, you issue from your account, after confirming the conditions, a Certificate of Completion with a unique number and a public verification link. It is not issued automatically and it comes at no extra cost, with a plan or with single-course access.

  • Verifiable by anyone. Every certificate has a QR code and a public verification page, with its unique code. A recruiter checks it without an account.
  • With figures, not just a name. The percentage of lessons completed, the average quiz score and the study time are written on the document.
  • Three tiers, by quiz average: Pass (70–79%), Merit (80–89%) and Distinction (90% and above), shown on the document.
  • Your name, your choice. You decide whether your name appears on the public verification page, and you can change that choice at any time.
A sample certificate. The real document is a PDF, with a QR code and a public verification link.

AI courses for Go: 2 stages, 9 courses

In the order in which they matter. If you already have experience with model APIs, skip the foundations. For each course, the reason it matters to someone who works with Go.

  1. Stage 1: Foundations: what runs on your infrastructure

    The mental model of LLMs and of AI applications, so that you design the layer underneath correctly. Language-independent, with the examples mostly in Python.

    1. Introduction to AI Engineering 28 lessons · ~25 h Beginner

      LLMs, APIs, RAG, agents, evaluation and security at concept level: the frame you need to understand what you are serving.

      Key modules: Machine Learning Essentials · Large Language Models and Modern Architectures · Working with LLM APIs and Integrations · AI Tools for Developers

      Read the free preview Course details This course only: €99/month
    2. Advanced LLM Integration in Production Applications 24 lessons · ~26 h Advanced

      Gateway, queue-based and sidecar patterns, streaming over SSE, circuit breakers, multi-provider fallback, rate limiting, caching and OpenTelemetry tracing: in effect the specification of a model gateway, which you then implement in Go. The examples are in Python.

      Key modules: Streaming and Error Handling · Function Calling and Tool Use · Model Context Protocol (MCP) · Multi-Model Orchestration and Optimization

      Read the free preview Course details This course only: €99/month
    3. MCP (Model Context Protocol) — Building Servers and Integrations (Enterprise Edition) 22 lessons · ~25 h Advanced

      Protocol fundamentals, stdio and HTTP transports, OAuth 2.1, testing, containerization with Docker and Kubernetes, and CI/CD. The example servers are in Python and TypeScript; the course itself points out that the protocol is language-agnostic and that an internal server can just as well be written in Go.

      Key modules: Building MCP Servers with Python · Building MCP Servers with TypeScript · Transport, Authentication and Configuration · Enterprise Patterns and Production Architectures

      Read the free preview Course details This course only: €99/month
  2. Stage 2: Operations: AIOps, self-hosting, evaluation and security

    The stage where AI enters the operation of the platform and where you answer for quality, cost and security.

    1. AI for DevOps and SRE: AIOps in Practice 30 lessons · ~25 h Advanced

      Observability with OpenTelemetry, Prometheus and Grafana, anomaly detection, AI-assisted incident response and root cause analysis, AI in CI/CD and infrastructure as code, ChatOps and Kubernetes operations: the course written for platform teams.

      Key modules: Intelligent Observability: OpenTelemetry, Prometheus, Grafana · Anomaly Detection and Intelligent Alerting · AI-Assisted Log and Trace Analysis · AI-Assisted Incident Response and On-Call

      Read the free preview Course details This course only: €99/month
    2. Fine-Tuning and Customizing Open-Source LLMs: LoRA, QLoRA and Self-Hosting 30 lessons · ~25 h Advanced

      The self-hosting half is yours: quantization for inference (GGUF, GPTQ, AWQ), serving with Ollama, vLLM and TGI behind OpenAI-compatible endpoints, and cost and hardware planning.

      Key modules: Data: The Foundation of Fine-Tuning · Parameter-Efficient Fine-Tuning (PEFT): LoRA · QLoRA and Quantized Training · The Fine-Tuning Toolchain

      Read the free preview Course details This course only: €99/month
    3. MLOps: The Machine Learning Lifecycle in Production 30 lessons · ~25 h Advanced

      Model serving patterns, deployment strategies (canary, shadow, A/B testing), drift monitoring and GPU cost: the lifecycle your platform has to support.

      Key modules: Experiment Tracking and Reproducibility · Data and Feature Management · Training Pipelines and Orchestration · Model Registry, Versioning, and CI/CD for ML

      Read the free preview Course details This course only: €99/month
    4. LLM Evaluation and Testing: Shipping Reliable AI 30 lessons · ~25 h Advanced

      Regression tests in CI/CD, online A/B testing and quality-drift monitoring: answer quality treated like any other operational signal.

      Key modules: Classic Metrics and Why They Fail on Open-Ended Text · LLM-as-a-Judge: Design, Bias and Calibration · Building Evaluation Datasets · The Evaluation Tooling Landscape

      Read the free preview Course details This course only: €99/month
    5. AI Security: Defending LLM Applications (OWASP LLM Top 10, Guardrails, Red-Teaming) 30 lessons · ~25 h Advanced

      Prompt injection, excessive agency, supply chain, secrets and denial of service against models: the security requirements for gateways and for tools exposed to agents.

      Key modules: Prompt Injection: The Defining Threat · Insecure Output Handling and Downstream Exploits · Guardrails: Input and Output Filtering · Securing RAG and Agents: Excessive Agency

      Read the free preview Course details This course only: €99/month
    6. Claude Code Mastery: Agentic Coding from the Terminal (multi-file, git, CI, MCP) 28 lessons · ~25 h Advanced

      Agentic coding from the terminal, headless mode, hooks and CI integration. The course notes that code-intelligence plugins for typed languages, Go among them, let the agent catch type errors after each edit.

      Key modules: Multi-File and Large Codebases: Plan, Edit, Review and Context Management · The Full Git Workflow: Branches, Commits, Conflicts, Code Review and PRs · Headless Mode (claude -p) and Non-Interactive Automation · Subagents and Agent Teams: Orchestration, Delegation and Controlled Parallelization

      Read the free preview Course details This course only: €99/month
All 25 IT courses, including the 9 in this track, in one plan. IT Pro: €399 per month, VAT included. Or only the course you are interested in, €99 per month, from the “This course only” link on each row.

The capstone project in the Go track

The course in the track that ends with a capstone project. The list comes from the real modules of the courses, not from a brochure.

The AI ecosystem around Go

What already exists, so that you do not start from zero. The courses in the track teach you what to build with it.

  • Official OpenAI and Anthropic SDKs for Go Official clients for Go services that talk directly to the models.
  • The Go SDK for MCP An official SDK of the protocol, for MCP servers and clients in Go.
  • Kubernetes, Prometheus and OpenTelemetry The operations stack the AIOps course works with, from telemetry to Kubernetes operations.
  • Infrastructure as code Terraform and Pulumi; the AIOps course notes that Pulumi programs can be written in Go.
  • Vector databases Two of the engines compared in the RAG course, Weaviate and Milvus, are listed there as written in Go, the second together with C++.
  • OpenAI-compatible endpoints Ollama, vLLM and TGI expose compatible APIs, so a Go gateway can route between cloud and self-hosted models.

What you will be able to do at the end of the Go track

  1. Specify a model gateway: routing, streaming, retries, cost limits and compliant logging
  2. Expose platform services as MCP tools, with authentication and least-privilege permissions
  3. Bring AI into operations: triage, root cause analysis and ChatOps, with clear limits and a human in the loop
  4. Plan self-hosted inference and treat answer quality as an operational signal

These are the skills you practise in the courses of the track; results depend on the time you invest and on your previous experience.

Start the Go track

What you get here that you do not get from a tutorial

Courses of about 25 hours, structured in modules

Code examples, architectures and case studies, and in some courses a capstone project at the end. Not summaries of documentation, but structured material that you go through in order, at your own pace.

AI Professor in every lesson

You ask in the context of the lesson, by typing or by voice, and get an explanation of the concept you are on. Every lesson has its quiz: about 354 questions per course on average. The professor is an AI system, not a human teacher, included in every plan with a fair-use limit.

Up to date with what the industry asks for

Agentic coding, MCP, RAG, agents, evals, LLM security, fine-tuning, AIOps, computer vision and Edge AI are whole courses in the catalog, not closing chapters.

See the material before you pay

A preview of the first lesson of every course can be read without an account. Then you choose a single course, €99 per month, or IT Pro, €399 per month, with the whole IT catalog. You can stop the renewal anytime from your account; access stays until the end of the period already paid for.

IT Pro opens all 25 IT courses, including the 9 in this track

The courses on this page are IT courses. With the IT Pro plan you have the whole IT catalog, the technical learning paths and the AI Professor; if you want only one course, you buy it separately.

One course

When you care about a single topic in the catalog

€ 99 per month, VAT included No commitment: stop the renewal anytime

about €4 per hour of course

1 course, your choice of 50 25 hours on average
  • All the course’s lessons, exercises and quizzes, about 354 questions per course on average
  • AI Professor in the lessons, included in your subscription
  • Publicly verifiable Certificate of Completion

IT Pro

Recommended

Developers, data engineers, architects. Code and production.

€ 399 per month, VAT included Billed monthly: stop the renewal anytime

€16 per month for each of the 25 courses

25 IT Pro courses 25 hours per course on average
  • Prompt engineering, LLMs, RAG, AI agents and MCP, Claude Code and Cursor, computer vision, MLOps, AI security
  • Hands-on projects with production examples
  • AI Professor in every lesson, quizzes and a certificate for every course

All Access

The whole catalog, both tracks side by side.

€ 499 per month, VAT included Billed monthly: stop the renewal anytime

€10 per month for each of the 50 courses

All 50 courses about 1,257 hours
  • You save €199 a month compared with the two bundles bought separately
  • Learning paths for both profiles
  • AI Professor in every lesson, quizzes and a certificate for every course

Subscriptions renew automatically; you can stop the renewal anytime from your account, and access stays until the end of the period already paid for. As digital content with immediate access, see the withdrawal conditions.

Compare all the plans · Plans for teams, by number of seats · Give a month of access as a gift

Questions about AI for Go

Do the courses have examples in Go?

No. The examples are mostly in Python, and the MCP course also has a module in TypeScript. The courses are chosen for architecture, protocols and operating practices, which you then implement with the official Go SDKs for OpenAI, Anthropic and MCP. That is also why this track has two stages instead of three: it lists only the courses whose substance does not depend on the language.

I am an SRE or a platform engineer, not an application developer. Does it make sense?

Yes. The AIOps course is written for operations teams, and the fine-tuning and MLOps courses cover the platform decisions: serving, cost, monitoring. The foundations give you the context, so that you do not operate blind.

I want to run models on our own servers. Which course helps?

The course on fine-tuning open-source LLMs: it covers open-model licenses, quantization for inference, serving with Ollama, vLLM and TGI, and cost and hardware planning. The MLOps course adds serving patterns, deployment strategies and monitoring.

Which plan opens these courses?

All the courses recommended here are IT courses and open with the IT Pro plan, which includes every IT course in the catalog. It is billed monthly, and you can stop the renewal anytime from your account. There is also individual, monthly access to a single course. Every course has a free preview of its first lesson, readable without an account.

A free preview of the first lesson, without an account

Start the Go track today

9 courses, ~225 hours, opened with IT Pro: €399 per month, VAT included, with access to all 25 IT courses. The AI Professor is included in every plan.

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ChatGPT, OpenAI, Claude, Anthropic, Gemini, Google, Microsoft 365 Copilot, Cursor, Midjourney and the other product and company names mentioned are trademarks or trade names of their respective owners. Cursuri-AI.ro is an independent training provider and is not affiliated with, sponsored by or endorsed by any of them.

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