Introduction to Deploying AI Applications to Production

In the previous lessons, we focused on building AI applications professionally, adding safety controls, managing cost, and monitoring how the application behaves.

Now we are ready for the final step:

We added logging, monitoring, and versioning.

And finally, we deployed the application so real users could access it.

The main lesson is that building the AI feature is only one part of building an AI application.

A production system also needs to be safe, efficient, observable, maintainable, and deployable.

Those practices are what turn an AI prototype into a real application.

Introduction to Monitoring, Logging and Versioning AI Applications

In the previous lesson, we focused on making AI applications more efficient by reducing unnecessary token usage, choosing the right model, and avoiding repeated work.

Now we are going to focus on what happens after an AI application is running.

We can now observe what the application is doing, detect problems, and connect changes in behavior to specific versions of our prompts, models, configuration, and code.

In the next lesson, we will bring everything together by looking at Deploying AI Applications using platforms such as Streamlit Cloud, Hugging Face Spaces, and Google Cloud Run.

Introduction to Cost Optimization and Token Management

In the previous lessons, we focused on building AI applications professionally and adding guardrails to make them safer.

Now we are going to look at another challenge that becomes important as an AI application grows:

As applications move into production, the next challenge is understanding what is happening after they are deployed.

In the next lesson, we will explore Logging, Monitoring and Versioning AI Apps, and look at how developers track application behavior, diagnose problems, and manage changes over time.

Introduction to Building Secure AI Applications

In the previous lesson, we added guardrails around an AI application.

We checked incoming requests, protected access to tools and data, moderated unsafe content, and validated the model’s output

How efficiently are we using the AI model?

Every model request consumes tokens, takes time, and has a cost.

In the next lesson, we will look at optimising AI applications for cost and performance.

We will measure token usage, reduce unnecessary prompt content, control response length, choose appropriate models, and learn how to balance cost, latency, and quality in a production AI application.

Introduction to Guardrails and Safety

In the previous lesson, we took a working AI application and improved the way it was structured, tested, documented, and managed.

But professional development is not only about clean code.

We protected the system against prompt injection, added content moderation, restricted access to sensitive resources, and checked model outputs before returning them to the user.

In the next lesson, we will focus on another production concern: Cost Optimization and Token Management, and look at practical ways to reduce unnecessary model usage while keeping the application effective.

Introduction to Agent Use Cases

In this lesson, learners will:

• Understand real-world AI agent use cases
• Learn how AI agents assist with research, coding, and automation
• Understand how modern AI systems combine reasoning, memory, and tools
• See how agent workflows operate in practical scenarios
• Understand where the AI industry is heading

• reasoning
• tools
• workflows
• memory

This is where applications begin behaving more like intelligent assistants.

Introduction to Multi Step Reasoning Systems

In this lesson, learners will:

• understand what multi-step reasoning systems are
• learn why modern AI systems break problems into smaller tasks
• build a simple multi step reasoning workflow using LangChain and Gemini
• understand sequential AI reasoning
• learn how AI systems plan before generating final answers

This is where AI systems begin interacting with the outside world.

Introduction to Building AI Workflows with Lang Chain

In this lesson, learners will:

we will take LangChain further by introducing:

• memory
• multi-step workflows
• tool usage
• AI agents

This is where applications start becoming truly intelligent.

SECTION 1 — Opening Problem

Large language models are powerful.

But by themselves, they cannot:

How AI systems use external tools

What task planning means

How AI agents complete workflows

Why tool usage matters

SECTION 1 — Opening Problem

Most AI applications today answer questions.

But newer AI systems can actually:

What AI agents are

How agents differ from chatbots

How agents use tools and memory

Why companies are investing in agents