Forward Deployed Engineering (FDE): Building Practical AI Applications – Live Training

Teacher

Forward Deployed Engineering (FDE): Building Practical AI Applications – Live Training

(Master Python, APIs, FastAPI, data flow, ML & LLMs, RAG systems, Streamlit UI, Git & Docker. Build end-to-end real-world applications with hands-on projects and production-ready skills.)

This program is designed to train you as a Forward Deployed Engineer (FDE), focusing on solving real-world business problems through practical, end-to-end application development. You will learn how to understand ambiguous requirements, break them down into structured technical solutions, and build systems that integrate backend, data, and AI components. The course emphasizes system thinking, where you design solutions by considering inputs, processing, outputs, constraints, and trade-offs.

Throughout the program, you will work on real-world use cases involving APIs, data flow, machine learning, and modern AI techniques such as LLMs and retrieval-based systems (RAG). You will also build simple user interfaces and learn how to connect them with backend services to create complete, functional applications. The focus is not just on coding, but on applying the right approach to solve business problems effectively.

By the end of the course, you will be able to design, build, and present production-ready solutions, demonstrating the mindset and skills of a Forward Deployed Engineer who can bridge the gap between business needs and technical implementation.

About the Instructor:

Jacob is an experienced AI and Data Science professional with over 8+ years of industry and training expertise, specializing in building end-to-end, real-world AI applications. His work spans across machine learning, computer vision, time series forecasting, and Generative AI, where he has designed and delivered multiple high-impact solutions for complex business problems. From developing recommendation systems and demand forecasting models to building advanced computer vision applications and AI-powered automation systems, Jacob brings a strong practical and solution-oriented approach to every project.

He has extensive experience working across the full lifecycle of AI solutions—from problem discovery and solution design to model development, deployment, and integration into production environments. His expertise includes working with Python, ML frameworks, LLMs, and modern tools for model tracking and deployment, along with a deep understanding of business-driven decision making. In addition to his technical contributions, he has actively mentored interns and junior professionals, established best practices, and contributed to reusable frameworks and accelerators.

As a trainer, Jacob has successfully trained 200+ learners, focusing on hands-on, application-driven learning. His teaching approach emphasizes breaking down complex concepts into simple, understandable components while aligning them with real-world use cases. He ensures that learners not only understand the theory but also gain the confidence to build, deploy, and scale production-ready systems, making them industry-ready professionals.

Sample Videos:

FDE-Live Training – Demo Recording

FDE-Live Training – Day1 Recording

Live Sessions Price:

For LIVE sessions – Offer price after discount is 3~~00 USD 259 ~~USD~~~~ 119 USD Or ~~13000 INR19900 INR 9~~900 Rupees

Enroll For Free Demo

Free Demo On:

Indian Timings: 28th April @ 7:30 AM – 8:30 AM (IST)/

U.S Timings: 27th April @ 10 PM – 11 PM (EST)/

U.K Timings: 28th April @ 3 AM – 4 AM (BST)

Class Schedule:

For Participants in India: Monday to Thursday @ 7:30 AM – 8:30 AM (IST)/

For Participants in the US: Sunday to Wednesday @ 10 PM – 11 PM (EST)/

For Participants in the UK: Monday to Thursday @ 3 AM – 4 AM (BST)

What Our Students Say About the Trainer:

The training was highly practical and industry-oriented. From backend development to AI and RAG systems, everything was taught with clear examples and use cases. The capstone project was especially useful in applying all concepts together.- Sudheer Verma

Jacob explains complex AI and backend topics in a very simple way. The hands-on approach helped me build confidence in working on real-time projects. – Thomas

Very practical sessions. Learned APIs, Python, and AI with hands-on examples. – Sangeetha

Excellent training with a strong focus on real-world applications. The projects and guidance helped me improve my problem-solving and development skills. –Mohmmed

Jacob’s training stands out because of its strong practical focus and real-world relevance. Instead of just explaining concepts, he ensures that every topic is implemented through hands-on exercises. I particularly liked how he connected backend development with AI use cases like RAG and LLMs. This helped me clearly understand how modern applications are built and deployed in real environments. – Amith

The course is well-structured and practical. I learned FastAPI, ML, and RAG concepts with real use cases, which made it easy to apply in my work. – Chandu

Prerequisites:

Basic knowledge of Python (functions, loops, and data structures) is required. Self-paced recorded videos will be provided for preparation.
Comfortable with programming logic and problem solving
Familiarity with JSON and APIs is an added advantage
A laptop with a development environment set up

Who can enroll for this course:

Software Engineers (Backend / Frontend / Full Stack)
DevOps Engineers
Cloud Engineers (AWS / Azure / GCP)
System Administrators
Technical Support / Application Support Engineers
QA / Test Engineers (Manual & Automation)
Technical Consultants
B.Tech / B.E (CSE, IT, ECE, etc.) students
B.Sc / BCA (Computer Science / IT) students
MCA / M.Tech students
Final-year students
Non-IT professionals with basic programming knowledge and IT understanding

Salient Features:

35+ Hours of Live Training along with recorded videos
Lifetime access to the recorded videos
Course Completion Certificate

What will I learn by the end of this course?

Think and operate like a Forward Deployed Engineer (FDE)
Understand real business problems and convert them into technical solutions
Break down ambiguous requirements and design structured approaches
Build end-to-end solutions combining backend, data, and AI
Work with APIs, data flow, and system-level thinking
Apply AI/ML (LLMs, RAG) to solve practical, real-world use cases
Handle constraints, trade-offs, and failure scenarios in applications
Package and demonstrate solutions in a production-ready manner

Course Syllabus:

Module 1: FDE Mindset and Problem Solving (3 hours)

What FDEs do in real-world scenarios
Breaking down business problems into technical components
Understanding constraints and trade-offs
System thinking: input → processing → output

Module 2: Writing Production-Ready Python (3 hours)

Writing clean, modular Python functions
Structuring small projects (files, separation of logic)
Working with JSON and API responses
Basic error handling

Module 3: Backend Development with APIs (4 hours)

Understanding APIs (GET, POST, request-response flow)
Building APIs using FastAPI
Designing endpoints with JSON input/output
Adding simple business logic (non-AI)
Basic error handling and testing using FastAPI docs

Use Case:

Build a working backend service with multiple endpoints

Module 4: Data Flow in Applications (2 hours)

How data moves through a system
Using simple storage (in-memory, JSON, file-based)
Connecting backend logic with data

Use Case:

Extend backend to store and retrieve data

Module 5: AI and ML in Applications (8 hours)

Using LLM APIs for real-world tasks
Prompt structuring and iteration
Understanding variability in outputs
Failure cases and limitations
Practical evaluation approaches across systems
When to use ML vs LLM

Use Cases:

Product Recommendation System (Classical ML)
- Similarity-based approach
- Structured data problem solving
Multimodal AI Application
- Input: product image
- Output: classification from predefined categories
- Prompt-based classification using LLM
- Evaluation using basic classification metrics

Module 6: Retrieval-Based Systems (RAG) – Practical Implementation (4 hours)

Why LLMs lack access to external data
RAG flow: split → retrieve → generate
Importance of context
Common failure cases and limitations

Use Case:

Chat with documents (basic document Q&A system)

Module 7: UI Integration with Streamlit (2 hours)

Building a simple interface (input, button, output)
Connecting UI with backend APIs

Use Case:

Create a UI for the existing backend + AI system

Module 8: Packaging and Sharing (Git + Docker Basics) (2 hours)

Structuring project for sharing
Basic Git usage
Docker basics: build and run application locally

Use Case:

Package and run the application locally

Capstone Project (2 hours guided + additional work outside sessions)

Students will work in groups of 2–3 to build a simple end-to-end application.

The project will include:

Backend API
ML/AI functionality
Streamlit UI

Example problem statements:

Chatbot for a specific use case
Resume or document analysis tool
Document Q&A system

Students will:

Define the problem
Design the approach
Build the solution
Present their work

Module 9: Watch Python videos for FREE here: (Self-paced recorded videos)

Click here to access and watch the Python videos

How can I enroll for this course?

Enroll For Free Demo

For any other details, Call me or Whatsapp me on +91-9133190573

Live Sessions Price:

For LIVE sessions – Offer price after discount is 3~~00 USD 259 ~~USD~~~~ 109 USD Or ~~13000 INR~~ ~~19900 INR~~ 9900 Rupees

Sample Course Completion Certificate:

Your course completion certificate looks like this……

Note:

To maintain the quality of our training and ensure a smooth learning experience for all participants, we do not allow batch repetition or switching between courses.

To reiterate, moving from one course to another or shifting from one trainer to another (even if it is the same course) is not possible. Changing batches or trainers in any form is strictly not permitted.

We request all learners to attend the scheduled sessions regularly and make the most of their learning journey. Thank you for your understanding and continued support.