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:
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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:
Forward Deployed Engineering (FDE)-Live Training – Demo Recording
Forward Deployed Engineering (FDE)-Live Training – Day 1 Recording
Live Sessions Price:
For LIVE sessions – Offer price after discount is 300 USD 259 119 USD Or USD13000 INR19900 INR 9900 Rupees
OR
Free Day 3 On:
Indian Timings: 4th May @ 7:30 AM – 8:30 AM (IST)/
U.S Timings: 3rd May @ 10 PM – 11 PM (EST)/
U.K Timings: 4th May @ 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:
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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, data structures)
- 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:
- Students or graduates with basic Python knowledge
- Working professionals looking to upskill in AI and backend development
- QA/Testers or automation engineers planning to move into development/AI roles
- Developers interested in building AI-powered applications
- Anyone comfortable with programming logic and eager to build real-world projects
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:
- What FDEs do in real-world scenarios
- Breaking down business problems into technical components
- Understanding constraints and trade-offs
- System thinking: input → processing → output
- Writing clean, modular Python functions
- Structuring small projects (files, separation of logic)
- Working with JSON and API responses
- Basic error handling
- 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
- Build a working backend service with multiple endpoints
- How data moves through a system
- Using simple storage (in-memory, JSON, file-based)
- Connecting backend logic with data
- Extend backend to store and retrieve data
- 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
- 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
- Why LLMs lack access to external data
- RAG flow: split → retrieve → generate
- Importance of context
- Common failure cases and limitations
- Chat with documents (basic document Q&A system)
- Building a simple interface (input, button, output)
- Connecting UI with backend APIs
- Create a UI for the existing backend + AI system
- Structuring project for sharing
- Basic Git usage
- Docker basics: build and run application locally
- Package and run the application locally
- Backend API
- ML/AI functionality
- Streamlit UI
- Chatbot for a specific use case
- Resume or document analysis tool
- Document Q&A system
- Define the problem
- Design the approach
- Build the solution
- Present their work