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Master AI Engineering: From Problem Definition to Deployment

Name: Master AI Engineering: From Problem Definition to Deployment
Rating: 4.3 (354 reviews)

Build AI solutions using data preparation, machine learning, deployment, monitoring, AI agents and n8n

Created byArun Singhal B-Tech, MBA (IIM-B),Unilever, J&J, Danone, IIMU, Cello

Last updated 6/2026

English

What you'll learn

Understand the end-to-end AI engineering lifecycle from problem definition to deployment and scaling
Learn how to define the right AI problem aligned with business goals and real-world use cases
Master data collection, cleaning, and preprocessing for building high-quality AI models
Build strong foundations in feature engineering to improve model performance and accuracy
Learn how to select, design, and optimize AI/ML algorithms for different problem types
Develop skills to train, evaluate, and fine-tune machine learning models effectively
Understand how to deploy AI models into real-world applications and production systems
Learn model monitoring, maintenance, and continuous improvement for long-term success
Apply structured frameworks to improve model reliability, scalability, and performance
Understand how to collaborate across teams including data scientists, engineers, and business stakeholders
Research and Innovation
Explore AI research, innovation, and emerging trends shaping the future of AI engineering
Learn how to build and use AI agents and modern AI systems
Understand ethical considerations, bias, and responsible AI practices
Gain practical insights into real-world AI engineering workflows and case-based learning
Build a clear roadmap to become a successful AI Engineer

Course content

15 sections • 67 lectures • 8h 45m total length

Introduction6:33
Introduction to the instructor and course
Introduction Case Study11:10
•A challenging, realistic, and deeply insightful case study designed for the learners who want to become successful Artificial Intelligence (AI) Engineers

Overview I6:23
At the end of this lecture, you will learn the following
What is Artificial Intelligence?
Overview I
What is Artificial Intelligence and career opportunities in this field6:21
At the end of this lecture, you will learn the following
•What is Artificial Intelligence and career opportunities in this field
Overview II
Overview III- What are the responsibilities of a AI Engineer?8:49
At the end of this lecture, you will learn the following
•What are the responsibilities of a AI Engineer?
Overview III

Problem Definition I - How to understand stakeholders' needs and define problems7:12
At the end of this lecture, you will learn the following
•How to understand stakeholders' needs and define problems that can be addressed using artificial intelligence and machine learning techniques
Problem Definition I
Problem Definition II- How to understand stakeholders' needs and define problems6:49
At the end of this lecture, you will learn the following
•How to understand stakeholders' needs and define problems that can be addressed using artificial intelligence and machine learning techniques
Problem Definition II
An example of understanding stakeholders' needs and define problems6:07
At the end of this lecture, you will learn the following
•An example of understanding stakeholders' needs and define problems that can be addressed using artificial intelligence and machine learning technique
Problem Definition III
An example of understanding stakeholders' needs and define problems5:56
At the end of this lecture, you will learn the following
•An example of understanding stakeholders' needs and define problems that can be addressed using artificial intelligence and machine learning technique
Problem Definition IV

Data Collection & Preprocessing I7:43
At the end of this lecture, you will learn the following
•How to gather relevant data from various sources, ensure its quality, and preprocess it to make it suitable for analysis and modeling
Data Collection & Preprocessing I
Standardizing data formats13:09
At the end of this lecture, you will learn the following
How to gather relevant data from various sources, ensure its quality, and preprocess it to make it suitable for analysis and modeling
Data Collection & Preprocessing II
Feature extraction11:46
At the end of this lecture, you will learn the following
•How to gather relevant data from various sources, ensure its quality, and preprocess it to make it suitable for analysis and modeling
Data Collection & Preprocessing III
Image feature extraction algorithms8:02
At the end of this lecture, you will learn the following
•How to gather relevant data from various sources, ensure its quality, and preprocess it to make it suitable for analysis and modeling
Data Collection & Preprocessing IV
An example of gathering relevant data from various sources, ensure its quality9:32
At the end of this lecture, you will learn the following
•An example of gathering relevant data from various sources, ensure its quality, and preprocess it to make it suitable for analysis and modeling
Data Collection & Preprocessing V
An example of gathering relevant data from various sources, ensure its quality10:06
At the end of this lecture, you will learn the following
•An example of gathering relevant data from various sources, ensure its quality, and preprocess it to make it suitable for analysis and modeling
Data Collection & Preprocessing VI

Algorithm Selection & Development I- Problem Understanding and Formulation8:35
At the end of this lecture, you will learn the following
How to research, select, and develop appropriate machine learning algorithms or deep learning architectures based on the problem at hand and the available data?
Algorithm Selection & Development I
How to determine type of output and evaluation metrices?10:07
At the end of this lecture, you will learn the following
•How to research, select, and develop appropriate machine learning algorithms or deep learning architectures based on the problem at hand and the available data?
Algorithm Selection & Development II
How to determine type of output and evaluation metrices- Contd6:44
At the end of this lecture, you will learn the following
•How to determine type of output and evaluation metrices- Regression and Clustering
Algorithm Selection & Development III
Algorithm Selection & Development IV6:58
At the end of this lecture, you will learn the following
•How does Silhouette Score measures how similar an object is to its own cluster compared to other clusters
Algorithm Selection & Development IV
Davies-Bouldin Index6:32
At the end of this lecture, you will learn the following
•How does Davies-Bouldin Index compute the average similarity between each cluster and its most similar cluster
Algorithm Selection & Development V
Adjusted Rand Index (ARI) and Adjusted Mutual Information (AMI)8:44
At the end of this lecture, you will learn the following
•How to does Adjusted Rand Index (ARI) and Adjusted Mutual Information (AMI) measure the agreement between true labels and cluster assignments
Algorithm Selection & Development VI
Data Understanding and Preparation •Researching Algorithms and Architectures6:55
At the end of this lecture, you will learn the following
•Data Understanding and Preparation
•Researching Algorithms and Architectures
•Model Selection and Evaluation
Algorithm Selection & Development VII
Learning rate in gradient descent hyperparameter7:05
At the end of this lecture, you will learn the following
•Learning rate in gradient descent hyperparameter
Algorithm Selection & Development VIII
Number of hidden layers in a neural network hyperparameter5:07
At the end of this lecture, you will learn the following
•Number of hidden layers in a neural network hyperparameter
Algorithm Selection & Development IX
Hyperparameter tuning18:24
At the end of this lecture, you will learn the following
•Hyperparameter tuning
Algorithm Selection & Development X
How to compare the performance of different models and architectures6:03
At the end of this lecture, you will learn the following
•How to compare the performance of different models and architectures to identify the most effective ones
Algorithm Selection & Development XI
How to Iterate on the model development process by fine-tuning hyperparameters7:45
At the end of this lecture, you will learn the following
•How to Iterate on the model development process by fine-tuning hyperparameters
Algorithm Selection & Development XII
Regularization, dropout, batch normalization, and learning rate scheduling14:13
At the end of this lecture, you will learn the following
•How to use techniques like regularization, dropout, batch normalization, and learning rate scheduling to improve model generalization and performance
Algorithm Selection & Development XIII
How to monitor and analyze model training/validation metrics7:32
At the end of this lecture, you will learn the following
•How to monitor and analyze model training/validation metrics
Algorithm Selection & Development XIV
How to consider the interpretability and explainability of the selected models8:06
At the end of this lecture, you will learn the following
•How to consider the interpretability and explainability of the selected models
Algorithm Selection & Development XV
How to train Decision trees algorithm for getting feature importance4:35
At the end of this lecture, you will learn the following
•How to train Decision trees algorithm for getting feature importance
Algorithm Selection & Development XVI
How to train Random Forests algorithm for getting feature importance4:00
•How to train Random Forests algorithm for getting feature importance
Algorithm Selection & Development XVII
How to train Gradient boosting machines algorithm for getting feature importance4:27
At the end of this lecture, you will learn the following
How to train Gradient boosting machines algorithm for getting feature importance
Algorithm Selection & Development XVIII
How to use feature importance analysis to provide insights into model prediction8:46
At the end of this lecture, you will learn the following
•How to use feature importance analysis to provide insights into model predictions
Algorithm Selection & Development XIX
Model Interpretability Methods8:34
At the end of this lecture, you will learn the following
•What are Model Interpretability Methods to consider the interpretability and explainability of the selected models
Algorithm Selection & Development XX
Attention mechanisms in deep learning models7:51
At the end of this lecture, you will learn the following
•What are attention mechanisms in deep learning models to consider the interpretability and explainability of the selected models
Algorithm Selection & Development XXI
Model deployment and continuous monitoring16:50
At the end of this lecture, you will learn the following
•Deploy the trained model in a production environment and integrate it into the application workflow.
•Implement monitoring and logging mechanisms to track model performance, drift, and errors over time.
•Continuously evaluate and update the model as new data becomes available or the problem requirements change
Algorithm Selection & Development XXII
An example of researching, selecting, and developing appropriate algorithm8:16
At the end of this lecture, you will learn the following
•An example of researching, selecting, and developing appropriate machine learning algorithms or deep learning architectures based on the problem at hand and the available data

Feature Engineering I- How to identify and extract meaningful features5:17
At the end of this lecture, you will learn the following
•How to identify and extract meaningful features from the data to improve the performance of machine learning models
Feature Engineering I
How to engineer new features or transform existing features16:10
At the end of this lecture, you will learn the following
How to engineer new features or transform existing features
Feature Engineering II
How to select a subset of the most relevant features9:56
At the end of this lecture, you will learn the following
How to select a subset of the most relevant features
Feature Engineering III
How to reduce the dimensionality of the feature space8:33
At the end of this lecture, you will learn the following
How to reduce the dimensionality of the feature space while preserving as much relevant information as possible
Feature Engineering IV
Feature Engineering V- Remaining steps of feature engineering6:14
At the end of this lecture, you will learn the following
Remaining steps of feature engineering
Feature Engineering V
An example of identifying and extracting meaningful features7:24
At the end of this lecture, you will learn the following
An example of identifying and extracting meaningful features from a dataset to improve the performance of a machine learning model

Deployment I7:05
•How to deploy trained models into production environments, ensuring they integrate smoothly with existing systems and meet performance requirements- Model Serialization
Deployment I
Deployment II6:32
•How to deploy trained models into production environments, ensuring they integrate smoothly with existing systems and meet performance requirements- Remaining steps
Deployment II
An example of deploying trained models into production environments5:53
•An example of deploying trained models into production environments, ensuring they integrate smoothly with existing systems and meet performance requirements

Monitoring and Maintenance I4:30
•How to monitor the deployed models to ensure they continue to perform well over time, and update or retrain them as needed to adapt to changing conditions or requirements
Monitoring and Maintenance I
How to use statistical tests, visualization techniques, or drift detection algor7:53
At the end of this lecture, you will learn the following
How to use statistical tests, visualization techniques, or drift detection algorithms to identify data drift
Monitoring and Maintenance II
Model Drift Detection7:47
At the end of this lecture, you will learn the following
Model Drift Detection
Monitoring and Maintenance III
Monitoring and Maintenance IV4:49
•How to monitor the deployed models to ensure they continue to perform well over time, and update or retrain them as needed to adapt to changing conditions or requirements
Monitoring and Maintenance IV
An example of how to monitor the deployed models6:20
•An example of how to monitor the deployed models to ensure they continue to perform well over time, and update or retrain them as needed to adapt to changing conditions or requirements

Collaboration I4:15
At the end of this lecture, you will learn the following
•How to collaborate with data scientists, software engineers, and domain experts to develop comprehensive AI solutions that address real-world problems effectively
Collaboration I
Collaboration II4:08
At the end of this lecture, you will learn the following
•How to collaborate with data scientists, software engineers, and domain experts to develop comprehensive AI solutions that address real-world problems effectively
Collaboration II
An example of collaborating with data scientists, software engineers, and....7:01
At the end of this lecture, you will learn the following
•An example of collaborating with data scientists, software engineers, and domain experts to develop comprehensive AI solutions that address real-world problems effectively

Research and Innovation I4:58
At the end of this lecture, you will learn the following
How to conduct research to explore new techniques and methodologies that could improve the performance or efficiency of AI system
Research and Innovation I
Research and Innovation II4:57
At the end of this lecture, you will learn the following
•How to conduct research to explore new techniques and methodologies that could improve the performance or efficiency of AI systems
Research and Innovation II
An example of conducting research to explore new techniques & methodologies6:48
At the end of this lecture, you will learn the following
•An example of conducting research to explore new techniques and methodologies that could improve the performance or efficiency of AI systems

Requirements

Willing to spend 8+ hours learning about Artificial Intelligence

Description

Want to become an AI Engineer but feel overwhelmed by machine learning, deployment, automation tools, and the rapidly changing AI landscape?

Most learners study algorithms, models, or AI tools in isolation.

But employers hire AI Engineers who can take a business problem, prepare data, build models, deploy solutions, monitor performance, and continuously improve results.

Learn the complete AI Engineering lifecycle—from problem definition and data preparation to machine learning, deployment, monitoring, AI agents, n8n automation, and workflow design.

Complete a portfolio-ready capstone project and understand how real AI solutions are built and managed in practice.

What You Will Learn

How to understand the role of an AI Engineer in real-world projects
How to define AI problems aligned with business objectives
How to collect, clean, and preprocess data for machine learning models
How to build and optimize machine learning and deep learning models
How to apply feature engineering to improve model performance
How to deploy AI models into production environments
How to monitor, maintain, and continuously improve AI systems
How to work on end-to-end AI engineering workflows
Introduction to AI agents, automation, and modern AI tools
Understanding of ethical AI and responsible AI practices

Why This Course Stands Out

Most AI courses focus only on coding or algorithms.

This course focuses on complete AI engineering thinking and execution:

Learn the full lifecycle of AI development
Focus on practical application, not just theory
Build a mindset to solve business problems using AI
Designed to help you become job-ready as an AI Engineer

Built on Real Industry Learning

My journey into Artificial Intelligence began in 2020, when the demand for AI Engineers started growing rapidly across industries.

I studied real-world job requirements and worked closely with learners and professionals to understand:

What companies actually expect
How AI solutions are built in practice
What skills truly differentiate successful AI Engineers

This course brings together those insights into a clear, structured learning path for you.

Learn by Doing

Apply concepts through practical examples and structured learning
Build your understanding step-by-step across the AI lifecycle
Gain confidence to work on real AI problems

What Students Are Saying

“Great learning on problem definition, data preprocessing, and algorithm selection.”
“Well-structured course with clear explanations and strong fundamentals.”
“Helped me understand how AI works in real-world scenarios.”
“Valuable insights for anyone aspiring to become an AI Engineer.”

Who This Course Is For

Aspiring AI Engineers and Machine Learning Engineers
Data Analysts, Developers, and Professionals transitioning into AI
Students looking to build a career in Artificial Intelligence
Anyone who wants a structured, practical roadmap into AI engineering

Start with Confidence

Preview lectures for free before enrolling
Backed by Udemy’s 30-day money-back guarantee

Take the Next Step in Your AI Career

If you want to:

Build real AI solutions, not just learn concepts
Develop skills in machine learning, deep learning, and AI systems
Become job-ready for AI Engineering roles
Stay relevant in the fast-growing AI landscape

Then this course will give you the clarity, structure, and skills to succeed.

Start Now

Preview the course and begin your journey to becoming a successful AI Engineer.

This Course is Part of a Structured Learning Path

Learning Path: TECHNOLOGY PATH (Starter → Builder → Advanced)

This course is your ADVANCED step.

Next Recommended Courses

After completing this course, continue your growth with:

How to become Software Developer (Starter)

Software Development Excellence (Builder)

End to end Solution Design (Builder)

Solution Architecture (Builder)

IT Product Management (Advanced)

Generative AI (Advanced)

Who this course is for:

Aspiring AI Engineers and Machine Learning Engineers who want to learn the complete end-to-end AI development process
Aspiring AI Engineers and Machine Learning Engineers who want to learn the complete end-to-end AI development process
Software Engineers and Developers who want to build and deploy AI-powered applications
Software Engineers and Developers who want to build and deploy AI-powered applications
Professionals looking to upskill in AI, ML, and modern AI systems (including AI agents)
Product Managers and Tech Leaders who want to understand how AI solutions are designed, built, and scaled
Entrepreneurs and innovators who want to apply AI to solve real business problems
Anyone who wants a structured, practical roadmap to move from beginner to AI Engineer

Master AI Engineering: From Problem Definition to Deployment

What you'll learn

Explore related topics

Course content

Introduction2 lectures • 18min

Overview3 lectures • 22min

Problem Definition4 lectures • 26min

Data Collection & Preprocessing6 lectures • 1hr

Algorithm Selection & Development23 lectures • 3hr 12min

Feature Engineering6 lectures • 54min

Deployment3 lectures • 20min

Monitoring and Maintenance5 lectures • 31min

Collaboration3 lectures • 15min

Research and Innovation3 lectures • 17min

Requirements

Description

Who this course is for: