Programme Parameters
Graduate / Intermediate
3 Weeks (30 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI Product Development and Lifecycle focuses on the product-management craft for AI, which differs meaningfully from ordinary software. You learn to identify and validate AI product opportunities, define requirements around uncertain model behaviour, manage the experiment-heavy build, and launch, measure and iterate — accounting for data, model drift and the trust and ethics AI products need. The lens is the product and its lifecycle, not the model alone. You finish able to lead an AI product from concept through iteration. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI product development and lifecycle — taking an AI product from idea through build, launch and iteration, with the distinct challenges AI products bring.
📋 Course Objectives
1. Identify and validate AI product opportunities.
2. Define requirements around model uncertainty.
3. Manage the experiment-driven build.
4. Launch, measure and iterate AI products.
5. Account for drift, trust and ethics.
👥 Who Should Enroll?
• Product managers building AI products
• Founders and product leaders
• AI teams and technical PMs
• Students of AI product management
🚀 Key Learning Outcomes
• The ability to manage AI products.
• A product-lifecycle perspective.
• An AI product-management skill set.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and Foundations
Develop a comprehensive understanding of AI concepts, including machine learning, deep learning, and neural networks • Analyze mathematical foundations of AI, including linear algebra, calculus, and probability theory • Design simple AI models using popular libraries and frameworks, such as TensorFlow or PyTorch
Data Engineering, Preprocessing, and Feature Pipelines
Implement data preprocessing techniques, including data cleaning, feature scaling, and data transformation • Configure data pipelines using tools like Apache Beam, Apache Spark, or AWS Glue • Evaluate the quality of datasets and develop strategies for data augmentation and feature engineering
Model Architecture, Algorithm Design, and Methods
Design and implement various machine learning algorithms, including supervised, unsupervised, and reinforcement learning • Develop and evaluate model architectures, including convolutional neural networks, recurrent neural networks, and transformers • Optimize model performance using techniques like regularization, dropout, and early stopping
Training, Hyperparameter Optimization, and Evaluation
Train and evaluate machine learning models using popular frameworks like scikit-learn, TensorFlow, or PyTorch • Implement hyperparameter optimization techniques, including grid search, random search, and Bayesian optimization • Analyze model performance using metrics like accuracy, precision, recall, and F1-score
Deployment, MLOps, and Production Workflows
Deploy machine learning models using cloud platforms like AWS, Azure, or Google Cloud • Configure and manage model serving pipelines using tools like TensorFlow Serving, AWS SageMaker, or Azure Machine Learning • Develop and implement monitoring and logging strategies for model performance and data drift
Ethics, Bias Mitigation, and Responsible AI Practices
Evaluate the ethical implications of AI systems, including bias, fairness, and transparency • Develop and implement strategies for bias mitigation and fairness in AI systems • Analyze the impact of AI on society and develop responsible AI practices for real-world applications
Industry Integration, Business Applications, and Case Studies
Develop AI-powered solutions for real-world business problems, including customer segmentation, recommendation systems, and predictive maintenance • Analyze case studies of successful AI implementations in various industries, including healthcare, finance, and retail • Design and propose AI-powered products or services for a specific industry or market
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | scikit-learn |
| Covered Tool / Platform | Apache Beam |
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