Programme Parameters
Graduate / Intermediate
4 Weeks (40 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 Project Management addresses why so many promising AI initiatives never ship: the hard part is rarely the model. You will learn to frame a business problem as a solvable ML task, assess data readiness, and set realistic success metrics before a line of code is written. The course covers the distinctive lifecycle of AI work — experimentation, iteration, deployment, monitoring and drift — and how to manage its uncertainty, stakeholders and risks, including ethics and compliance. You leave able to lead an AI project that delivers measurable value. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
AI Project Management teaches how to scope, staff, run and de-risk machine-learning projects — from problem framing and data readiness to deployment, monitoring and ROI.
📋 Course Objectives
1. Frame a business problem as a solvable ML task.
2. Assess data readiness and set realistic success metrics.
3. Manage the experiment-to-deployment AI lifecycle.
4. Handle uncertainty, stakeholders and delivery risk.
5. Plan monitoring, drift and ethical/compliance safeguards.
👥 Who Should Enroll?
• Project and product managers leading AI initiatives
• Team leads and technical managers in data teams
• Consultants and analysts scoping AI work
• Engineers moving into AI leadership roles
🚀 Key Learning Outcomes
• The ability to scope and lead an AI project to delivery.
• A project plan and risk framework you can reuse.
• The judgement to separate viable AI ideas from hype.
• 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 Ai Project Management Foundations
Develop a comprehensive understanding of AI fundamentals, including machine learning, deep learning, and neural networks • Analyze mathematical concepts underlying AI, such as linear algebra, calculus, and probability theory • Design a framework for AI project management, incorporating agile methodologies and stakeholder engagement
Data Engineering, Preprocessing, and Feature Pipelines
Configure data pipelines using tools like Apache Beam, Apache Spark, and AWS Glue • Implement data preprocessing techniques, including data cleaning, feature scaling, and data transformation • Evaluate data quality and develop strategies for data validation, data normalization, and data augmentation
Model Architecture, Algorithm Design, and Ai Project Management Methods
Design and implement model architectures using popular deep learning frameworks like TensorFlow, PyTorch, and Keras • Develop and evaluate algorithmic solutions for supervised, unsupervised, and reinforcement learning tasks • Apply AI project management methods, including project planning, risk management, and team collaboration
Training, Hyperparameter Optimization, and Evaluation
Train and fine-tune machine learning models using techniques like transfer learning, regularization, and early stopping • Implement hyperparameter optimization methods, including grid search, random search, and Bayesian optimization • Evaluate model performance using metrics like accuracy, precision, recall, F1-score, and mean squared error
Deployment, MLOps, and Production Workflows
Deploy machine learning models using cloud platforms like AWS SageMaker, Google Cloud AI Platform, and Azure Machine Learning • Implement MLOps practices, including model monitoring, model serving, and model updating • Design and automate production workflows using tools like Docker, Kubernetes, and Apache Airflow
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze and address ethical concerns in AI, including bias, fairness, and transparency • Develop and implement strategies for bias mitigation, including data curation, feature engineering, and model regularization • Evaluate and ensure compliance with responsible AI practices, including explainability, accountability, and human oversight
Industry Integration, Business Applications, and Case Studies
Apply AI solutions to real-world business problems, including customer segmentation, demand forecasting, and recommender systems • Develop and evaluate AI-powered products and services, including chatbots, virtual assistants, and predictive maintenance • Analyze and discuss case studies of successful AI implementations in various industries, including healthcare, finance, and retail
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Keras |
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