Udemy – AI Project Lifecycle Mastery: Strategy to Deployment – 2025 2025-5

Udemy – AI Project Lifecycle Mastery: Strategy to Deployment – 2025 2025-5 Downloadly IRSpace

Aug 17, 2025 - 02:18 Updated: Aug 19, 2025 - 14:14

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Udemy – AI Project Lifecycle Mastery: Strategy to Deployment – 2025 2025-5

AI Project Lifecycle Mastery: Strategy to Deployment – 2025. This course teaches participants how to manage the full lifecycle of AI projects from strategy design to implementation and monitoring. With over 85% of AI projects failing due to lack of planning and a misunderstanding of the lifecycle, this course is designed as a practical guide for product managers, engineers, and enthusiasts. Topics begin by defining basic concepts such as AI, generative models, and AI agents, and then cover key steps including defining business objectives, developing a data strategy, selecting and evaluating models, and deploying practical solutions. The importance of adhering to ethics, regulations, and avoiding common pitfalls is also emphasized. No programming background is required, participants work with tools such as Pandas, SageMaker, and Hugging Face, and hone their skills through case studies and hands-on exercises. At the end of the course, you will be able to effectively lead AI projects.

What you will learn

Complete understanding of the life cycle of AI projects: from initial concept and problem definition to model deployment and monitoring.
Building a strong foundation in AI fundamentals: including traditional AI, generative AI, and autonomous AI agents.
Familiarity with the unique challenges and requirements of AI projects: compared to conventional software development.
Transform business problems into AI use cases: by identifying high-value applications and clearly defining key performance indicators (KPIs).
Master the art of building effective AI teams: including data scientists, ML engineers, domain experts, and project managers.
Understand the differences between structured and unstructured, labeled and unlabeled data: and select appropriate internal and external data sources.
Design a comprehensive data strategy: including data collection, management, access control, and lifecycle management.
Mastery of data cleansing techniques, feature engineering, and dataset versioning: and understanding the importance of data quality and labeling accuracy for model performance.
Selecting appropriate AI/ML models based on problem type and data availability: and learning the trade-offs of different architectures.
Applying appropriate metrics (accuracy, F1 score, ROC AUC, etc.) to evaluate models: and using testing strategies and open-source scorecards to benchmark performance.
Understanding of MLOps practices: such as CI/CD, model provisioning, monitoring, and automated retraining.
Learn how to set up performance monitoring pipelines: to track AI model deviations, errors, and model performance degradation.
Understanding the ethical implications of AI: learning how to comply with legal frameworks, ensure fairness and transparency, and avoid bias.
Using tools and platforms like Pandas, Hugging Face, Kaggle, and Google Teachable Machines.
Understand the differences between databases, data lakes, and data warehouses for storing AI data.

This course is suitable for people who:

Product managers who want to define the AI product vision, set KPIs, and lead cross-functional teams throughout the AI lifecycle.
Engineers and data scientists who want to master the full AI project lifecycle from data strategy to deployment, beyond just model building.
Business leaders and analysts who want to use AI to improve decision-making, optimize operations, and gain competitive advantage.
Project managers who want to manage the schedule, risks, and resources of AI projects. No technical background required.
Executives and innovation leaders who evaluate AI investments, align them with strategy, and drive successful adoption across the business.
Students and career changers who want to gain practical AI project management skills without the need for prior experience.