Avoid the 85% failure rate. Learn to plan, manage, build, & deploy AI projects that succeed in the real world.
What you'll learn
- Understand the full lifecycle of AI projects from initial concept and problem definition to model deployment and monitoring.
- Build a strong foundation in AI fundamentals, including traditional AI, generative AI, and autonomous AI agents.
- Learn the unique challenges and requirements of AI projects compared to conventional software development.
- Translate business problems into AI use cases by identifying high-value applications and clearly defining success metrics (KPIs).
- Master the art of building effective AI teams, including data scientists, ML engineers, domain experts, and project managers.
- Understand differences between structured vs. unstructured, labeled vs. unlabeled and choosing appropriate internal and external data sources.
- Design a comprehensive data strategy that includes data collection, governance, access control, and lifecycle management.
- Master data cleaning techniques, feature engineering, & dataset versioning. Understand the importance of data quality & labeling accuracy for model performance.
- Select suitable AI/ML models based on the problem type and data availability and learn the trade-offs of different architectures.
- Apply appropriate metrics (accuracy, F1 score, ROC AUC, etc.) to evaluate models. Use testing strategies and open-source leaderboards to benchmark performance.
- Understand MLOps practices such as CI/CD, model serving, monitoring, and automated retraining.
- Learn how to set up performance monitoring pipelines to track AI Models drift, errors, and model decay.
- Understand the ethical implications of AI. Learn to navigate legal frameworks, ensure fairness and transparency, and prevent bias.
- Use tools and Platforms like Pandas, Hugging Face, Kaggle, & Google Teachable Machines.
- Understand the differences between Databases, Data Lakes, and Data Warehouses for AI data storage.