Priority trainings, certification tracks, and specialization pathways for the Canada AI & Data practice. Stay current with the skills that matter most.
The most important trainings typically include Generative AI fundamentals, Responsible AI and ethics, data engineering and analytics foundations, and practical AI development using platforms like Azure AI and Databricks.
Core understanding of generative models, prompt engineering, and enterprise applications of Gen AI.
Ethics, governance, and trust frameworks for building AI systems responsibly.
Hands-on skills with Microsoft's AI platform and Azure OpenAI Service.
Lakehouse architecture, Spark-based engineering, and MLflow for production ML.
Foundational ML concepts, model lifecycle, and practical applications.
Autonomous AI agents, multi-agent systems, and enterprise transformation with agentic architectures.
The AI & Data practice offers several specialization paths. Align your learning journey to the track that best fits your career goals.
Build and optimize data pipelines, ETL/ELT, and large-scale data processing
Data quality, cataloging, lineage, compliance, and Purview
Model development, deployment, monitoring, and lifecycle management
LLMs, RAG, prompt engineering, and Gen AI applications
Visualization, BI dashboards, and data-driven insights
Azure, AWS, GCP infrastructure and platform architecture
Ethics, fairness, transparency, and AI governance frameworks
For Data Governance specialists, these industry-recognized certifications are valuable for demonstrating expertise.
Certifications may take a few days to reflect in Workday after completion. If your certifications aren't appearing, check with your People Lead or HR. Align your certification path with your specialization track for maximum impact.