AI Training

This track helps leaders make confident AI decisions—strategy, investment, governance, and measurable value.

• AI & GenAI landscape: what’s real, what’s hype, what’s next

• Live Enterprise model: AI-powered decisions, agility at scale, always-on learning

• AI value mapping: use-case identification and ROI frameworks

• AI governance and responsible AI: risk, auditability, compliance, and controls

• Operating models: AI CoE, build-vs-buy, vendor strategy, talent planning

• Change management: adoption, communication, workforce impact, and ethics

Outcome: leaders can approve AI initiatives with clarity and reduce transformation risk.

These pathways help non-technical teams leverage AI responsibly and effectively.

• Using AI copilots and enterprise AI tools safely

• Prompting for productivity (without exposing sensitive information)

• Decision support: interpreting AI outputs, validation, and human oversight

• Process improvement with AI automation concepts

• Function-specific modules:

  • Sales & Marketing: personalization, content ops, lead scoring, customer insights

  • Customer Service: triage, knowledge assistants, quality monitoring

  • Finance: forecasting, reconciliation automation, fraud/risk concepts

  • HR: policy copilots, talent analytics, employee experience automation

  • Operations & Supply Chain: demand forecasting, anomaly detection, scheduling concepts

Outcome: faster adoption and better AI usage quality across functions.

AI success depends on data quality and governance. This track supports data engineers, analysts, and platform teams.

• Data architectures: lakehouse, warehouse, streaming, event-driven pipelines

• Data quality, lineage, and governance essentials

• Feature engineering concepts and feature store fundamentals

• Dataset versioning, labeling workflows, and reproducibility

• Privacy and access controls: data residency, encryption, and role-based access

• Instrumentation for marketing and product analytics (events, attribution basics)

Outcome: a data foundation that supports reliable models and scalable AI systems.

This program trains teams to build and evaluate ML systems with professional engineering discipline.

• Supervised learning, unsupervised learning, anomaly detection

• Model evaluation: accuracy, precision/recall, calibration, bias checks (where relevant)

• Feature engineering and model selection

• Time-series forecasting: demand, operations, predictive maintenance

• Deep learning fundamentals and practical use

• Explainability techniques for high-stakes use cases

• Deployment basics: APIs, batch scoring, latency considerations

Outcome: teams can build robust ML models and deploy them in business workflows.

This track is designed for developers building GenAI applications for enterprises.

• GenAI fundamentals: LLM capabilities and limitations

• Prompt engineering and prompt patterns (with guardrails)

• Retrieval-Augmented Generation (RAG): ingestion, chunking, embeddings, vector search

• Building enterprise search with citations and role-based access boundaries

• Evaluation harnesses for GenAI: relevance, hallucination testing, regression tests

• Safety patterns: refusal behavior, sensitive data handling, escalation flows

• Cost and performance: caching, retrieval tuning, latency optimization

Outcome: teams can build secure, reliable GenAI copilots beyond “chatbot demos.”

This program trains teams to build AI systems that can take actions safely.

• Agent architectures: tool calling, planning, memory, policies

• Workflow orchestration and integration with enterprise systems

• Human-in-the-loop controls and approval gates

• Action logging, audit trails, and governance models

• Testing and monitoring agent behavior

• Reliability patterns: fallbacks, retries, bounded actions, safe termination

Outcome: teams can design agentic automation responsibly with enterprise controls.

For industries that use imaging, inspection, surveillance, and perception systems.

• Object detection and segmentation

• OCR and document vision

• Video analytics fundamentals

• Dataset collection, labeling workflows, and drift management

• Edge inference design: deployment constraints and monitoring

• Evaluation metrics and real-world testing practices

Outcome: teams can build and deploy vision solutions reliably in operational environments.

This track industrializes AI by training platform teams and ML engineers on operations.

• CI/CD for ML and GenAI applications

• Model registry, versioning, approvals

• Monitoring for drift, performance, data issues

• Retraining pipelines and release management

• LLMOps: prompt/version management, retrieval monitoring, safety regression tests

• Observability dashboards and incident response playbooks

• FinOps for AI: cost visibility, usage governance, and resource allocation

Outcome: AI systems remain stable, secure, and continuously improving in production.

For risk, compliance, legal, and cybersecurity stakeholders.

• Responsible AI concepts: fairness, explainability, privacy, accountability

• Threats and risks in GenAI: data leakage, prompt injection, unsafe outputs

• Governance controls: policy, audit logs, approvals, model documentation

• Evaluation and red-teaming approaches

• Secure architecture patterns for AI systems

• Regulatory alignment guidance (high-level, organization-specific)

Outcome: better risk control and improved trust for scaling AI.

Maayan emphasizes learning-by-building. We provide guided labs and capstones using your context:

• Build a RAG-based internal assistant with citations and access controls

• Develop a churn prediction model and deploy it to a dashboard

• Create a document automation pipeline for invoices or forms

• Develop a demand forecasting model and integrate outputs into planning

• Build an agentic workflow for ticket triage and resolution suggestions

Outcome: training turns into deployable assets, not just classroom knowledge.