Master machine learning and AI agent systems through enterprise workflows and real-world use cases
Journey from Unknown to Known: Building Production-Ready Agents for Enterprise Delivery
A structured path that transforms junior engineers into confident ML and agent system experts capable of enterprise-level contributions
Starting point: Limited exposure to ML and agent systems. Understanding the problem space.
Learning fundamentals: ML concepts, agent architectures, and enterprise data patterns.
Hands-on training: Building agents using enterprise workflows and data sources.
Real use cases: Implementing agents for actual enterprise delivery scenarios.
Expert level: Contributing feature engineers capable of building production agents.
Comprehensive curriculum designed to build practical ML and agent development skills
Real-world scenarios where junior engineers build production-ready agent systems
Build agents that analyze pull requests, identify code quality issues, suggest improvements, and learn from enterprise coding standards.
Create ML models that automatically categorize, prioritize, and route support tickets based on historical enterprise data and patterns.
Develop agents that generate, update, and maintain technical documentation by learning from codebase changes and team interactions.
Build agents that monitor production systems, detect anomalies, and predict potential failures using enterprise operational data.
Construct conversational agents that answer employee questions by learning from internal documentation, wikis, and communication channels.
Create agents that automatically generate test cases, identify edge cases, and improve test coverage by analyzing code patterns.
Train agents using real enterprise data to deliver immediate business value
Learn from Git history, commit patterns, code reviews, and development practices to understand enterprise standards.
Analyze logs, performance data, and system metrics to identify patterns and build predictive models.
Extract knowledge from internal docs, technical specifications, and institutional memory.
Mine JIRA, GitHub Issues, and support systems for problem-solution patterns and escalation workflows.
Learn from Slack channels, email threads, and meeting notes to understand team dynamics and decision-making.
Study build failures, deployment patterns, and release processes to optimize delivery workflows.
Junior engineers don't just learn predefined use cases—they discover and create new ones through hands-on workshop simulations that mirror real-world scenarios
Our 10-step AI workshops function as immersive simulations where junior engineers actively build agents that scan, analyze, and relate content—just like production systems. Through this simulation-based learning, they naturally discover use cases unique to their enterprise context.
Learn more: Why We Call Workshops "Simulations"
Simulations provide a safe sandbox where juniors experiment with different agent architectures, discover edge cases, and identify automation opportunities without risk to production systems.
As juniors build agents in simulations, they recognize repetitive tasks and inefficiencies in current processes, naturally identifying new automation opportunities and use cases.
Working with actual enterprise data in simulations helps juniors identify use cases specific to their organization's unique workflows, tools, and challenges that generic training would miss.
Team-based simulations encourage juniors to share observations and build on each other's ideas, multiplying the discovery of potential use cases through collective intelligence.
Through multiple simulation cycles, juniors refine initial use case ideas, discover edge cases, and evolve simple automation into comprehensive agent systems that solve complex problems.
The act of building agents in simulations reveals hidden complexities and opportunities that theoretical learning misses, leading to discovery of novel use cases during the development process itself.
Traditional training teaches fixed use cases. Our simulation approach empowers juniors to become use case discoverers—actively identifying and creating solutions as they work with real enterprise systems and data.
Our structured approach transforms junior engineers into productive feature engineers capable of building and deploying enterprise agent systems. By focusing on real workflows, actual enterprise data, and proven use cases, juniors gain practical experience that immediately contributes to organizational success.
Embed ML and agent training directly into your development workflow
Evaluate current skill levels and identify knowledge gaps. Set clear learning objectives aligned with enterprise needs and establish baseline metrics for progress tracking.
Follow a progressive curriculum from ML fundamentals to agent systems. Each module includes theoretical concepts, practical exercises, and real-world examples from enterprise scenarios.
Build agents for actual use cases using real enterprise data. Work in version-controlled repositories with code reviews, testing, and deployment pipelines.
Regular code reviews from senior engineers, pair programming sessions, and feedback loops ensure continuous improvement and adherence to best practices.
Deploy agents to production environments with proper monitoring, observability, and incident response procedures. Learn from real-world performance and user feedback.
Iterate based on metrics, feedback, and new requirements. Stay updated with latest ML techniques and agent frameworks while maintaining production systems.
A proven framework to guide junior engineers in building production-ready enterprise agents
Start building a team of feature engineers capable of delivering production-ready ML and agent systems