Nordic Global Solutions
Applied AI and MLOps Engineering
Professional Certification
Program Focus
The curriculum focuses on production engineering. Students deploy models as scalable microservices on AWS and GCP.
Instructors
Craig Martin
Principal DevSecOps Engineering Leader
Experience includes RedHat, PerceptiLabs, GE Digital, Intel Media, and Verizon. Expertise in DevSecOps initiatives, emerging technologies, and strategic planning. Founder of Nordic Global Solutions. Experience includes Nordic Global Trade, Workflowspace, Affiliate Flow, and NorthStarOS. Expertise in venture building, strategic leadership, and market expansion.
Lars-Josef Lindbom
Founder of Nordic Global Solutions
Experience includes Nordic Global Trade, Workflowspace, Affiliate Flow, and NorthStarOS. Expertise in venture building, strategic leadership, and market expansion. Background in scaling technology ventures and ecosystem development. Co-founded and led multiple platforms from concept to operational scale. Focus on strategic partnerships, operational excellence, and connecting talent with opportunity across markets.
Included Services
- Weekly mentorship calls with Craig Martin and Josef
- Discord community access
- Physical office access in England
- Human code review for all assignments
- 100 hours of A100 GPU time
- Server and hosting access
- Resume rewriting
- Three mock technical interviews
- Potential for integration into internal company projects
16-Week Curriculum
Phase 1: Data Engineering (Weeks 1–4)
- Week 1: Advanced Python and Vectorization
- Week 2: SQL and NoSQL for AI including vector databases
- Week 3: Data cleaning and reproducible pipelines
- Week 4: Project — Build an automated ETL pipeline
Phase 2: Machine Learning and Deep Learning (Weeks 5–10)
- Week 5: Supervised learning and hyperparameter tuning
- Week 6: Neural networks with PyTorch
- Week 7: Computer vision and object detection
- Week 8: NLP and Large Language Models
- Week 9: Generative AI and Retrieval-Augmented Generation
- Week 10: Project — Fine-tune a custom Large Language Model
Phase 3: MLOps and Deployment (Weeks 11–14)
- Week 11: Containerization with Docker
- Week 12: Model serving and FastAPI design
- Week 13: Cloud deployment on AWS and GCP
- Week 14: Monitoring and observability
Phase 4: Capstone and Career (Weeks 15–16)
- Week 15: Capstone development — pipeline, model, and interface
- Week 16: Demo day and technical mock interviews
Portfolio Projects
- Real-Estate Price Predictor API — XGBoost and FastAPI
- Semantic Search Engine for Code — BERT and Pinecone
- Autonomous Agent for Scheduling — LangChain