Ignite Tuner
Knows whether the AI answer is actually good enough.
Ignite Tuner combines data craft with AI evaluation. You design how the enterprise measures whether AI answers are actually correct, how data flows into models, and how fine-tuning and hybrid pipelines create measurable improvement.
Every enterprise asks 'how do we know the AI answer is good enough?'. Few can answer. This role defines it in practice — and creates a clear competitive advantage.
Typical client situations
Eksempler på når Ignite Tuner-profilen er den riktige for kunden.
- 01The client has AI in use but doesn't know if answers are good enough for production
- 02The client has lots of internal data to be searchable or answerable — RAG or graph RAG needs
- 03The client is considering fine-tuning and needs someone who knows when it pays off and when not to
- 04The client wants to build agentic analytics on top of a data platform (Databricks, Fabric, or own lake)
Core skills
- Evaluation as craft: rubrics, inter-rater reliability, LLM-as-judge with calibration
- RAG depth: embedding choice, vector DB trade-offs, graph RAG, hybrid pipelines
- Fine-tuning and distillation: when, why, how (and most often, when-not-to)
- Natural-language-to-SQL, agentic analytics, automated insight generation
- Data governance for AI: lineage, consent, sensitivity classification
- Classical ML meets GenAI: hybrid pipelines and feature engineering
Tools and frameworks
- Python · Databricks · Microsoft Fabric
- Langfuse · Weights & Biases · MLflow
- Pinecone · Weaviate · pgvector
- DSPy · Instructor · Pydantic AI
- Unity Catalog · Apache Iceberg · Delta Lake
Example deliverables
- 01Eval framework that measures groundedness, relevance and latency over time
- 02RAG depth with hybrid search and documented quality improvement
- 03Fine-tuned model for domain-specific task with measurable lift
- 04Agentic analytics flow: natural language → insight → action
Track courses
6 kursDisse kursene er unike for Ignite Tuner. Felleskursene som alle Ignitere tar finner du på kursoversikten.
- TunerIn Craft
Evaluation craft: rubrics and LLM-as-judge
Design quality measures that survive contact with reality.
- TunerIn Craft
Vector DB trade-offs
Pick the right vector DB for your data and workload — pgvector, Pinecone, Weaviate.
- TunerIn Craft
Graph RAG and hybrid retrieval
When flat RAG isn't enough — how to find answers that require context.
- TunerIn Launch
Fine-tuning and distillation
When it pays to train, and when to just not.
- TunerIn Launch
Natural-language-to-SQL and agentic analytics
Let the model query the data directly — safely.
- TunerIn Anchor
Data governance for AI: lineage and consent
Keep track of who owns which data and what it can be used for.
Career path after graduation
After graduation you are billable as an Applied AI / Eval Engineer. You help clients build a quality regime around AI, fine-tune for their domain, or build the platform for agentic analytics.
Who should choose this track
Choose Tuner if you want to sit at the interface between data and AI, care about quality and measurability, and want to be the person who dares to say 'the model is not good enough yet'.
Certifications
- Microsoft AI-102 (required)
- AWS AI Practitioner (recommended)