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.
Most enterprises adopting AI lack a good answer to whether the results are reliable enough to trust. The Tuner role builds exactly that answer, with measurement methods that hold up in production.
Typical client situations
Examples of when the Ignite Tuner profile is the right one for the client.
- 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
7 kursDisse kursene er unike for Ignite Tuner. Felleskursene som alle Ignitere tar finner du på kursoversikten.
- TunerIn Discover
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- TunerIn Build
Evaluation craft: rubrics and LLM-as-judge
Design quality measures that survive contact with reality.
- TunerIn Build
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- TunerIn Operate
Fine-tuning and distillation
When it pays to train, and when to just not.
- TunerIn Operate
Dataset craft and labeling
Build golden datasets that actually teach the model — and the client — something.
- TunerIn Operate
Agent governance in production
Identity, oversight and control once agents go live.
- TunerIn Adopt
AI governance: frameworks, registries and control points
Keep track of who owns which data and what it can be used for.
Career path after graduation
After graduation you join client projects as an Applied AI or Eval Engineer. You help clients establish a quality regime around AI, adapt models to 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
- Claude Certified Associate – Foundations (required): Anthropic's foundational certification. Documents solid model understanding, prompting and safe use of the Claude platform — the foundation your evaluation work rests on. The curriculum is covered in the Foundations phase; the exam is 120 minutes at Pearson VUE.
- Microsoft AI-102 – Azure AI Engineer Associate (required): Documents that you can run AI solutions in production on Azure. Prepared through the platform and operations courses.
- Claude Certified Developer – Foundations (recommended next step): For those who want to go deeper into the API and agent building after graduation.
See how the year is structured
Six phases, 42 courses and 10 months. The program builds each profile gradually.