Hugging Face
Develop the skills to package and evaluate a model inference pipeline through a learning path centred on Hugging Face. Explore concepts, guided practice and an applied project.
Build practical Hugging Face skills.
Skills and knowledge developed in this course.
- Hub workflows
- Transformers use
- Dataset processing
- Model delivery
- Applied project: Package and evaluate a model inference pipeline
- Hugging Face — validation and review
About this course
This course develops practical capability in Hugging Face. Its course-specific content covers Hub workflows, Transformers use, Dataset processing, Model delivery, followed by an applied project focused on package and evaluate a model inference pipeline. The lessons include concrete configuration, design, testing or operational decisions appropriate to the subject.
Who this is for
Administrators, developers and practitioners who work with Hugging Face or plan to take on responsibilities in this area.
Before you start
Basic computer literacy and a foundational understanding of artificial intelligence & generative ai. Prior experience with the named product helps with the practical exercises.
Package and evaluate a model inference pipeline
Deliver package and evaluate a model inference pipeline. Use the module outcomes to state requirements, demonstrate a working result, test an expected failure or boundary case, and document assumptions and follow-up work.
Course content
Course-specific syllabus and practical exercises. Lab release, access arrangements and final delivery scope are confirmed before enrolment.
01Hub workflows
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- Model cards, datasets and revisions
- Licensing and gated-resource access
Practical exerciseApply hub workflows to the package and evaluate a model inference pipeline and capture the validation evidence.
02Transformers use
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- Tokenisers, pipelines and model loading
- Batching, device selection and inference settings
Practical exerciseApply transformers use to the package and evaluate a model inference pipeline and capture the validation evidence.
03Dataset processing
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- Splits, mapping and preprocessing
- Evaluation metrics and reproducible experiments
Practical exerciseApply dataset processing to the package and evaluate a model inference pipeline and capture the validation evidence.
04Model delivery
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- Save artifacts and record configuration
- Compare model outputs and deployment constraints
Practical exerciseApply model delivery to the package and evaluate a model inference pipeline and capture the validation evidence.
05Applied project: Package and evaluate a model inference pipeline
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- Define inputs, assumptions and acceptance criteria for: Package and evaluate a model inference pipeline.
- Implement the project using hub workflows, transformers use, dataset processing, model delivery.
Practical exercisePackage and evaluate a model inference pipeline.
06Hugging Face — validation and review
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- Demonstrate the completed project against its acceptance criteria.
- Exercise an invalid-input or failure scenario and document the observed result.
Practical exercisepresent the project evidence, limitations and a repeatable handover.
Technical references
These primary vendor, project or standards sources inform this independent IT Purview outline. Reference pages checked 25 October 2026; current provider documentation and exam objectives take precedence when versions change.
From understanding to application.
Progress by learning milestone. The schedule is agreed around your starting level and learning goals.
- 01Learning milestone 1 In progress
Set the context
Hub workflows · Transformers use
Establish the Hugging Face scope, baseline concepts and operating assumptions. - 02Learning milestone 2 In progress
Build the working skills
Dataset processing · Model delivery
Apply the core Hugging Face techniques in the course lab and record the result. - 03Learning milestone 3 In progress
Validate and handle exceptions
Check expected behavior, investigate a failure case and capture evidence for package and evaluate a model inference pipeline. - 04Learning milestone 4 In progress
Deliver the applied project
Complete package and evaluate a model inference pipeline, explain key decisions and identify a practical next step.
Career outlook after this learning path
Typical roles this roadmap can support, with broad salary ranges by relevant work experience.
| Role | 0–3 years | 3–7 years | 7–12+ years |
|---|---|---|---|
| AI / ML Engineer | ₹10–17 LPA | ₹20–35 LPA | ₹38–62 LPA |
Ranges are indicative India-market compensation estimates in ₹ lakh per annum, not a salary promise. They reflect broad market guides and vary by location, employer, skills and role scope; the experience bands refer to relevant work experience, not time since training. Completing this course does not guarantee employment, a role, compensation or career progression. View market reference ↗