Course Code: slmsdsa
Duration: 28 hours
Prerequisites:
  • Basic understanding of machine learning concepts
  • Familiarity with Python programming
  • Knowledge of natural language processing fundamentals

Audience

  • Data scientists
  • Machine learning engineers
Overview:

Small Language Models (SLMs) are a cutting-edge subset of AI that enables efficient language processing on devices with limited computational resources.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data scientists and machine learning engineers who wish to create and apply small language models tailored for specific domains such as legal, medical, and technical fields.

By the end of this training, participants will be able to:

  • Understand the importance and application of domain-specific language models.
  • Curate and preprocess specialized datasets for model training.
  • Train and fine-tune language models for domain-specific applications.
  • Evaluate and benchmark models using domain-relevant metrics.
  • Deploy domain-specific language models in real-world scenarios.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.
Course Outline:

Introduction to Domain-Specific Language Models

  • Overview of language models in AI
  • Importance of specialization in language models
  • Case studies of successful domain-specific models

Data Curation and Preprocessing

  • Identifying and collecting domain-specific datasets
  • Data cleaning and preprocessing techniques
  • Ethical considerations in dataset creation

Model Training and Fine-Tuning

  • Introduction to transfer learning and fine-tuning
  • Selecting base models for domain-specific training
  • Techniques for effective fine-tuning

Evaluation Metrics and Model Performance

  • Metrics for domain-specific model evaluation
  • Benchmarking models against domain-specific tasks
  • Understanding limitations and trade-offs

Deployment Strategies

  • Integration of language models into domain-specific applications
  • Scalability and maintenance of deployed models
  • Continuous learning and model updates in deployment

Legal Domain Focus

  • Special considerations for legal language models
  • Case law and statute corpus for training
  • Applications in legal research and document analysis

Medical Domain Focus

  • Challenges in medical language processing
  • HIPAA compliance and data privacy
  • Use cases in medical literature review and patient interaction

Technical Domain Focus

  • Technical jargon and its implications for language models
  • Collaboration with subject matter experts
  • Technical documentation generation and code commenting

Project and Assessment

  • Project proposal and initial dataset collection
  • Presentation of a completed project and model performance
  • Final assessment and feedback

Summary and Next Steps

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