Large Language Models: Build & Deploy

What you'll learn

• Building and evaluating ML models
• Preparing data for training
• Tuning model performance
• Deploying models to production
• Understand supervised and unsupervised learning
• Understand deploying and monitoring models

Planned outline shown above (sample metadata). The detailed module and lesson list for this course hasn't been published yet.

Description

Understand and build LLMs from scratch. Fine-tune open-source models like LLaMA and Mistral and deploy them using Hugging Face and AWS SageMaker.

What this course covers: Large Language Models: Build & Deploy works through supervised and unsupervised learning, deploying and monitoring models, neural network architectures and model training and evaluation.

Skills you will build: deploying models to production, tuning model performance and preparing data for training.

Practical applications: prediction and classification systems, computer vision and NLP applications and AI features inside products.

Who should take this course: developers, data analysts and engineering students moving into machine learning and AI. It is designed for learners with solid experience in machine learning and AI who want advanced, in-depth practice.

Learning outcomes: by the end of the course you should be able to apply supervised and unsupervised learning and deploying models to production to your own work or projects, and know which areas to practise next.

Requirements

• Solid working knowledge of machine learning and AI
• Comfort with intermediate concepts
• A computer with internet access

Your instructor

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  Pranav D. ★★★★★ 5.0/5 Sample review

  The section on neural network architectures was explained step by step, and the examples made model training and evaluation much easier to follow. The hands-on notebooks made backpropagation finally make sense to me.

• A
  Aarav M. ★★★★★ 4.5/5 Sample review

  Good balance of theory and practice. The deploying and monitoring models lessons helped me apply tuning model performance in real situations. The deployment section filled a gap most ML courses skip.

• S
  Simran Z. ★★★★★ 5.0/5 Sample review

  Well organised. Revisiting the deploying and monitoring models part a second time really helped it click. Loved that model evaluation got as much attention as model building.

Sample reviews are illustrative demo content, not verified purchases. Ratings from learners will appear here once submitted.