Course 07
Accelerate Your Model
A functional model is useless if too slow or expensive to run. This course focuses on MLOps and optimization, particularly relevant for the African context where compute resources may be constrained and mobile deployment is essential.
Key Topics
- Inference Optimization: Reducing latency
- Quantization: Reducing model precision (8-bit, 4-bit)
- Distillation: Training smaller student models
- Hardware acceleration (TPUs/GPUs)
- Mobile and edge deployment
Learning Outcomes
- Understand the engineering challenges of deployment
- Learn techniques to make models efficient for real-world use
- Bridge the gap between research prototype and production
- Deploy models on resource-constrained devices
- Benchmark accuracy vs speed trade-offs
Practical Projects
- Quantize models to 8-bit and benchmark accuracy loss
- Knowledge Distillation: Teacher to Student model transfer
- Deploy a quantized ASR model on mobile devices
Recommended Datasets
6 datasets are aligned with this course for hands-on exercises and projects.
Malaria Cell Images
Parasitized vs Uninfected Blood Cell Images (NIH)
English (Metadata)
KenSwQuAD
Swahili Question Answering Dataset
Swahili
SIB-200
Benchmark for 200+ Languages
200+ including many African LRLs
AfriSpeech-200
Pan-African Speech Corpus
English (120 African accents)
IrokoBench
Comprehensive Benchmark for African Languages
Hausa
Igbo
Yoruba
Swahili
+2 more
Nigerian Colposcopy Images
Cervical Cancer Screening Images from Nigeria
English (Metadata)
About This Course
Many deployment scenarios in Africa involve limited computational resources. This course teaches you to make models smaller, faster, and more efficient without sacrificing too much performance.
Key Topics
- Quantization: Reducing model precision for efficiency
- Pruning: Removing unnecessary model parameters
- Knowledge Distillation: Training smaller models from larger ones
- Efficient Architectures: MobileNet, DistilBERT, and similar models
- Edge Deployment: Running models on mobile and IoT devices
Prerequisites
- Completion of Courses 01-06 or equivalent knowledge
- Understanding of model architectures
- Experience with deployment tools