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Course Code: IT 1888
8 Course Visits
Advanced Deep Learning Engineering and Model Optimization
Course Sector:
Information Technology
Course Dates and Locations
Choose a date and location to book your seat
No.
Date
Days
Location
Fees
Enrollment
01
5 Days
Riyadh, KSA $4,250
02
5 Days
Online, Virtual $2,150
03
5 Days
Dubai, UAE $4,250
Introduction
Training course introducion / brief
This course is designed for experienced ML and AI professionals who want to advance their skills in building and optimizing deep learning models for real-world deployment at scale. It covers best practices in model engineering, architectural design, GPU acceleration, and evaluation techniques using state-of-the-art frameworks such as PyTorch, TensorFlow, and ONNX. Emphasis is placed on performance optimization, efficient training, model validation, and deployment-ready design.
Course Objectives
At the end of the training course, participants will be able to
By the end of this course, participants will be able to:
- Design and implement advanced deep learning architectures (CNNs, RNNs, Transformers).
- Optimize deep learning models using quantization, pruning, and mixed precision.
- Apply advanced image preprocessing and augmentation techniques.
- Leverage GPU acceleration and distributed training for large-scale model development.
- Perform rigorous model validation and evaluation.
- Deploy optimized models using tools like TensorRT, ONNX, and TorchScript.
- Utilize model monitoring and A/B testing strategies in production.
Course Audience
Who is this course for, and can benefit the most
Course Outline
The course aims and learning outcomes
Module 1: Foundations of Deep Learning Engineering
- Review of supervised, unsupervised, and self-supervised learning
- Overview of deep learning architectures (CNNs, RNNs, Transformers)
- Frameworks: PyTorch vs TensorFlow deep dive
- Anatomy of a deep learning pipeline
Module 2: Advanced Model Architecture Design
- Custom layers and modular model building
- ResNet, Inception, EfficientNet architectures
- Transformer models (BERT, ViT, GPT-like architectures)
- Autoencoders and GANs for generative tasks
Module 3: Image Processing & Data Pipeline Optimization
- Advanced image preprocessing (CLAHE, edge detection, segmentation)
- Data augmentation techniques (MixUp, CutMix, Albumentations)
- Building efficient input pipelines using TF Data and PyTorch Dataloaders
- Handling large datasets and caching
Module 4: Training Optimization Techniques
- Hyperparameter tuning (Optuna, Ray Tune)
- Mixed precision training (NVIDIA Apex, AMP)
- Gradient accumulation and learning rate schedules
- Early stopping, checkpointing, and model versioning
Module 5: GPU Acceleration and Distributed Training
- GPU vs TPU architecture overview
- Multi-GPU and distributed training with DDP and Horovod
- Profiling and debugging with TensorBoard, NVIDIA Nsight, and PyTorch Profiler
- Efficient memory and compute usage
Module 6: Model Compression and Inference Optimization
- Model pruning and quantization (Post-training and Quantization-Aware Training)
- Knowledge distillation and teacher-student training
- Exporting models to ONNX, TorchScript, and TensorFlow Lite
- Inference optimization with TensorRT and OpenVINO
Module 7: Robust Model Evaluation and Validation
- K-fold cross-validation and stratified sampling
- Confusion matrix, ROC-AUC, precision-recall curve analysis
- Advanced metrics for regression and classification
- Out-of-distribution detection and adversarial robustness
Module 8: Scalable Model Deployment & Monitoring
- Serving models with TensorFlow Serving, TorchServe, or FastAPI
- Deploying models in Kubernetes and cloud environments (GCP, AWS, Azure)
- Model monitoring, drift detection, and real-time feedback loops
- A/B testing and canary deployments
Providers and Associations
Providing the best training services and benefits to our valued clients
Boost certificate of completion
BOOST's Professional Attendance Certificate “BPAC” is always given to the delegates after completing the training course, and depends on their attendance of the program at a rate of no less than 80%, besides their active participation and engagement during the program sessions.
ENDORSED EDUCATION PROVIDER
Flexible deadlines
Customized dates accordance to your schedule
Shareable Certificate
Earn certificate upon completion
COURSE METHODOLOGY
Our Training programs are implemented by combining the participants' academic knowledge and practical practice (30% theoretical / 70% practical activities).
At The end of the training program, Participants are involved in practical workshop to show their skills in applying what they were trained for. A detailed report is submitted to each participant and the training department in the organization on the results of the participant's performance and the return on training. Our programs focus on exercises, case studies, and individual and group presentations.
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