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Education & Training Services

Further your AI knowledge by signing up to our of our Deep Learning Institute courses


As the UK’s leading NVIDIA Elite Partner, Scan AI is also certified to deliver NVIDIA-certified education and training courses. The two courses are instructor-led Deep Learning Institute (DLI) courses or Ideation Workshops. Click the tabs below to explore each further.


• Involve all stakeholders to establish where you are in your AI journey, what are your goals and use cases

• Explore how to accelerate the business, reduce time to insight, and achieve ROI


• Map your goals to a schedule of activities and set priorities

• Create a roadmap to start using AI in your business


• Run pilot projects to gain momentum

• How to build an in-house AI team and provide in-house AI training

• Managing internal and external communications

What is an NVIDIA AI Ideation Workshop?

In collaboration with NVIDIA, the Scan AI team is able to provide an Ideation virtual workshop for your organisation. In this workshop, you will be able to evaluate your existing AI projects and wider strategy, or use the day to formulate an AI plan from scratch. This will be done in collaboration with experts in AI and deep learning practices from both Scan AI and NVIDIA. Following the workshop you will receive a written report with recommendations and guidance as how to implement your plans.

Your workshop will be completely subsidised by Scan and NVIDIA - with no charges and no obligation to purchase anything. Our goal is to promote the wider use of AI technology and show you the possibilities within your industry vertical.

An Example AI Ideation Workshop Agenda

9:00-9:15 Introductions
9:15-9:30 Workshop Overview - Confirmation of goals
9:30-10:00 AI/Data Science Current State - what has been done, what worked, what didn't.
10:00-10:30 AI/Data Science Future State - 1, 3 and 5 year desired state
10:30-10:45 Break
10:45-12:30 Use Case exploration - most applicable with ROI
12:30-13:00 Lunch
13:00-14:00 Data Exploration - What data sources are available, how ready for AI?
14:00-15:00 Architecture Exploration - what is current and planned architecture for AI?
15:00-15:15 Break
15:15-16:00 Summary and Initial feedback, indication of AI readiness scale 1-10


• Learn from technical industry experts and instructors

• Gain hands-on experience with the most widely used, industry-standard software, tools, and frameworks


• Earn an NVIDIA DLI certificate in select courses to demonstrate subject matter competency and support professional career growth


• Access GPU-accelerated servers in the cloud to complete hands-on exercises

• Build production-quality solutions with the same DLI base environment containers used in the courses, available from the NVIDIA NGC catalogue

NVIDIA Deep Learning Institute

The NVIDIA DLI offers hands-on training for developers, data scientists, and researchers looking to solve challenging problems with deep learning and accelerated computing. Our DLI courses are delivered by qualified instructors who are in the perfect position to pass on their knowledge and educate developers on how to get the most from this rapidly evolving field. The DLI also teaches you how to optimise your code for performance using NVIDIA, CUDA and OpenACC.

Explore Our Upcoming Courses

Computer Vision for Industrial Inspection

Introduction (15 mins) • Meet the instructor
• Create an account at
Data Exploration and Pre-Processing with DALI (120 mins) Learn how to extract valuable insights from a data set and pre-process image data for deep learning model consumption.

• Explore data set with Pandas.
• Pre-process data with DALI.
• Assess scope for feasibility testing.
Lunch (60 mins)
Learn how to efficiently train a classification model for the purpose
of defect detection using transfer learning techniques
Learn how to efficiently train a classification model for the purpose of defect detection using transfer learning techniques.

• Train a deep learning model with TAO Toolkit.
• Evaluate the accuracy of the model.
• Iterate model training to improve accuracy.
Break (15 mins)
Model Deployment for Inference (120 mins) Learn how to deploy and measure the performance of a deep learning model.
• Optimize deep learning models with TensorRT.
• Deploy model with Triton Inference Server.
• Explore and assess the impact of various inference configurations.
Assessment and Q&A (15 mins)
Networking (30 mins) • Discuss your AI projects with the Scan AI data science team
• Make a follow-up appointment


ex VAT per person

Next Course Date - 25th September 2024


Data Parallelism: How to Train Deep Learning Models on Multiple GPUs

Introduction (15 mins) • Meet the instructor
• Create an account at COURSES.NVIDIA.COM/JOIN
Stochastic Gradient Descent and the Effects of Batch Size (120 mins) • Learn the significance of stochastic gradient descent when training on multiple GPUs.
• Understand the issues with sequential single-thread data processing and the theory behind speeding up applications with parallel processing.
• Understand loss function, gradient descent, and stochastic gradient descent (SGD).
• Understand the effect of batch size on accuracy and training time with an eye towards its use on multi-GPU systems.
Break (60 mins)
Training on Multiple GPUs with PyTorch Distributed Data Parallel (DDP) (120 mins) • Learn to convert single GPU training to multiple GPUs using PyTorch Distributed Data Parallel.
• Understand how DDP coordinates training among multiple GPUs.
• Refactor single-GPU training programs to run on multiple GPUs with DDP.
Break (15 mins)
Maintaining Model Accuracy when Scaling to Multiple GPUs (90 mins) • Understand and apply key algorithmic considerations to retain accuracy when training on multiple GPUs.
• Understand what might cause accuracy to decrease when parallelizing training on multiple GPUs.
• Learn and understand techniques for maintaining accuracy when scaling training to multiple GPUs.
Workshop Assessment (30 mins) • Use what you have learned during the workshop: complete the workshop assessment to earn a certificate of competency.
Final Review (15 mins) • Review key learnings and wrap up questions.
• Take the workshop survey.
Networking (30 mins) • Discuss your AI projects with the Scan AI data science team
• Make a follow-up appointment


ex VAT per person

Next Course Date - 28th November 2024


Generative AI with Diffusion Models

Introduction (15 mins) • Meet the instructor.
• Create an account at
From U-Nets to Diffusion (60 mins) • Build a U-Net, a type of autoencoder for images.
• Learn about transposed convolution to increase the size of an image.
• Learn about non-sequential neural networks and residual connections.
• Experiment with feeding noise through the U-Net to generate new images.
Break (10 mins)
Control with Context (60 mins) • Learn how to alter the output of the diffusion process by adding context embeddings.
• Add additional model optimizations such as Sinusoidal Position Embeddings, The GELU activation function, Attention.
Text-to-Image with CLIP (60 mins) • Walk through the CLIP architecture to learn how it associates image embeddings with text embeddings.
• Use CLIP to train a text-to-image diffusion model.
Break (60 mins)
State-of-the-art Models (60 mins) • Review various state-of-the-art generative ai models and connect them to the concepts learned in class.
• Discuss prompt engineering and how to better influence the output of generative AI models.
• Learn about content authenticity and how to build trustworthy models.
Final Review (60 mins) • Review key learnings and answer questions.
• Complete the assessment and earn a certificate.
• Complete the workshop survey.
• Learn how to set up your own AI application development environment.
Networking (30 mins) • Discuss your AI projects with the Scan AI data science team
• Make a follow-up appointment


ex VAT per person

Next Course Date - 12th December 2024


Rapid Application Development with Large Language Models

Introduction (15 mins) • Meet the instructor.
• Create an account at
From Deep Learning to Large Language Models (75 mins) • Learn how large language models are structured and how to use them.
• Review deep learning- and class-based reasoning, and see how language modeling falls out of it.
• Discuss transformer architectures, interfaces, and intuitions, as well as how they scale up and alter to make state-of-the-art LLM solutions.
Break (15 mins)
Specialized Encoder Models (45 mins) • Learn how to look at the different task specifications.
• Explore cutting-edge HuggingFace encoder models.
• Use already-tuned models for interesting tasks such as token classification, sequence classification, range prediction, and zero-shot classification.
Break (60 mins)
Encoder-Decoder Models for Seq2Seq (75 mins) • Learn about forecasting LLMs for predicting unbounded sequences.
• Introduce a decoder component for autoregressive text generation.
• Discuss cross-attention for sequence-as-context formulations.
• Discuss general approaches for multi-task, zero-shot reasoning.
• Introduce multimodal formulation for sequences, and explore some examples.
Decoder Models for Text Generation (45 mins) • Learn about decoder-only GPT-style models and how they can be specified and used.
• Explore when decoder-only is good, and talk about issues with the formation.
• Discuss model size, special deployment techniques, and considerations.
• Pull in some large text-generation models, and see how they work.
Break (15 mins)
Stateful LLMs (60 mins) • Learn how to elevate language models above stochastic parrots via context injection.
• Show off modern LLM composition techniques for history and state management.
• Discuss retrieval-augmented generation (RAG) for external environment access.
Assessment and Q&A (60 mins) • Review key learnings.
• Take a code-based assessment to earn a certificate.
Networking (30 mins) • Discuss your AI projects with the Scan AI data science team
• Make a follow-up appointment


ex VAT per person

Next Course Date - 13th February 2025