Education & Training Services - Further your AI knowledge by signing up to our of our instructor-led courses

Education & Training Services

Further your AI knowledge by signing up to our of our instructor-led courses

As the UK’s leading AI provider, Scan AI is also certified to deliver vendor-certified education and training courses. We offer NVIDIA Deep Learning Institute (DLI) courses and NVIDIA Ideation Workshops, alongside a range of Software Webinars aimed at demonstrating the benefits of selected AI applications. Click the tabs below to explore each further.

NVIDIA Deep Learning Institute Logo

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.

schoolLearn

• Learn from technical industry experts and instructors

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

editor_choiceQualify

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

desktop_cloud_stackImplement

• 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

Rapid Application Development with Large Language Models

event Next Course Date: 12th February 2025
Previous Course Next Course BOOK THIS COURSE
Introduction (15 mins)
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

Fundamentals of Deep Learning

event Next Course Date: 5th March 2025
Previous Course Next Course BOOK THIS COURSE
Introduction (30 mins)
The Mechanics of Deep Learning (3 hours)

Explore the fundamental mechanics and tools involved in successfully training deep neural networks:

  • Train your first computer vision model to learn the process of training.
  • Introduce convolutional neural networks to improve accuracy of predictions in vision applications.
  • Apply data augmentation to enhance a dataset and improve model generalization.
Break 1 hour
Pre-trained Models and Large Language Models (1.5 hours)

Leverage pre-trained models to solve deep learning challenges quickly. Train recurrent neural networks on sequential data:

  • Integrate a pre-trained image classification model to create an automatic doggy door.
  • Leverage transfer learning to create a personalized doggy door that only lets in your dog.
  • Use a Large Language Model (LLM) to answer questions based on provided text.
Break 15 mins
Final Project: Object Classification (1 hour)

Apply computer vision to create a model that distinguishes between fresh and rotten fruit:

  • Create and train a model that interprets color images.
  • Build a data generator to make the most out of small datasets.
  • Improve training speed by combining transfer learning and feature extraction.
  • Discuss advanced neural network architectures and recent areas of research where students can further improve their skills.
Final Review (30 minutes)
  • 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.

Building Transformer-Based Natural Language Processing Applications

event Next Course Date: 24th April 2025
Previous Course Next Course BOOK THIS COURSE
Introduction (15 mins)
Introduction to Transformers (120 mins)

Explore how the Transformer architecture works in detail:

  • Build the Transformer architecture in PyTorch.
  • Calculate the self-attention matrix.
  • Translate English to German with a pre-trained Transformer model.
Break 60 mins
Self-Supervision, BERT, and Beyond (120 mins)

Learn how to apply self-supervised Transformer-based models to concrete NLP tasks using NVIDIA NeMo:

  • Build a text classification project to classify abstracts.
  • Build a NER project to identify disease names in text.
  • Improve project accuracy with domain-specific models.
Break 15 mins
Inference and Deployment for NLP (120 mins)

Learn how to deploy an NLP project for live inference on NVIDIA Triton:

  • Prepare the model for deployment.
  • Optimize the model with NVIDIA® TensorRT™.
  • Deploy the model and test it.
Final Review (15 mins)
  • Review key learnings and answer questions.
  • Complete the assessment and earn a certificate.
  • Take the workshop survey.
  • Learn how to set up your own environment and discuss additional resources and training.
Networking (30 mins)
  • Discuss your AI projects with the Scan AI data science team
  • Make a follow-up appointment

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

event Next Course Date: 5th June 2025
Previous Course Next Course BOOK THIS COURSE
Introduction (15 mins)
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

Building Transformer-Based Natural Language Processing Applications

event Next Course Date: 25th September 2025
Previous Course Next Course BOOK THIS COURSE
Introduction (15 mins)
Introduction to Transformers (120 mins)

Explore how the Transformer architecture works in detail:

  • Build the Transformer architecture in PyTorch.
  • Calculate the self-attention matrix.
  • Translate English to German with a pre-trained Transformer model.
Break 60 mins
Self-Supervision, BERT, and Beyond (120 mins)

Learn how to apply self-supervised Transformer-based models to concrete NLP tasks using NVIDIA NeMo:

  • Build a text classification project to classify abstracts.
  • Build a NER project to identify disease names in text.
  • Improve project accuracy with domain-specific models.
Break 15 mins
Inference and Deployment for NLP (120 mins)

Learn how to deploy an NLP project for live inference on NVIDIA Triton:

  • Prepare the model for deployment.
  • Optimize the model with NVIDIA® TensorRT™.
  • Deploy the model and test it.
Final Review (15 mins)
  • Review key learnings and answer questions.
  • Complete the assessment and earn a certificate.
  • Take the workshop survey.
  • Learn how to set up your own environment and discuss additional resources and training.
Networking (30 mins)
  • Discuss your AI projects with the Scan AI data science team
  • Make a follow-up appointment

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

event Next Course Date: 3rd October 2025
Previous Course Next Course BOOK THIS COURSE
Introduction (15 mins)
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

Fundamentals of Deep Learning

event Next Course Date: 6th November 2025
Previous Course Next Course BOOK THIS COURSE
Introduction (30 mins)
The Mechanics of Deep Learning (3 hours)

Explore the fundamental mechanics and tools involved in successfully training deep neural networks:

  • Train your first computer vision model to learn the process of training.
  • Introduce convolutional neural networks to improve accuracy of predictions in vision applications.
  • Apply data augmentation to enhance a dataset and improve model generalization.
Break 1 hour
Pre-trained Models and Large Language Models (1.5 hours)

Leverage pre-trained models to solve deep learning challenges quickly. Train recurrent neural networks on sequential data:

  • Integrate a pre-trained image classification model to create an automatic doggy door.
  • Leverage transfer learning to create a personalized doggy door that only lets in your dog.
  • Use a Large Language Model (LLM) to answer questions based on provided text.
Break 15 mins
Final Project: Object Classification (1 hour)

Apply computer vision to create a model that distinguishes between fresh and rotten fruit:

  • Create and train a model that interprets color images.
  • Build a data generator to make the most out of small datasets.
  • Improve training speed by combining transfer learning and feature extraction.
  • Discuss advanced neural network architectures and recent areas of research where students can further improve their skills.
Final Review (30 minutes)
  • 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.

Computer Vision for Industrial Inspection

event Next Course Date: 28th November 2025
Previous Course Next Course BOOK THIS COURSE
Introduction
Data Exploration and Pre-Processing with DALI

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
Efficient Model Training with TAO Toolkit

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

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

Fundamentals of Deep Learning

event Next Course Date: 12th December 2025
Previous Course Next Course BOOK THIS COURSE
Introduction (30 mins)
The Mechanics of Deep Learning (3 hours)

Explore the fundamental mechanics and tools involved in successfully training deep neural networks:

  • Train your first computer vision model to learn the process of training.
  • Introduce convolutional neural networks to improve accuracy of predictions in vision applications.
  • Apply data augmentation to enhance a dataset and improve model generalization.
Break 1 hour
Pre-trained Models and Large Language Models (1.5 hours)

Leverage pre-trained models to solve deep learning challenges quickly. Train recurrent neural networks on sequential data:

  • Integrate a pre-trained image classification model to create an automatic doggy door.
  • Leverage transfer learning to create a personalized doggy door that only lets in your dog.
  • Use a Large Language Model (LLM) to answer questions based on provided text.
Break 15 mins
Final Project: Object Classification (1 hour)

Apply computer vision to create a model that distinguishes between fresh and rotten fruit:

  • Create and train a model that interprets color images.
  • Build a data generator to make the most out of small datasets.
  • Improve training speed by combining transfer learning and feature extraction.
  • Discuss advanced neural network architectures and recent areas of research where students can further improve their skills.
Final Review (30 minutes)
  • 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.

Rapid Application Development with Large Language Models

event Next Course Date: 12th February 2026
Previous Course Next Course BOOK THIS COURSE
Introduction (15 mins)
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

Generative AI with Diffusion Models

event Next Course Date: 5th March 2026
Previous Course Next Course BOOK THIS COURSE
Introduction (15 mins)
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

Building Transformer-Based Natural Language Processing Applications

event Next Course Date: 23rd April 2026
Previous Course Next Course BOOK THIS COURSE
Introduction (15 mins)
Introduction to Transformers (120 mins)

Explore how the Transformer architecture works in detail:

  • Build the Transformer architecture in PyTorch.
  • Calculate the self-attention matrix.
  • Translate English to German with a pre-trained Transformer model.
Break 60 mins
Self-Supervision, BERT, and Beyond (120 mins)

Learn how to apply self-supervised Transformer-based models to concrete NLP tasks using NVIDIA NeMo:

  • Build a text classification project to classify abstracts.
  • Build a NER project to identify disease names in text.
  • Improve project accuracy with domain-specific models.
Break 15 mins
Inference and Deployment for NLP (120 mins)

Learn how to deploy an NLP project for live inference on NVIDIA Triton:

  • Prepare the model for deployment.
  • Optimize the model with NVIDIA® TensorRT™.
  • Deploy the model and test it.
Final Review (15 mins)
  • Review key learnings and answer questions.
  • Complete the assessment and earn a certificate.
  • Take the workshop survey.
  • Learn how to set up your own environment and discuss additional resources and training.
Networking (30 mins)
  • Discuss your AI projects with the Scan AI data science team
  • Make a follow-up appointment

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

event Next Course Date: 4th June 2026
Previous Course Next Course BOOK THIS COURSE
Introduction (15 mins)
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
nvidia software webinar

AI Software Webinars

Software applications are a critical part of any AI workflow, from libraries and frameworks in data preparation and model development through to GPU virtualisation during training and computer vision and orchestration. Our range of webinars aim to show you first-hand how AI-specific applications can revolutionise your productivity, visualisation or time to results.

desktop_windowsRun:ai

Run:ai enables you to maximise GPU utilisation by pooling disparate compute resource and enabling intelligent scheduling and allocation.

analyticsWeights & Biases

W&B helps manage your AI workflows end-to-end by quickly tracking experiments and iterations, evaluating model performance, reproducing models.

identity_platformUbiOps

The UbiOps platform helps teams to quickly run their AI workloads as reliable and secure micro-services, without upending their existing workflows.

task_altSupervisely

Supervisely helps you develop AI faster and better with on-premise, enterprise-grade solutions for every task - from labelling to building production models.

display_settingsYellowdog

Yellowdog provides a single interface to control any compute device - on-prem, hybrid or multi-cloud - supporting any operating system.

Run:ai

BOOK THIS WEBINAR
What is Run:ai? Run:ai software is a scheduling and orchestration platform, that creates virtual 'pools' of GPU resource, so they can be dynamically allocated as tasks require.
Why do I need Run:ai? Run:ai's platform revolutionises AI and machine learning operations by addressing key infrastructure challenges through dynamic GPU resource allocation, comprehensive AI lifecycle support, and strategic resource management. By pooling GPU resources across environments and utilising advanced orchestration and accelerators, Run:ai significantly enhances GPU efficiency and workload capacity. This results in significant increases in GPU availability, workloads, and GPU utilisation, all with zero manual resource intervention, accelerating innovation and providing a scalable, agile, and cost-effective solution for enterprises.
What will I learn on this webinar?

Our webinars are led by one of our in-house data scientists, who will show you how to:

Set up batch scheduling of your workloads

  • Reduce GPU idleness and increase cluster utilisation with job queueing and opportunistic batch job scheduling

Ensure equity amongst workgroups

  • Prevent resource contention with over quota priorities and automatic job preemption and fairshare resource allocation

Get the most from the user-friendly interface

  • Real-time and historical metrics by job, workload, and team in a single dashboard. Assign compute guarantees to critical workloads, promote oversubscription, and react to business needs easily.
nvidia scan ai workshop

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.

searchInvestigation

• 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

descriptionPlanning

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

• Create a roadmap to start using AI in your business

desktop_cloud_stackImplementation

• 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

Example AI Ideation Workshop Agenda

BOOK A WORKSHOP
09:00 - 09:15 Introductions
09:15 - 09:30 Workshop Overview - Confirmation of goals
09:30 - 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
Scan AI

Get in touch with our AI team.