Services

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In this course we take the attendees through an overview of AI. What it is, where it came from, how we got here, and where it's going. We cover the technology basics as well as how AI is being used by companies from start-ups to multinationals to develop new products and services and to improve on existing ones.

We examine data sets, hardware, algorithms and full-stack platforms, and then focus on business use cases and scaling to real world AI deployments in production environments - MLOps. This course is high level so is suitable for business executives and people curious as to what the new AI-first world looks like from a business perspective.

Overview of Generative AI, its history and development, current and future frameworks and use cases, along with unpacking the underlying technologies. We will take a look at various generative AI frameworks, including GPT3, ChatGPT, CLIP, VQ-GAN and Dall-E2, along with actual business applications. This is intended as an overview class for technologists and business leaders who wish to become more familiar with generative AI technologies and their potential for mass disruption. We will examine use cases along with the various types of generative AI including foundation models, LLM's and diffusion models. There will be opportunity for hands-on exploration including fine-tuning and prompt engineering.

By now you've heard and read about quantum computing, and are wondering if your business can use it to gain competitive advantage. In this half day workshop, you will learn about where exactly quantum computing is in terms of enterprise readiness and how businesses can harness what is available today.

We will look at various real-world use cases where companies are currently using quantum computing, including in the transportation, financial, energy, materials and pharmaceutical sectors. By the end of the session participants will have a clear understanding of what quantum computing is, where it is being applied today, as well as being provided with a quantum roadmap.

We examine data sets, hardware, algorithms and full-stack platforms, and then focus on business use cases and scaling to real world AI deployments in production environments. This course is high level so is suitable for business executives and people curious as to what the new AI-first world looks like from a business perspective.

Now on version 2.0, TensorFlow has become the most popular deep learning framework since Google open sourced it back in November 2015. Find out what it can do for your business in this overview of deep neural networks and the TensorFlow framework.

This is a business focussed introduction to TensorFlow which will include concepts, use cases and some code walkthroughs to demonstrate examples using real world data sets.

Since Google open-sourced their Deep Learning framework TensorFlow, it has become the most popular tool for Data Scientists and Machine Learning experts worldwide. In this course we will explore the various facets of TensorFlow – expect an 80% lab content with this course, both locally on your laptop, and in the cloud.

We will cover various business use cases with case studies and code walkthroughs using Colab notebooks. Some exposure to Python, TensorFlow and Jupyter notebooks is required. 

In this intensive five-day hand on TensorFlow & PyTorch course, we will explore how different businesses are using artificial intelligence to improve profits and productivity. We will explore, through theory and labs, how TensorFlow and PyTorch are being used to classify images, recognize text and speech, generate images and language and make predictions from data to help companies derive more efficient and effective business decisions.

We will examine business use cases using a variety of image, text, speech and video data sets. Hands on lab instruction will be done both locally and, in the cloud, using GPU's, TPU's and MLOps frameworks. There will be approximately a 70% lab element to this course.

A one day overview of the main concepts and technologies underlying the practice of data science with code walkthroughs. We will cover the basics from business cases to which technologies to use and when to use them and how businesses are leveraging them today to drive innovation and productivity.

From Kafka and Spark to machine learning and AI, see how the latest technologies are differentiating businesses from their competitors through the exploration of real-world business scenarios.

Consisting of an 80/20 split of labs and concepts, this five-day data science course provides a comprehensive exploration of all of the main aspects of data science applied to business. Going into much more depth than our one-day overview course in terms of both theory and practice, topics covered include strategy and planning, data ingestion and cleaning, data analysis using frameworks such as Spark, Kubernetes, TensorFlow and AutoML, and finally presentation and recommendations. Several real-world business use cases will be covered.

Prerequisites include hands on familiarity with Python, TensorFlow and Jupyter notebooks.

This three-day course will introduce participants to the major recent advancements in the field of reinforcement learning and how these developments can be applied in organizations to build intelligent systems and improve business processes. It will explore various real-world scenarios to implement some of the latest algorithms for building intelligent applications and services using the Dopamine framework.

As well as exploring the various RL frameworks and algorithms, participants will gain an understanding of what RL algorithms to use in a given business context. Domain examples will include self-driving cars, manufacturing (robotics), medicine and financial services. As this course comprises a 70% lab element, some exposure to Python and TensorFlow is desirable.

In this three-day hands-on course, we will explore Julia’s capabilities and see why it has fast become a favoured language for data science and machine learning aficionados along with Python and R. As well as getting to know the language and some of its main statistical libraries, we will apply it to analysing various business data sets to produce meaningful business outcomes and predictions.

We will particularly focus on the deep learning packages such as Flux.jl and TensorFlow. This course will consist of a 70/30 mix of practice and theory with Labs being performed using Jupyter notebooks. Some prior Julia programming experience is desirable.

Massive (e.g., petabyte scale) data sets require massively parallel processing in order to do timely analysis. GPU’s & TPU's are well suited for this task, and in these practical hands on course, we will learn how to program them to extract useful information.

We will configure GPU instances on the cloud and then use them to analyse various business data sets. We will also look at how TPU's are being used in the Google Cloud Platform. 80/20 practice/theory. Some exposure to Python and Jupiter notebooks desirable.

In this course with a 20/80 theory/practice split, we will look at the techniques and analysis of natural language processing (NLP), including speech and text recognition, generation and translation.

We will examine some of the popular language frameworks from Google, Facebook, OpenAI and the Allen Institute such as BERT, DeepText, GPT-2 and ELMO, respectively. We will look at how businesses use NLP to gain competitive advantage by considering several real-world use cases. The labs will be done using TensorFlow so previous exposure to Python is advisable.

We will configure GPU instances on the cloud and then use them to analyse various business data sets. We will also look at how TPU's are being used in the Google Cloud Platform. 80/20 practice/theory. Some exposure to Python and Jupiter notebooks desirable.

In this overview course, we will explore quantum computing including theory (quantum physics without a lot of maths), hardware, quantum algorithms and software frameworks, as well as looking at quantum computing services available on the market today.

We will explore the various hardware vendors' cloud service offerings including D-Wave, IBM and Rigetti. We will also look at some QC use cases within the domains of machine learning, chemistry and optimizations. Ideal for businesses who wish to become quantum ready. 

We will look at the theory behind quantum computing as well as the practical aspects of building and programming a quantum computer. After covering the various types of quantum computing hardware, we will look at the frameworks available today and how they are being used across different business domains.

There will be a hands-on element whereby we will use a quantum computer to program a selection of quantum algorithms using the Qiskit framework. Familiarity with the Linux command line as well as the Python programming language is required. There will be a roughly 30/70 split theory/hands on.

Neuromorphic computing is an emerging computing paradigm, which uses an analogue processor for spiking neural networks, much the way the brain does computations. Neuromorphic processors utilize massively parallel computations in their synaptic connections between the artificial neurons and run at very low power (1000x reduction in power consumption over CPU's, for example).

Despite sounding esoteric, this technology is seeing commercial application in companies and government organizations today. In this two-day overview course, we will examine the technology from theory to practice, survey the past and present research and product landscapes, take a look at the various product offerings available today and the differences between them, and cover some case studies to show how companies are already reaping performance benefits from this exciting new technology. Basic python programming is required.

Starting off with a comparison between Bayesian and classical statistics, this course introduces the participants to the basics of Bayesian statistics including Bayesian probability and inference. It will cover theory and practice with some hands-on labs in python so that the student can get experience with analysing actual data sets using Bayesian methods. 

12-week bootcamp exploring how businesses are using artificial intelligence for competitive advantage. There will be extensive labs each week in TensorFlow.

Week 1 - Basics of deep learning - math, data sets, hardware (CPU, GPU, TPU, MLOps, CloudAI), mathematical foundations of deep neural networks and foundation models. Frameworks including TensorFlow, Keras, ChatGPT and PyTorch.

Week 2 - Basic Convolutional Neural Networks with TensorFlow

Week 3 - Natural Language Processing, RNNs, LSTM's and Time Series Data with TensorFlow

Week 4 - Transformer models, LLMs, generative AI, and foundation models

​Week 5 - Further generative AI including ChatGPT, Dall-E2

Week 6 - Deep Reinforcement Learning overview with TensorFlow and Dopamine

Week 7 - Further Deep RL algorithms plus advanced generative AI use cases

Week 8 - Deploying your AI solution to production - MLOps

Weeks 9-12 - Capstone Project - Students work with a company on a commercial deep learning project with the view to getting hired upon completion.

​Prerequisites: Math (linear algebra, calculus and probability), programming skills in python, comfortable at the command line. Some previous exposure to TensorFlow desirable.

8-week bootcamp exploring how businesses can use quantum computing for competitive advantage. There will be extensive labs each week in Qiskit. Get in touch for further details and availability.