Driving Outcomes in Artificial
Intelligence & Machine Learning

AI/ML

Overview

Firemind specialise in leveraging the AWS cloud framework to optimise and automate your machine learning and artificial intelligence capabilities.

Intelligent Cloud Solutions

Proactive Forecasting
Leverage data to build models that predict outcomes and suggest real-time solutions.
Machine Vision
Providing insights using imaging-based automatic inspection and analysis for industry-specific applications.
Language Processing (NLP)
Using text interpretation, translation, and sentiment analysis to drive better customer experience.
Human-Machine Interaction (HMI)
Analyse human-machine interfaces, like chatbots to optimise intuitive language, behaviours and gestures.

'The combination of experience and knowledge of the AWS enviroment and their vision and creativity.Also their speed of response and anility to identify elegant solutions from complex inputs'

Andrew Paddison M&C Saatchi Performance

'Having worked with Firemind over the past 18 months or so we have found them to be experts in AWS, they have helped us a great deal with some of our projects'

Tom Simpson Penna

AWS Machine Learning Services

Whether its starting from the ground up with Amazon SageMaker, or choosing a singular tool like Rekognition or Textract, Firemind has the expertise to guide your ML journey.

There are 3 main machine learning services layers. The first is AWS pre-built services for quick deployment. These are powerful and have limited customisation to your use case. The services can be combined together to create complex applications.

Build your own Alexa
Transcribe → Lex → Polly
Build a Jeff Bezos detector
DeepLens → Rekognition
Are people on the phone happy?
Transcribe → Comprehend

The second service layer is AWS Sagemaker, which is designed to handle the entire workflow. This is more powerful than prebuilt services with the option to develop custom algorithms or to leverage existing training sets and AWS algorithms.

Its modular architecture makes Sagemaker flexible to any use case, allowing you to utilise SageMaker independently for model building, training or deployment and to accelerate the development of the business solution.

Leveraging AWS can reduce your total cost of ownership and time to deployment. This is most obvious in smaller applications where Sagemaker can reduce the TCO by up to 90% with no loss of functionality or flexibility by using a fully managed service.

The third option is the AWS Deep Learning AMIs which provide the infrastructure and tools to accelerate deep learning in the cloud, at any scale, enabling complete customisation.

Using Deep Learning AMIs we can quickly launch Amazon EC2 instances pre-installed with popular deep learning frameworks and interfaces such as TensorFlow, PyTorch, Apache MXNet, Chainer, Gluon, Horovod, and Keras to train sophisticated, custom AI models. Whether your use case will benefit from Amazon EC2 GPU or CPU instances, there is no additional charge for the Deep Learning AMIs – you only pay for the AWS resources needed to store and run your applications.