What methods should I use Deep learning, Machine Learning?
Should you use a commercial software solution or write your own algorithms?
How can you scale your solution?
Do you have the right skills to manage the solution?
Should you deploy as SaaS, in the cloud, on premise, hybrid solution?
What is the right infrastructure to deploy on?
What data sources should you use?
Is your data ready for AI?
Are your getting the right results?
How accurate will your results be?
How can you scale your model?
Understand the purpose
of the AI initiative &
Identify appropriate data sources and data
Identify what is
within time and budget
Understand what is the current technical environment and skill position is to build a roadmap to bridge the gap
Identify what algorithms
or Software solutions
can be applied to meet
the objects of the initiative
Design Infrastructure requirements
Working and partnering
with multiple stakeholder
within the project
Define, execute and manage project plan
There are multiple POC’s, benchmarking, raining and modelling tests that are often run throughout your AI Journey. Often at the start of an AI initiative the focus is all dedicated to building the algorithms and solution testing. Then the focus moves to results testing and improvement roadmaps, often with Infrastructure left as an afterthought. Most AI initiatives that are in POC or production mode have at some point hit “the infrastructure wall” – meaning that their current compute power can no longer meet the demands of their solution.
That’s why our AI and infrastructure specialist has access to the necessary range of powerful compute power and work together to build and test the optimal configurations needed to run successful POC’s and production environments.
AI workloads, and especially deep learning systems, which process large amounts of data, are extremely demanding and require powerful parallel processing capabilities. Furthermore, its important to look at all of the contributing factors, such as how is your data stored and accessed as this can have a dramatic effect on latency speeds, performances, speeds and training.
Speed is the key when looking at IA, the quicker the application can be deployed the quicker it can realise results or be commercialised. A company investing into a complete infrastructure based on CPU, GPU, Storage, Interconnects, and a central analytical hub will get a far higher ROI and quicker deployment schedules.
Well-integrated and scalable implementation can dramatically improve the performance and quality of results.
Join us to find out more about how AI is a game changer for financial service industry, how AI can fight Financial Crime and why Infrastructure really does matter for AI.
In London on 20th June from 8.30am to 1.30pm
Join us to find out more about how our collaboration of partnerships can provide the skill set required to get the big idea into a commercialised viable business along with how AI projects should be planned and deployed and why Infrastructure really does matter for AI.
In London on 20th June from 3.30pm to 8.30pm
So whether your POC or production environment has hit “the infrastructure wall” and you need access to more computing power or you are about to start a new AI initiative but don’t know where to begin, our team of specialist can help.
Contact us to to speak to one of our specialist.
Telephone: +44 (0) 1695-712664
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