May 24, 2021

Faculty raises £30m to deliver 'AI as a Service' globally

AI
Technology
Faculty
2 min
New funding raised by a British startup, Faculty, expected to create 400 new jobs and accelerate international expansion

Faculty, a British artificial intelligence (AI) company, today announced that it has raised £30m in growth funding from the Apax Digital Fund (ADF). It is the largest investment that the startup has accepted to date, and brings total investment to nearly £40m. 

The funding will be used to drive the expansion of Faculty’s 'AI as a Service' model, which can be applied to a broad range of problems for both public and private sector organisations. It enables customers to customise powerful AI solutions to their needs, with the ongoing training and support that guarantees safe and high-performance AI over the long term. 

The investment will support the next phase of the company’s growth over the coming years, with the company expecting to create over 400 new jobs across its engineering, product, and delivery teams. The investment will also be put towards the rollout of Faculty’s new learning and development programme.

The value of AI

“It’s an incredibly exciting time for artificial intelligence, and for Faculty in particular. Too many organisations haven’t been able to realise the value of AI, because they haven’t had the tools to integrate it successfully into their business. Customers are rightly demanding high performance technology to unlock the power of data and maximise impact. Faculty can help elevate an organisation’s performance, whether this enables better operational decisions, or increasing ROI. Apax’s expertise and global network means we will continue to grow at pace, bringing the power of AI to even more customers, helping them to make effective, robust decisions with real-world impact.” said Marc Warner, CEO & Co-Founder of Faculty.

Founded in 2014 by Dr Marc Warner, Dr Angie Ma and Andrew Brookes, the company now has a specialist team that includes over 50+ PhDs with experience of working with over 230 customers across the globe. In April this year, the company announced it had won a contract to work with the UK’s NHS to better predict its future needs. They won 52 new customers in the last financial year including the National Crime Agency, Red Bull, Virgin Media, Moonpig.

The Apax Digital Fund joins Faculty’s existing investors, including Guardian Media Group Ventures, LocalGlobe, and Jaan Tallinn, one of Skype’s founding engineers

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Jun 15, 2021

The advantages and disadvantages of AI in cloud computing

AI
CloudComputing
Data
ML
3 min
AI is being used in cloud computing, which works by allowing client devices to access data over the internet remotely, but are there pros and cons?

Cloud computing offers businesses more flexibility, agility, and cost savings by hosting data and applications in the cloud. AI capabilities are now combining with cloud computing and helping companies manage their data, look for patterns and insights in information, deliver customer experiences, and optimise workflows.

We take a look at some of the benefits and drawbacks of AI in cloud computing. 
 

The benefits of AI in cloud computing

 

Lower costs

A major advantage of cloud computing is that it eliminates costs related to on-site data centers, such as hardware and maintenance. Those upfront costs can be restrictive with AI projects, but with cloud enterprises you can access these tools for a monthly fee, making research and development related costs more manageable. AI tools can also gain insights from the data and analyse it without human intervention, reducing staff costs.

Deeper insights 

AI is able to identify patterns and trends in large data sets. Using historical data, AI compares it to the most recent data, which provides IT teams with well-informed, data-backed intelligence. AI tools can also perform data analysis fast so enterprises can rapidly and efficiently address customer queries and issues. The observations and valuable advice gained from AI capabilities result in quicker and more accurate results.

Improved data management

AI enables extensive data management, and cloud computing maximises information security, making it possible to deal with massive amounts of data in a programmed manner to analyse them properly, allowing the business to leverage information that has been “mined” and filtered to meet each need. AI can also be used to transfer data between on-premises and cloud environments. 
 

Intelligent automation 

Businesses use AI-driven cloud computing to be more efficient and insight-driven. AI can automate repetitive tasks to boost productivity, and also perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows. IT teams can focus more on strategic operations while AI performs the mundane tasks. 

Increased security 

With businesses deploying more applications in the cloud, security is crucial in order to keep data safe. IT teams can use different AI-powered network security tools which can track network traffic, they can flag issues, such as finding an anomaly. 
 

The drawbacks of AI in cloud computing

 

Data privacy 

 Enterprises need to create privacy policies and secure all data when using AI in cloud computing. AI applications require a large amount of data, which can include consumer and vendor information. While some data can be anonymous and can't be tied to personally identifiable information, knowing who the data belongs to makes it more valuable. When sensitive information is used, data protection and compliance is a major concern.

Connectivity concerns 

IT teams use the internet to send raw data to the cloud service and recover processed data. Poor internet access can hinder the advantages of cloud-based machine learning algorithms, as cloud-based machine learning systems need consistent internet connectivity. 

While processing data in the cloud is quicker than conventional computing, there is a time lag between transmitting data to the cloud and receiving responses. This is a significant issue when using machine learning algorithms for cloud servers, where prediction speed is one of the primary concerns.

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