Deloitte Introduces AI-as-a-Service Solution
, one of the leading global consultancy firms, recently announced to the world. ReadyAI is a full portfolio of capabilities and services that companies can use to accelerate and scale their artificial intelligence (AI) integration projects. The system brings together skilled AI specialists and managed services in a flexible AI-as-a-service model as an all-in-one package solution to every organisation’s needs.
The Growing AI Market
According to Deloitte, the AI market is expected to exceed US$191bn by 2024, steadily rolling at a compound annual growth rate of 37 per cent. As organisations across the globe accelerate their adoption of AI, we’re likely to witness many struggling with the lack of specialised talent, slow development cycles, and the resources to continuously maintain new AI models.
To successfully implement and maintain AI systems, companies need human resources with expertise in data science, IT operations, and user experience who can group together to work towards a common goal ─ a hefty price tag for smaller companies with lesser resources at their disposal.
Deloitte’s AI Solution
By investing in Deloitte’s ReadyAI, organisations can now access the expertise, services, and technology that they need to accelerate their AI journey, without all the additional strain of recruitment and preparation.
“In the , human and machine collaboration is taking organizations to new heights. While AI adoption is accelerating, many organizations struggle to scale their AI projects. ReadyAI provides the flexible and scalable capabilities that these companies need to successfully become AI-fueled organisations.” ─ Nitin Mittal, AI co-leader and Principal, Deloitte Consulting LLP
According to Deloitte, ReadyAI offers comprehensive service capabilities including:
- Data preparation: Provide data extraction, wrangling and standardization services. Also supports advanced analytical model development through feature engineering.
- Insights and visualization: Design and generate reports and visual dashboards utilizing data output from automations to improve business outcomes and automation performance.
- Advanced analytics: Data analysis for both structured and unstructured data. Creation of rule-based bots and insights-as-a-service.
- Machine learning and deep learning: ML and deep learning model development. Video and text analytics to assist conversational AI.
- Machine learning deployment: Create deployment architecture and pipelines for upstream and downstream integration of ML models.
- Model management and MLOps: Management of model performance, migration and maintenance. Automation of model monitoring process and overall DevOps for machine learning.
“The promise of AI lies in its deployment at scale in a fair, ethical and trustworthy fashion. ReadyAI helps clients take AI all the way from labs and pilot programs to real-life business integration and adoption.” ─ Rohit Tandom, General Manager for Managing Director, ReadyAI and Deloitte Consulting LLP
In Deloitte’s most recent (third edition) study of enterprise AI adopters, the organisation found that less than half of AI adopters believe they have a high level of skill in their talent pool around integrating new AI technologies into their existing IT environment. Fortunately, Deloitte packs a pool with in excess of 3100 AI professionals, and “can assemble teams that have the right combination of industry, domain and AI technology skills to best suit clients’ needs. These experts include cloud engineers, data scientists, data architects, technology and application engineers, business and domain specialists, and visualization and design specialists. By leveraging the right combination of skills, organizations can quickly accelerate their AI journey.”
The advantages and disadvantages of AI in cloud computing
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
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.
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.
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.
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
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.
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.