Despite the fact that 90% of businesses have adopted cloud technology, only one third is achieving the ROI they were expecting.  Companies with advanced technologies know that cloud computing offers a powerful, scalable computing environment and gives access to data in the right quantity and quality, but AI enables that data to be turned into business value.  All levels of the executive suite are now involved in the AI agenda, and they want to know what the next step is.

Applied Intelligence can help in this situation. With cloud, data, and AI, we believe that the market will be enabled, driven, and differentiated. By bringing them together, we enable smarter, faster decisions that help grow your organization.  Moreover, because our experts understand that people are the most important component of any technology transformation, we bring the cross-functional expertise to both deliver business outcomes and facilitate cultural change – enabling your workforce to properly use data and AI Services.

Artificial intelligence has been prohibitively expensive for most companies for a long time:

  1. It was an expensive and massive machine.
  2. As a result, there were a limited number of programmers available (hence the high salaries they demanded).
  3. Several companies did not have enough data to study.

As cloud computing has become more accessible, AI has become more accessible as well: companies can now gather and store infinite amounts of data . Artificial intelligence as a service makes this possible.

Types of AI Service 

  • Computing cognitively

There are many cognitive computing APIs, including speech recognition, text analytics, and voice translation. Developers can access these services via REST endpoints and integrate them with applications using a single API call.

  • Customized computing

Despite the usefulness of APIs in generic scenarios, cloud providers are moving toward custom computing, which lets users experience cognitive computing using customized datasets. Here, users train cognitive services using their own data. Selecting the right algorithms and training the custom models becomes much easier with the custom approach.

  • AI conversational

 As end-users continue to accept artificial intelligence, the world is becoming increasingly familiar with virtual assistants. By leveraging bot services, cloud providers enable developers to make bots (voice, text) available across multiple platforms. Developers of web and mobile applications can integrate digital assistants into their applications by using this service.


AI tools 

In addition to APIs and infrastructure, cloud vendors provide tools to help data scientists and developers. These tools promote the use of virtual machines, storage, and databases since they are in sync with data platforms and compute platforms.

  • Wizards: 

To make ML training easier for amateur data scientists, wizards are provided. This set of tools, when combined, acts as a multi-tenant development environment.

  • Integrated development environment (IDE)

Cloud vendors with experience in machine learning are investing heavily in IDEs and notebooks (browser based) to facilitate testing and management of ML models. These tools enable developers and data scientists to create smart applications with ease.

  • Data preparation tools

A ML model’s performance is heavily influenced by the quality of the data. Public cloud vendors offer extract, transform, load (ETL) tools that can be used to prepare data for ML models to ensure the highest level of efficiency. After these ETL jobs are completed, they are fed into the machine learning pipeline for training and evaluation.

  • Frameworks

 As setting up, installing, and configuring the required data-science environment has become increasingly complicated, cloud providers offer ready-to-use VM templates with frameworks like TensorFlow, Apache MXNet, and Torch. 

The GPU-supported nature of these VMs allows them to train complex neural networks and machine learning models. Since public cloud providers are trying to attract more customers to their platforms, they are adopting AI on a large scale. Even though AIaaS is still in its infancy, it has the potential to change the way data and compute services are offered in the coming years

What is the process of artificial intelligence?

Algorithms are at the heart of most AI systems. The term algorithm refers to a set of rules or a process used by a computer for calculating or solving problems. A computer solves a particular task by:

  • Analyzing huge amounts of data.
  • Statistical estimations or generalizations.

Growth of AIaaS

AIaaS is the solution for companies who are unable or unwilling to build their own clouds or to build, test, and use their own artificial intelligence systems. Taking advantage of data insights without needing to invest vast amounts of talent and resources up front is the biggest draw.

AIaaS offers the same benefits as other “as a service” options:

  • Concentrating on core business (rather than becoming a data and machine learning expert)

  • Investing with minimal risk

  • Getting more value out of your data

  • Flexibility in strategic planning

  • Transparency and flexibility of costs

Artificial intelligence impact on business

A business that deploys the right AI technology may be able to:

  • Automating and optimizing routine processes and tasks can save you time and money
  • Enhance productivity and operational efficiency
  • Using cognitive technologies, accelerate business decision-making
  • If AI systems are properly configured, they can avoid human errors and mistakes
  • Understanding customer preferences can help you provide them with a better, more personalized service
  • You can generate quality leads and grow your customer base by mining vast amounts of data
  • Identify and maximize sales opportunities to increase revenue
  • Enable analysis and provide intelligent advice and support to grow expertise

You gain the following:

  • Discover and identify the organization’s competitive advantage using artificial intelligence 
  • Analyze the company’s readiness for AI adoption
  • Prepare a list of AI transformation goals that are aligned with your business objectives 
  • Using the proposed solution vision, choose the right combination of AI technology
  • By increasing awareness across the company, you can accelerate AI transformation


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