The future of artificial intelligence will foster revolutionary and cost-effective solutions using machine learning, IoT, and cloud computing technologies. For this, data analysts are relying on the high computational powers of cloud-based infrastructures for optimum results. Cloud-based predictive analytics is the latest development under AI that is poised to enhance digital experiences with automated data analytics capabilities. In this blog post, we explore how cloud computing combined with AI development services is deriving cognitive insights for global businesses.
Strengthening AI-backed Predictive Analytics with Cloud Computing
While cloud computing empowers businesses with massive data storage, AI incubates this data to extract valuable insights beyond human intelligence. The fusion of AI and cloud computing will enable businesses to build scalable machine learning models while saving high operational costs.
Especially, the integration of cloud computing in AI-powered predictive analytics is poised to transform how businesses formulate their future strategies. Besides high-volume data processing and significant cost-cutting capabilities of cloud platforms, the following are the advantages of blending AI and cloud-
1) Elasticity
Cloud computing adds a layer of elasticity to the business applications hosted inside the cloud environment. Businesses can easily resize the application bandwidth over the cloud according to their real-time customer movements without procuring additional hardware.
2) Scalability and Global Accessibility
Traditional data analytics techniques are not efficient at handling large data surges. Contrary to it, cloud-based predictive analytics accelerate business productivity with highly scalable data processing environments across boundaries.
Business Applications Of Cloud-based Predictive Analytics
1) Data Preparation and In-depth Insights
There’s no dearth to the insights that businesses can derive from the ever-expanding volumes of audience data. However, the limited capacities of traditional analytics techniques prevent businesses from harnessing the full potential of large datasets. It directly affects the customer outreach efforts of businesses by mismatching customer needs and available services and ruining the end-user experience.
The underlying machine learning algorithms of predictive analytics channelize this explosion of data to purposeful use cases. Flexible storage and computational powers of cloud platforms enable ML models-
a) To extract valuable insight from complex datasets with speed and accuracy.
b) To easily merge with a cloud environment and deploy algorithms over the available data layers for labeling, clustering, filtering, etc.
c) To trace structured and unstructured audience data and contribute to business marketing strategies.
How does Oodles AI deploy ML models over cloud infrastructures?
The AI team at Oodles is working on a machine learning model over Amazon’s Sagemaker (a cloud service). The model is trained with raw CSV data that is labeled using function-specific AI algorithms. The primary objective of this ML model is to extract meaningful insights and predictions about potential resources under the earth’s surface. For this, we use sensor-based drilling machine data to train the model.
