Mobilizing Big Data for Cloud-based Predictive Analytics

Sanam Malhotra | 27th November 2019

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.

Also Read- Artificial Intelligence in CRM | Analyzing Business Usecases and Benefits

 

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.

Here’s a simplified version of the workings of this model-

 

 

cloud-based predictive analytics ai

The high-volume data processing and scaling capabilities of Amazon Sagemaker enable us to generate near accurate predictions efficiently.

 

2) Recommendations and Interactive Experience

Content recommendations not only enhance customer engagement and experience but are also effective at increasing customer loyalty. The ability of recommendation systems to procure personalized content depends on the audience’s data used to train the ML model.

Complex data inputs such as users’ screen time, likes and dislikes, activity periods, etc. enable businesses to segment audiences with definite characteristics. To compute recommendations-

a) ML algorithms are trained to distinguish between explicit and implicit user behaviors to map preferences.

b) ML algorithms use real-time customer interactions and search queries within business applications to train cloud-based predictive models effectively. 

 

Optimizing Cloud-based Predictive Analytics with Oodles AI

We, at Oodles, use big data platforms such as Apache Mahout to process source log data stored in a cloud or web store. By using a Hadoop cluster, we can process large datasets cleanse and label data, and push it further to ML models. We have hands-on experience with cloud-based predictive analytics for extracting valuable and business-oriented insights to enhance customer experience significantly.

Talk to our AI development team to know more about our Cloud-based Predictive Analytics Development.

About Author

Sanam Malhotra

Sanam is a technical writer at Oodles who is currently covering Artificial Intelligence and its underlying disruptive technologies. Fascinated by the transformative potential of AI, Sanam explores how global businesses can harness AI-powered growth. Her writings aim at contributing the multidimensional values of AI, IoT, and machine learning to the digital landscape.

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