How Cloud-based Machine Learning Solutions Amplify Business Growth

Sanam Malhotra | 10th January 2020

Artificial Intelligence (AI) has proliferated business infrastructures both on-premise and in the cloud. Extreme cloud computing services have empowered emerging and established businesses to practice cloud-based machine learning techniques proficiently. At Oodles, we are exploring innovative ways to integrate our AI development services in cloud setups to assist business automation significantly.

Let’s discuss how cloud-based machine learning development services accelerate business applications along with a functional predictive model built by Oodles AI.


Significance of Cloud-based Machine Learning Applications

Cloud computing and machine learning are the major driving factors of Industry 4.0. High computational powers and machine learning algorithms together are transforming business intelligence, IT security, and financial trading across global industries. Here are the key benefits of hosting machine learning models in cloud-based infrastructures-

1) High-Performance Infrastructure

The struggle of procuring high-capacity hardware and servers is one of the major causes of low business performance. Cloud-based infrastructures provide several data accessing tools using SQL interfaces and boost the data processing capabilities of businesses. Businesses can choose private, public, or hybrid cloud storage options to store, process, and scale databases and train machine learning models effectively.machine learning in the cloud process

Essential steps involved in machine learning deployment as explained by Cloudtp


2) Cost efficiency

The ability to run and maintain huge databases comes with a heavy tag. With cloud-based virtual machines (VMs), businesses can avail of high computational services with minimal expenses and resources. It enables businesses to market their ML models quickly and efficiently without paying for enormous data centers, servers, and other equipment.

3) Scalability

Businesses are experiencing an explosion of data from terabytes to zettabytes including big data, social media activities, IoT data, and real-time online records. Cloud services enable businesses to resize their application bandwidth and manage the inflow of data streams at scale. Cloud-based vertical and horizontal scalability boosts business performances, handles heavy web traffic, and increases the response time significantly across digital channels.

Also read- Mobilizing Big Data for Cloud-based Predictive Analytics


Building Efficient Predictive Models with Cloud-based Machine Learning

Data is the most valuable asset for any organization. It is with data that businesses are able to run an in-depth analysis of patterns, historical events, and audience inputs. However, traditional analytics techniques are limited in scope and bandwidth to reach the full potential of big data.

Cloud-based machine learning has opened new business opportunities with comprehensive data lake architecture to predict target values.

cloud-based machine learning predictive analysis

Cloudtp explains how cloud data lake implements predictive Analytics and machine learning.


Cloud-based machine learning technologies enable businesses to target prospects, support customers, build advanced products, and respond to market needs effectively.  Here’s how cloud computing enhances machine learning capabilities of businesses-

a) A cloud-based data lake can be easily integrated with other systems to ensure seamless streaming, pattern matching, running Extract-Transform-Load (ETL) engines, and more.

b) Cloud features such as elasticity, automated recovery, and multi-zone accessibility optimize predictive analytics outcomes to be more accurate and highly contextual.

c) Google Cloud and AWS provide a rich set of trained models to deploy complex machine learning models with ease. Google Prediction APIs quickly identify patterns in large data sets to make optimum predictions and business-oriented recommendations.

Also read- Deploying Machine Learning for Android Applications Development


How does Oodles AI use cloud-based machine learning platforms for predictive analytics?

We, at Oodles, have experiential knowledge in deploying cloud-based machine learning models to fulfill the artificial intelligence requirements of global businesses. Our most recent machine learning application in the cloud is built on Amazon’s Sagemaker platform. The model training is done with raw CSV data from sensor-based drilling machines and labeled using function-specific ML algorithms. This model’s purpose is to extract actionable insights and make predictions regarding the presence of certain resources under the earth’s surface.

Here’s the infrastructure architecture of the model-

predictive analytics cloud-based machine learning application

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

Besides predictive analytics, our cloud-based machine learning services extend to Google Cloud, IBM Watson, Azure, and AWS Cloud Consulting Services. Our AI team is adept at integrating computer vision, natural language processing, conversational AI, and other machine learning techniques in the cloud.

Consult our AI development team to learn more about our artificial intelligence services.

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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