Cloud-Based IoT Analytics and Machine Learning
                     Cloud-Based IoT Analytics and Machine 
Learning
Satya K Vivek
Writes for Product engineering services and 
Gadgeon-IoT software development company.
Among the IT developments that have made it to the forefront in recent 
times, machine learning and IoT certainly stand out. As with most such 
technologies, integrating the two can help develop powerful IoT solutions 
and tackle complex challenges. More specifically speaking, machine 
learning can be leveraged in cloud based IoT analytics. 
3 reasons why cloud-based analytics and machine learning are relevant 
for IoT solutions
Here’s how machine learning and cloud-based analytics helps develop and 
implement successful IoT solutions
Real-time insights
Cloud-based analytics provide IoT solutions with real-time or near real-
time insights by processing large volumes of data. These insights drive 
data -driven decision culture that eventually help driving and optimizing 
various business transactions and operations. Machine learning has a role 
to play too – analyzing the trends and patterns leading to identification of 
anomalies and events. 
Predictive analytics
In today’s competitive business paradigm, it’s all about being proactive 
and staying ahead of the curve. One of the key applications of machine 
learning is predictive analytics, i.e., using the gathered data to predict 
future trends or events. 
Automated decision-making
Lastly, machine learning and cloud based IoT analytics make a powerful 
combination for automated decision-making. This can help streamline and 
simplify the workflow of the IoT solution for users and hence boosts 
productivity and efficiency.
Essential cloud-based analytics and machine learning tools to use in IoT 
solutions
The wide range of tools for machine learning and cloud-based analytics 
can honestly be a little overwhelming to choose from. However, here are 
3 essential ones that you must implement in your IoT solutions. 
Data lakes
The term is self-explanatory, but to put it simply, data lakes are central 
repositories designed to store both structured and unstructured data at 
varying scales. Organizations that have implemented data lakes have 
significantly outperformed by 9% in organic revenue growth in 
comparison with similar companies.
Streaming analytics
Contrary to the traditional method of processing data in batches, 
streaming analytics involves the continuous processing and analysis of 
data records. By implementing streaming analytics, you could prevent 
fraud, better plan maintenance of your critical assets, and get a 
comprehensive understanding of your customers.
Machine learning platforms
You would need relevant tools to develop and deploy machine-learning in 
an IoT solution. Thankfully, there are comprehensive machine learning 
platforms designed exactly for these purposes. These platforms speed up 
data processing, automate data workflows, and automate related 
functions. 
Best cloud analytics and machine learning practices for IoT solutions
Here are some of the best practices for the integration of machine 
learning and cloud analytics in an IoT solution.
Data preprocessing
Process the raw data and prepare it for another processing module or 
procedure. Though it was important mostly in data mining, data 
preprocessing has now become crucial in machine learning too.
Model selection
Though you may have several machine learning models available for a 
training dataset, you can only pick one. Choose the model best suited for 
the purpose, and make sure it meets the stakeholders’ requirements and 
constraints. 
Deployment strategies 
Different ML deployment strategies like Big Bang, blue/green, rolling 
updates, A/B testing, shadow, and canary deployment all have their 
respective applications. You’d have to choose the right deployment 
strategy based on your goal and requirements
Integrating cloud-based analytics is worthwhile
Integrating cloud based IoT analytics with machine learning might be 
complicated, but it’s most beneficial. An IoT solution powered by cloud 
analytics and machine learning boasts limitless possibilities and can prove 
to be a massive success. 
About Gadgeon
Gadgeon is known for its expertise in Industrial IoT and engineering 
excellence. We connect devices, operations, and processes to create 
business value, and revolutionize enterprises with the power of data. As 
an end-to-end technology services company, we successfully enabled the 
digital journey of customers with critical digital services ranging from 
embedded systems, cloud app development, mobile app development, 
data & analytics, application modernization, emerging technology based 
solutions, and testing & test automation across the industries such as 
connected factory, telecom & datacom, digital healthcare, CSPs, and 
home & building automation.
Thank you for time in reading this article!