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Mastering Cloud: Leveraging Cloud-Native ML Platforms

by - ThoughtFocus | September 27, 2023 |

Cloud-native ML platforms are gaining industry significance as they leverage cloud power to offer benefits like overcoming computing power, storage, and data accessibility limitations.  


AI, ML and Cloud Native technology complement each other, offering elasticity, scalability, and performance benefits while AI optimizes architecture maintenance. Choosing the right cloud-native architecture based on the business goals and needs, while being diligent and fully aware of what cloud native applications has to offer is highly vital for any IT businesses’ success.  


Cloud-Native Benefits  

Cloud-native architecture is crucial for IT businesses as it focuses on designing applications for cloud environments, rather than traditional on-premises infrastructure. While cloud native applications are scalable, flexible, and resilient, consisting of microservices that can be quickly updated. The principles guiding cloud-native architecture include automation, Continuous Integration/Continuous Delivery (CI/CD) tools, and breaking functionalities into smaller microservices. 


While cloud-native ML platforms automate machine learning processes, reducing human resource burden and allowing stakeholders to access insights without extensive coding experience. AI platforms leverage the cloud’s scalable environment to deploy AI models and deep learning algorithms, benefiting businesses from AI micro-services and data lakes for comprehensive training. By adopting these platforms, organizations can revolutionize machine learning, reduce feedback loop times, and gain a competitive edge. 


Gartner says, by 2025, 85% of organizations will be “cloud first.” And Nearly 50% of organizations are cloud-native or fully cloud-enabled. 


Business and IT teams to ensure the success of a cloud-native ML platform implementation, should consider the following best practices: 

Implementing a cloud-native machine learning (ML) platform requires clear objectives, robust data strategy, scalable and elastic infrastructure, data preprocessing and feature engineering, model development and deployment, continuous integration, and deployment (CI/CD), monitoring and observability, model versioning and management, collaboration and knowledge sharing, security and privacy measures, and continuous learning and improvement. 


Define clear objectives and goals for the platform, including specific problems, expected outcomes, and business needs. Further, develop a robust data strategy and governance framework to ensure data quality, data security, and compliance.  


Choose a cloud provider with flexible resources, auto-scaling capabilities, and efficient storage options. Incorporate feature engineering techniques to extract meaningful features from raw data, which influences the accuracy and performance of ML models. 


Utilize cloud-native ML frameworks and tools for model development and deployment and use containerization technologies like Docker and Kubernetes to package and deploy ML models consistently across different environments. Remember, monitor and observability mechanisms are essential to track the performance of ML models and identify potential issues.  


Establish a model versioning and management system to track different iterations, facilitate model comparisons, and simplify rollbacks. Foster a culture of continuous learning and improvement by monitoring model performance, gathering feedback, and incorporating user insights.


Okay, now that you have the hang of it and you picked a cloud-native approach to data and ML platforms, what’s next? Firstly, congratulations on picking cloud-native – a first step in the right direction. Your company and team will greatly benefit from this approach. 


Wondering where and how to get started and incase, if you opt for a cloud-native approach to data and ML platforms – Here’s what we recommend doing: 

To improve data management, engage with a business stakeholder who is committed to gaining more from data. Ask a valuable question about a dataset or domain and establish a cross-functional team. If you have a platform team, support them in their journey with cloud platform systems. 


Further, access the cloud and source data, and help them focus on answering the first question within a month. Determine the next three questions about the source data system, with similar impacts and value.  


Demonstrate the value of these questions and set a deadline while advocating for mature functionality and focus on delivering self-service for the source data system. Scalability is key, and platform capabilities will enable quality and self-service. Expand teams and establish a dedicated platform for broader, repetitive consumption.  


This would be the first step in the right direction and should guide you to the initial steps. Write to us at and learn how we can scale your business using our cloud-native approach to data and ML, bringing significant business value and ROI.