XenonStack Recommends

MLOps Lifecycle Management Services

Accelerate Complex Operations with ML Solutions

Developing and implementing advanced Machine Learning Solutions to help enterprises leverage predictive analytics for real-time insights, MLOps driven best practices for Maintaining Dashboards & Manage the Full Lifecycle of ML powered applications to unlock the potential of AI for solving complex business challenges.


Managed Analytics

Managed Cognitive Analytics Solutions for facilitating Enhanced Customer Engagement, Improved Decision Making, and Effective Business Processes.


ML Workloads

Manage and Optimize your Machine Learning and Artificial Intelligence workloads for Performance and Deployment.


Security Solutions

Streamline mapping of assets, vulnerabilities, best in class security and regulatory compliance.

Machine Learning Architecture

ML mechanism for enterprises to build a Multi-ML parallel pipeline system and examine different ML methods' outcomes to enhance the overall functioning, productivity, Repeatability, Versioning, tracking, and Decision-Making process.


Machine Learning Managed Services




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    Managed Security

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      Basic Monitoring

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      24 x 7 Support




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    All Standard features

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      Managed Backup Full and Daily Snapshots

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      Managed Operating System Patches and Updates, Hardening, Configuration and Tuning




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    All Standard and Pro features

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      Application Monitoring and Response CPU, RAM, Disk IO, URL, and Application metrics

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      Advanced Enterprise Analytics and Dashboard

XenonStack Managed Services Left Image
XenonStack Managed Services Right Image

Machine learning as a Service for Enterprise


MLOps Platform

Deploy ML models within minutes rather than weeks and enables them to achieve a far faster value result than with homegrown deployments.


Automate the process completely from model building to model deployment in production.

Utilize intelligent technologies to make data sources within reach of analytics methods for decision making and business intelligence.

Supervised detection using a combination with statistical schemes, including the capability of encoding interdependencies between variables and predicting events.

Use the technique to construct an enhanced perspective on customer experiences and the voice of the customer.

Machine Learning Service Providers

Building scalable cloud infrastructure-driven solutions on Google, AWS, and Azure ML Platforms.


AWS Services

Accelerating and streamlining end-to-end model development lifecycle with Amazon SageMaker.


Azure Services

Leveraging Azure ML capabilities at scale,and effectively adding intelligence to applications.

Related Machine Learning Insights

7 Machine Learning Trends for Businesses in 2022


7 Machine Learning Trends for Businesses in 2022


Amazon SageMaker : End-to-End Managed Machine Learning Platform

Amazon SageMaker : End-to-End Managed Machine Learning Platform

MLOps Platform - Managing end-to-end Life Cycle of MLOps Applications


MLOps Platform - Managing end-to-end Life Cycle of MLOps Applications



Conversational AI

Virtual Assistant


Encourages enterprises to shorten production cycles and deliver results at scale while continuously improving results.


MLOps features data scientists and hybrid services designed to deliver automation in ML pipelines and gain more valuable insights into production systems.

Operate conversational AI-centric solutions to develop consistent experiences, improve customer engagement, and gain personalized insights in real time.

Virtual Assistant

Build voice and text-based assistants with natural language understanding (NLU) and AI.