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©2025 Cloudzen Innovations | All rights reserved.

MLOps

Optimize your ML model lifecycle management with CloudZen
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Model Building

Expedite model monitoring, training and testing, enable model repository and provision scalable infrastructure
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Model Deployment

Maximize AI initiatives and leverage open-source and cloud-based solutions or MLOps tools to deploy scalable MLOps frameworks. We also value innovation and continuous improvement, and are always looking for ways to improve our services and stay ahead of the competition.
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Model Serving

Batch or real-time business insights for reports/dashboards and downstream systems .
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Model Management

Detect model drift to ensure model accuracy and data drift, and manage model degradation.

Our Expertise

CloudZen ML Stack Ecosystem

CloudZen MLStack Services

ML Pipeline
Development

Cloudzen elevate your business’s machine learning operations, boosting productivity and efficiency by automating ML pipelines and integrating cutting-edge AutoML platforms. Leveraging our MLOps expertise guarantees enhanced planning and development, reproducibility throughout model training and deployment, scalability via streamlined access to essential tools and resources, and seamless continuity across the entire production flow, resulting in optimized machine learning operations.

Model Deployment
& Implementation

We have extensive experience in deploying machine-learning models on cloud-native infrastructure that are optimized for ML workloads, such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), ensuring their high availability, scalability and reliability.

Continuous Delivery for
Machine Learning

Our CI/CD service empowers your data science team to swiftly test new ideas and refine models through automated building, testing, and deployment of pipeline components to the target environment. By streamlining your machine learning pipeline’s development process, we expedite time-to-market and drive business growth. .

Model
Monitoring

Our observability solutions, such as distributed tracing, log analysis and anomaly detection are designed to provide real-time insights into the performance of your AI systems, enabling you to optimize and fine-tune your models for better accuracy and efficiency. .

CloudZen MLOPS Process

Aligning Machine Learning Objectives With Business Goals

  • Grasping the business goals and objectives of the organization.
  • Formulating a precise problem statement to be addressed through machine learning.
  • Pinpointing the necessary data sources and required data for the machine learning model.
  • Crafting a comprehensive strategy for building, testing, deploying, and monitoring the machine learning model

Data Preparation and Management

  • Creating a program for offline extraction or batch fetching from the desired data source.
  • Employing an automated data validation process to guarantee data cleanliness and compliance with a predefined schema.
  • Leveraging an auto-distribution mechanism to partition the validated data into distinct training and validation datasets.
  • Setting up a feature store as a centralized repository for storing and organizing pre-existing features.

Model Training

  • Selecting a set of storage-agnostic version control systems tailored for machine learning workflows.
  • Seamlessly integrating the selected version control systems into the platform and fine-tuning their configurations.
  • Ensuring automatic commitment of metadata generated from new training runs to the relevant version control system.
  • Establishing a metadata store to capture pertinent information for subsequent analysis.

Model Evaluation

  • Setting up a robust framework for model monitoring and validation using the chosen toolkit.
  • Enabling automated capture of critical performance data from every model run.
  • Logging and archiving all pertinent details to ensure effortless reproducibility of results.
  • Establishing specific triggers to initiate pre-training when the model underperforms relative to expectations.

Model Serving

  • Identifying the optimal framework for encapsulating the model as an API service.
  • Alternatively, choosing and configuring a container service for deployment purposes.
  • Setting up a repository tailored for production, ensuring models are deployment-ready.
  • Developing a model registry to house all pertinent metadata linked to each individual model.

Model Monitoring

  • Choosing the optimal agent for real-time model monitoring.
  • Configuring the agent to detect anomalies, identify concept drift, and monitor model accuracy in real-time.
  • Integrating supplementary mechanisms to estimate model resource consumption.
  • Setting up re-training triggers and configuring alerts accordingly to ensure timely actio

Work with us

We would love to hear more about your project

Let’s talk us
CloudZen is a leading Europe-based data engineering and Software Automation firm dedicated to crafting bespoke digital solutions for businesses worldwide.

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© 2025 Cloudzen Innovations | All rights reserved