MLOps: 10-Week Implementation

MAQ Software

Streamline your Machine Learning Workflow with MLOps

MLOps is a critical component of any machine learning project. It provides a framework for managing the projects by applying best practices from software engineering and DevOps to machine learning. Developing and deploying models in production can be challenging. By adopting MLOps practices, organizations can ensure that their machine learning models are optimized for performance, scalability, and accuracy.
Our MLOps solution provides a comprehensive approach to assess and optimize MLOps processes by using Azure’s set of tools and services. This will enable the development, deployment, and management of Machine Learning models at scale. We will plan for the implementation of Azure based services such as Azure Data Factory, Azure Databricks, Azure Cognitive services, and Azure DevOps in your system that will help in managing the project more efficiently. Our team of certified experts will help you design, develop, and implement MLOps processes to improve the speed, collaboration, and governance of ML (Machine Learning) models. We will provide you with detailed reports, guidance, and support to ensure that MLOps processes are aligned with your business objectives and optimized for efficiency, effectiveness, and quality.

Target Customers

  • Data scientists
  • Machine learning engineers
  • IT professionals
  • Business leaders


Our 10-week engagement includes:

  • An assessment of current ML development and deployment processes
  • The design and development of MLOps processes, including the selection of tools that best fit the organization’s needs
  • Building reproducible workflows and models
  • Training your team(s) on the use and maintenance of the MLOps processes
  • Assistance with ongoing support and maintenance


By the end of this engagement, your team will be equipped with the knowledge and resources needed to effectively use and maintain MLOps processes.


  • Improved efficiency and speed of ML model development and deployment
  • Increased collaboration and communication between data scientists and operations teams
  • Enhanced monitoring and management of ML models in production
  • Reduced risk and improved governance of ML models
  • Be able to easily deploy highly accurate models anywhere
  • Achieve governance across assets
  • Benefit from interoperability with MLFlow