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MLOps Services

Scale your machine learning efforts and leverage them for business value.

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Objectivity Blog 416 306

MLOps services for your business


Machine learning operations (MLOps) is a set of best practices for the creation, deployment, maintenance, and management of machine learning solutions. By leveraging MLOps, data science teams can streamline their machine learning workflows and deliver models to production faster and more reliably, ensuring greater scalability at the same time. 

These practices can reduce the time and costs associated with building machine learning solutions while improving the accuracy and efficiency of their models, ultimately helping you gain a business advantage.  

Machine learning initiatives that support your goal

Benefits of MLOps

  • Improved scalability of data science efforts

    The application of MLOps best practices results in improved communication and closer collaboration among data engineers, data scientists and DevOps engineers and enables you to reliably scale your data science initiatives.  

  • Streamlined research & development

    Seamless access to the data they require allows your data scientists to run more experiments, thus increasing the likelihood of successfully solving your business problems. With the ability to validate business cases quickly, you can productionise concepts smoothly and accelerate return on investment. 

  • Reduced re-work and cost of data preparation

    Reusable frameworks for data preparation, centralised feature stores, and ML pipeline templates reduce project costs and let you avoid repetitive development tasks. 

  • Adaptation to the changing business environment

    By constantly monitoring your model’s accuracy, retraining it whenever needed, and incorporating A/B testing with multiple real-time models, you ensure continuous optimisation and the best possible performance of your solution. 

  • Reduced risk of faulty results

    Gain stronger trust in your ML initiatives by evaluating your models on the right datasets. Implement repeatable processes, enable the explainability of your ML solutions, and protect your data with robust security and compliance measures. 

What you’ll gain with comprehensive MLOps services

Operational automation of machine learning

Objectivity’s MLOps services are aimed at applying best practices to streamline the development, deployment, management, and monitoring of machine learning models. With the MLOps approach, you’ll be able to set up new ML models faster and improve the quality of final solutions. Additional elements of MLOps, such as real-time monitoring of production solutions, can tell you if your models are still providing up-to-date predictions or require retraining.  

With us, you'll choose the best infrastructure for your ML environment (cloud, on-premises, or open source), ensure proper integration and easy access to your data, and provide your data scientists with the tools they need. The final model will be automatically deployed to production with the right level of governance and monitoring and the cooperation among data engineering, data science, and infrastructure teams incorporating CI/CD processes will be seamless across all those domains. 

Whether you're looking into evaluating and conceptualising MLOps or building tailored, end-to-end MLOps platforms, our specialists are ready to support you. 

You’ll gain: 

  • Scalable solutions for data acquisition layers to simplify integration management. 
  • Reusable feature stores, ML pipeline templates, and CI/CD capabilities for smooth deployments. 
  • Automated and unified ML processes that serve as a foundation for efficient experimenting and a shorter path to production with business value delivered with a low engineering effort. 
Accordion Pattern

Why Objectivity?

Advantages of working with an experienced partner

By leveraging our experience from various fields of development — data science, data engineering, software development, cloud, and infrastructure, we can deliver comprehensive solutions that address various challenges of the machine learning delivery process. 

Our approach

Proven framework

  • Objectivity Icons Life Science Services & Solutions

    #1 Evaluate

    • Define your goals, and how they fit into your data strategy
    • Describe your current ML operational lifecycle
    • Assess your environment against an MLOps maturity model
    • List the tools you are using
    • Determine your capabilities in data science

  • Operations

    #2 Conceptualise

    • Design an end-to-end ML lifecycle process together with your team
    • Recommend technologies which fit your needs
    • Recognise additional roles that the process requires
    • Identify specific actions to lead you to a higher MLOps maturity

  • Area

    #3 Build

    • Set up the infrastructure
    • Build an MLOps platform:
      - Data acquisition layer
      - Feature store
      - MLOps pipelines
      - Models registry and serving services,
      - Models monitoring
      - DevOps infrastructure
    • Augment with additional data roles if needed

  • Devops Improved Collaboration

    #4 Adapt

    • Handover the MLOps platform
    • Train and upskill your data team
    • Move to hypercare and maintenance


Case Studies

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Data & AI Guild Master

Julia Orłowska

By implementing our MLOps framework, we can help your organisation scale and productionise its machine learning efforts to make sure they deliver tangible business value.


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