Operationalising machine learning models

Implementing MLOps practices in the machine learning ecosystem allows organisations to be more effective in developing, deploying and governing production models.



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The MLOps context

Approximately 80% of data science ML projects never make it to production despite an increase in investments in ML-enabled applications.

According to Gartner, although organisations are keen to apply DevOps principles and practices for AI and ML projects, they lack the skills and experience to design and implement a fully automated ML pipeline solution.

Present challenges in managing ML models’ lifecycle:

  • Data science teams slowed down by highly manual and repetitive tasks
  • Lack of standardised ML processes and templates for creating and scaling ML projects
  • Unnecessary delays when deploying ML models
  • Data scientists struggling to keep track of ML experiments, results and model versions
  • Lack of visibility if the accuracy of the ML models deployed to production is beginning to decay

Learn how organisations can implement MLOps on AWS to boost productivity and trigger efficiencies. Click on the link here to download our white paper.


An automated ML training pipeline increases the frequency of ML experiments which leads to rapid innovation and shortens the time taken to bring models to production.

Implementing MLOps lays the foundation for data scientists to collaborate with software engineers and IT professionals on the development and deployment of machine learning models to production

Having a model governance framework and also versioning ML models as part of MLOps implementations enables data science teams to reproduce experiments and trained models.

Implementing model monitoring as part of MLOps solutions protects against data and model drift.

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    Data Reply is the Reply group company offering a broad range of advanced analytics and AI-powered data services. We operate across different industries and business functions, enabling them to achieve meaningful outcomes through effective use of data. We have strong competences in Big Data Engineering, Data Science and IPA; we build Big Data platforms and implement ML and AI models in a manner that is repeatable, efficient, scalable, simple and yet secure. We support companies in combinatorial optimization processes with Quantum Computing techniques that enable an engine with high computational performances.