Simplify ML model deployment and maintenance in production

Gartner reports that only 54% of AI pilot projects make it to production, with the rest failing to deliver business value. The answer to this issue? MLOps (Machine Learning Operations).

MLOps applies DevOps concepts such as automation, version control, and so on to machine learning models, making them easier to deploy, maintain and optimize in production.

Client cases

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Typical challenges

How MLOps can help your organisation overcome bottlenecks that occur during the lifecycle of a machine learning project? Here are some examples:

Collaboration & communciation issues

ML development involves many teams with different needs, which can cause collaboration problems. MLOps solves this by supporting close collaboration and communication so that everyone - data scientists, ML engineers, software engineers and business stakeholders - is on the same page from the beginning of the project and knows their responsibilities.

Poor data quality

Consistent data quality is essential for accurate ML model predictions, but poor data quality is a common issue that ML engineers face. MLOps solves this problem through automated data quality checks and version control tools to track data throughout the ML model lifecycle. By identifying data issues early on, you can avoid unexpected failures and save time in the long run.

Difficulties to scale and maintain

Scalable ML apps generate more business value. Why? Because they can handle increased workload or traffic without reduced performance. MLOps improves scalability by using automated pipelines for ML model training, evaluation, and deployment through CI/CD processes.

High level outline of the solution

ML applications are complex, with many different components that require specific best practices to guarantee success.

However, while attention to individual components is crucial, there are general best practices that all components can benefit from and that we would like to share with you.

Modularize code into logical ML related steps

Break down an ML pipeline into smaller, manageable components such as data ingestion and data validation. This leads to easier maintenance, updates and reuse. Moreover, team members can work together on different components uninterrupted.

Containerize code after experimentation phase

Package the code and dependencies into a container once the experimentation phase is complete. This makes it easier to use the ML model in different environments without having to worry about environment-specific performance differences.

Version control data and models

Version control is like a time machine for your data and models. By keeping track of changes over time, you can go back to specific versions used for experiments or deployment. This helps with reproducibility and allows teams to test different versions to find the best one.

Mixed, autonomous teams

Mixed teams consist of diverse experts who work together on ML models. Each team member has autonomy over their area of expertise, leading to more efficient collaboration and better overall coverage during the lifecycle of a model.

Peer reviews, peer reviews and more peer reviews

Reviewing and giving feedback on team members' work is important for maintaining quality and consistency. For example, code reviews help identify any bugs or mistakes before they impact the final solution and are a great opportunity to learn from each other.

In practice

By integrating MLOps best practices into all of our projects, we help you realize the full potential of AI in the most effective way possible.

We can also guide you in improving your current approach to MLOps by aligning the solution with your infrastructure and specific needs. No MLOps solution fits all, but we can use our expertise to help you create the best MLOps solution possible.


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