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.
July 25, 2023
February 13, 2023
How MLOps can help your organisation overcome bottlenecks that occur during the lifecycle of a machine learning project? Here are some examples:
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.
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.
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.
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.