While pure software products are primarily about code and its further development, machine learning applications also have to take into account data and machine learning models. This creates new, machine-learning-specific challenges which go beyond the well-known DevOps challenges and which are explicitly addressed by MLOps. MLOps practices optimise the processes involved in order to make the development and maintenance of machine learning models faster, more efficient and of a higher quality.
Solving challenges in data science projects
In many machine learning projects, the greatest challenge is not creating a proof of concept (PoC). Instead, the biggest issues arise when deploying an initial machine learning model to production – i.e. integrating it into existing processes and the infrastructure provided.
Many proofs of concept do not take into account the subsequent integration and deployment processes, with the result that machine learning applications cannot be transferred to productive environments.
Prerequisites for machine learning in productive environments
Over and above the specifications for pure software applications, a machine learning application within a productive environment requires:
- A data connection (ETL pipeline) to enable the use and automatic retraining of machine learning models
- Opportunities for further development (such as using agile processes to add more features) by data scientists
- Rollback and A/B testing capabilities to enable experimentation
- Monitoring of model quality to enable the use of KPIs
The benefits of MLOps
MLOps enables machine learning applications to be used to generate high, sustainable added value for companies. The advantages of using MLOps principles are:
Minimal production time
Less risk and better business decisions (fewer application errors)
High-speed release process
Adaptability to new requirements
Increased user acceptance and effectiveness of the machine learning application
Successful implementation of MLOps
The implementation of a machine learning application consists of three phases: design, development, and production.
These phases are repeated several times during the iterative development of the optimal product. The first cycle results in the Minimal Viable Product (MVP), which is enhanced over multiple iterations, gaining added value each time. Machine-learning-specific monitoring tracks the results directly in the production environment.
Agnostic use of MLOps tools
When selecting frameworks and tools, we follow a technology-agnostic approach in order to provide our customers with the best solutions. In addition to Azure Machine Learning and AWS Sagemaker, we also use open-source tools such as Kubeflow, MLflow, ClearML and DVC.
Flexible development of machine learning applications through MLOps
A mature MLOps system permits a holistic perspective during the development of machine learning applications. It enables adjustments based on data collected from operations and monitoring to be made quickly and with high quality within the three phases.
Despite strong dependencies between design, development and production, MLOps ensures a high degree of automation, as well as reducing technical debt. Specifically, this means the application of agile principles, the integration of machine learning models and data sets into the CI/CD cycle, and the standardization of the release cycle for applications both with and without machine learning components. Dedicated data and model versioning and experiment tracking also ensure reproducibility.
MLOps makes for successful machine learning projects
We will work with you to develop a customised, holistic MLOps concept to ensure the success of your machine learning applications and data products. Our experienced data scientists and data/machine learning engineers will help you implement the technical aspects of a sustainable MLOps architecture while exemplifying the MLOps culture.
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