The Engineering Chapter is the ML6 special division specializing in best practices and insights with regards to bringing machine learning models into a production environment. This means we foster the culture of MLOps within the entire company to efficiently bring our ML solutions to production.
The real challenge isn't building an ML model, the challenge is building an integrated ML system and to continuously operate it in production.
Internal tooling
Internal tooling helps us to speed up delivery, standardize approaches and technologies and enable efficient use of time and resources. With our internal tool GCCP we are able to reach high internal quality of our solutions without the downside of slow start due to setup.
GCCP is our command line “Cookie Cutter” tool that can easily generate boilerplate code for our most used GCP components. GCCP enables boilerplate customization which helps us to adapt the templates for the needs of a specific use case, and then build more custom logic on top. In addition, it generates configuration files for secure infrastructure setup and CICD.In short, it kickstarts any project and allows flexibility to solve problems creatively within the safeguards set up by GCCP.
Open Source Contributions
Taking ownership and responsibility is in our DNA. We are always happy to help the community to improve the tools we use ourselves, thus we are very enthusiastic about contributing to open source software. Have you heard about Connexion?
We are convinced of the API-first approach for building applications. This way, we start from the business problems we are solving instead of getting bogged down in technical details from the start. It allows us to clearly separate definition and implementation, to efficiently collaborate with the stakeholders and to reduce applications developing costs. Connexion is a great Python framework and the best choice for our needs. We are very proud to be part of the community and to help move Connexion forward!
Best practices and code quality
Standardization has a positive impact on any business. It enables efficient use of resources, reduces risks and delivery costs. The Engineering chapter provides the guidelines, documents and propagates best practices at ML6. CI/CD and automation in general is part of our development culture.
We aim to avoid any manual action when possible which enables delivery of high quality software faster and allows us to focus on solving business problems. Next to that, we foster good development practices within the company such as code review guidelines.Code review leads to better implementations and more efficient solutions, it helps to find bugs earlier when those are cheaper to fix, as well as to share the experience of senior developers and to improve team cohesion.
Infrastructure
Our preferred infrastructure is GCP but we also built solutions on other cloud platforms, and we have a proven track record of on-edge solutions.
Security
The Chapter works closely together with our Security unit to not only make the secure way the easy way for our developers, but also to constantly monitor our deployed solutions.
MLops
MLOps is an ML engineering culture and practice that aims at unifying ML system development (Dev) and ML system operation (Ops). This mindset enables us to accelerate any PoC, as there is a production mindset from the start - and it’s not an afterthought.
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Connexion
Have you heard about the Python framework Connexion? We are convinced of the API-first approach for microservices. It allows us to focus on solving the business problem at hand instead of implementation details.
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CI/CD and automation
CI/CD and automation in general is part of our development culture. It increases development velocity and eliminates potential manual errors. Closely related to MLOps.
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GCCP
GCCP is our command line “Cookie Cutter” tool that can easily generate boilerplate code for our most used GCP components.
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A General Framework for Machine Learning Pipelines on GCP
A General Framework for Machine Learning Pipelines on GCP
Our takeaways and sessions to rewatch from Beam Summit 2022
Our takeaways and sessions to rewatch from Beam Summit 2022
How to train Darknet models in Vertex AI pipelines
How to train Darknet models in Vertex AI pipelines
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