ML and AI Operations
Engineering
A core servicing offer is our comprehensive MLOps (Machine Learning Operations) engineering services designed to streamline and optimize the end-to-end lifecycle of machine learning models. Our dedicated team of MLOps engineers collaborates closely with data scientists, software developers, and DevOps professionals to automate and standardize processes, from model development and training to deployment and monitoring.
Our value proposition
1 / Remove manual effort
Automated model monitoring solutions to detect drifts, anomalies, and performance degradation, enabling proactive maintenance and optimization.
2 / Establish Governance
Implement model governance frameworks and compliance controls to ensure transparency, accountability, and regulatory compliance.
3 / Continuous improvement
LMA facilitates feedback loops and iterative development cycles to refine ML models, drive innovation, and adapt to changing business needs.
What do I get?
Working with LMA, we can customize the output of our services to your unique needs. Below is a list of deliverables we have created for other clients.
Project management plan
A detailed document outlining the specific requirements and objectives of the tailored AI solution.
CI/CD Pipeline
Configured CI/CD pipelines tailored to automate the build, test, and deployment processes of ML models, promoting rapid iteration and seamless integration.
Requirements documentation
A detailed document outlining the specific requirements and objectives of the tailored AI solution.
MOdel governance framework
Established model governance framework defining roles, responsibilities, and processes for managing ML models throughout their lifecycle, ensuring accountability and transparency.
Solution architecture
Comprehensive blueprint detailing the technical components, interactions, and deployment strategy of the AI solution
Knowledge transfer
Comprehensive documentation and training materials to facilitate the onboarding and adoption of the ML solution by stakeholders and end-users.
How do we measure success?
At the end of your engagement with LMA, you will have a working pipeline to build a repeatable, scalable and responsible ML/AI capability.
