Body leasing of MLOps engineers (Machine Learning Operations contracting) at Commoditech gives you instant access to experts automating the lifecycle of artificial intelligence models. We move ML models from the experiment phase to stable production deployment in the cloud.
What does MLOps do and why is it crucial for AI projects?
Building a machine learning (ML) model is only 10% of the success. The remaining 90% is MLOps - system engineering responsible for ensuring that the model works stably in production, automatically learns on new data and is constantly monitored for a decrease in accuracy (data drift). MLOps engineers implement:
- Pipeline automation (ML Pipelines): We create continuous pipelines of data collection, training, testing and deployment of models (CI/CD for ML using Kubeflow, MLflow or Prefect).
- Model registry management: We configure central model repositories (Model Registry) to track versions, parameters and training metrics.
- Containerization and orchestration: We package models into Docker images and deploy them on Kubernetes clusters (K8s) for elastic scaling of GPU/CPU resources.
- Production monitoring: We implement systems for monitoring anomalies in model operation (model and data drift detection) using tools such as Prometheus, Grafana and Evidently AI.
Why hire MLOps engineers at Commoditech?
MLOps engineers have a rare combination of DevOps competences with knowledge in the field of Data Science and Cloud Architecture:
- Google Vertex AI & AWS Bedrock cloud experts: We specialize in the full use of native cloud tools (Vertex AI Pipelines, SageMaker, Azure ML).
- Quickly scale your team: We provide access to engineers with a system profile, ready to cooperate with your Data Scientists and backend programmers.
- Infrastructure cost reduction: We optimize the use of GPU/TPU instances in the cloud, reducing the costs of maintaining AI models in production.
Technology stack of MLOps engineers
| Area of competence | Technologies, platforms and frameworks |
|---|---|
| ML Cloud Platforms | Google Cloud Vertex AI, AWS SageMaker, AWS Bedrock, Azure Machine Learning |
| Model Tracking & Pipeline | MLflow, Kubeflow, Weights & Biases, Prefect, Apache Airflow, DVC |
| Infrastructure & IAC | Terraform, Ansible, Docker, Kubernetes (K8s), KServe, Triton Inference Server |
| Languages & DevOps | Python, Bash, CI/CD (GitHub Actions, GitLab CI), Prometheus, Grafana |
Are your ML models waiting for production implementation?
Contact us. We will select MLOps engineers for you who will design automated cloud architecture for your AI projects.
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