Delivered either online or onsite, our instructor-led live MLOps training courses offer interactive, hands-on experience in leveraging MLOps tools to automate and refine the deployment and upkeep of ML systems in production environments.
MLOps training can be accessed as "online live training" or "onsite live training". Online live training (also referred to as "remote live training") is facilitated through an interactive remote desktop. Onsite live training may take place directly at your premises in Gaia or at NobleProg corporate training centers in Gaia.
NobleProg -- Your Local Training Provider
Holiday Inn Porto - Gaia
220, Rua Diogo Macedo, 220, Vila Nova de Gaia 4400-107 , Gaia, Portugal
From Porto by Metro
If you're coming from Porto city centre:
Take the Metro Line D (yellow line) toward Vila d’Este.
Get off at João de Deus.
From João de Deus, walk about 13 minutes to: Rua Diogo Macedo 220, 4400-107 Vila Nova de Gaia.
From Vila Nova de Gaia train station
If by “local train station” you mean Vila Nova de Gaia-Devesas:
The address is approximately 12 minutes walking from Vila Nova de Gaia station according to the accommodation's location information.
You can therefore walk directly rather than taking the metro.
This instructor-led live training in Gaia (online or onsite) is aimed at advanced-level AI engineers and data scientists with intermediate-to-advanced experience who wish to enhance DeepSeek model performance, minimize latency, and deploy AI solutions efficiently using modern MLOps practices.
By the end of this training, participants will be able to:
Optimize DeepSeek models for efficiency, accuracy, and scalability.
Implement best practices for MLOps and model versioning.
Deploy DeepSeek models on cloud and on-premise infrastructure.
Monitor, maintain, and scale AI solutions effectively.
This live training in Gaia empowers intermediate practitioners to build automated MLOps pipelines on Kubernetes. Participants will design CI/CD workflows, implement GitOps strategies, and deploy ML models using containerized infrastructure, ensuring scalable and reproducible machine learning operations.
This practical training in Gaia provides you with the proficiency necessary to build, train, and serve machine learning models on Kubernetes using Kubeflow. You will master navigating the ecosystem, designing scalable pipelines, and overseeing production-ready workloads while adhering to industry best practices.
This instructor-led training on Gaia guides professionals through the process of containerizing complete ML pipelines with Docker. Learners will gain expertise in creating reproducible environments, orchestrating training and inference workloads, and implementing CI/CD for scalable MLOps deployments.
This instructor-led, live training in Gaia (online or onsite) is designed for developers and data scientists aiming to build, deploy, and manage machine learning workflows on Kubernetes.
By the end of this training, participants will be able to:
Install and configure Kubeflow in on-premise and cloud settings.
Build, deploy, and manage ML workflows using Docker containers and Kubernetes.
Run entire machine learning pipelines across diverse architectures and cloud environments.
Use Kubeflow to create and manage Jupyter notebooks.
Develop ML training, hyperparameter tuning, and serving workloads across multiple platforms.
This instructor-led, live training in Gaia (online or onsite) is designed for engineers looking to evaluate current approaches and tools. The course helps participants make informed decisions about adopting MLOps within their organizations.
Upon completion, participants will be able to:
Install and configure various MLOps frameworks and tools.
Build a skilled team capable of constructing and supporting an MLOps system.
Prepare, validate, and version data for consumption by ML models.
Understand the components of an ML Pipeline and the necessary tools to construct one.
Experiment with different machine learning frameworks and servers for production deployment.
Operationalize the entire Machine Learning lifecycle to ensure it is reproducible and maintainable.
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