Online or onsite, instructor-led live MLOps training courses demonstrate through interactive hands-on practice how to use MLOps tools to automate and optimize the deployment and maintenance of ML systems in production.
MLOps training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Rome onsite live MLOps trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Rome
NobleProg Rome, Via Clelia Bertini Attilj 4, Rome, italy, 34D
CoworkTalenti is located in the northern part of Rome, in the Talenti–Montesacro district, just a few minutes from the ring road. It is easily accessible via Via Nomentana.
This instructor-led, live training in Rome (online or onsite) is designed for advanced-level AI engineers and data scientists with intermediate to advanced experience who aim to improve DeepSeek model performance, reduce latency, and efficiently deploy AI solutions using contemporary MLOps practices.
Upon completion 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 both cloud and on-premise infrastructure.
Monitor, maintain, and scale AI solutions effectively.
MLOps on Kubernetes is a framework for automating the training, validation, packaging, and deployment of machine learning models using containerized pipelines and GitOps workflows.
This instructor-led, live training (online or onsite) is aimed at intermediate-level practitioners who wish to build automated, scalable MLOps pipelines on Kubernetes.
After completing this training, participants will be equipped to:
Design end-to-end CI/CD pipelines for machine learning.
Implement GitOps workflows for model deployment and versioning.
Automate training, testing, and packaging of ML models.
Integrate monitoring, alerting, and rollback strategies.
Course Format
Instructor-guided presentations and technical deep dives.
Hands-on exercises that build real-world CI/CD workflows.
Live-lab practice deploying ML workloads to Kubernetes.
Course Customization Options
Organizations may request tailored content aligned with their internal MLOps tools and infrastructure.
Kubeflow is an open-source platform engineered to simplify the process of building, training, and deploying machine learning workloads on Kubernetes.
This instructor-led live training (available online or onsite) is designed for professionals at beginner to intermediate levels who aim to establish robust ML workflows using Kubeflow.
After completing this training, participants will acquire the ability to:
Explore the Kubeflow ecosystem and its fundamental components.
Docker serves as a containerization platform designed to create reproducible, portable, and scalable environments for machine learning systems.
This instructor-led live training, available both online and onsite, targets intermediate to advanced technical professionals seeking to containerize and operationalize complete ML pipelines using Docker.
Upon completing this training, participants will be able to:
Containerize ML training, validation, and inference workloads.
Design and orchestrate end-to-end ML pipelines utilizing Docker and complementary tools.
Implement versioning, reproducibility, and CI/CD practices for ML components.
Deploy, monitor, and scale ML services within containerized environments.
Course Format
Interactive lectures reinforced by practical demonstrations.
Hands-on exercises centered on constructing real-world ML pipeline components.
Live-lab implementation of end-to-end containerized workflows.
Customization Options
For training tailored to specific ML infrastructure requirements, please contact us to explore available options.
This instructor-led live training in Rome (online or onsite) is aimed at developers and data scientists who wish 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 on premise and in the cloud.
Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
Run entire machine learning pipelines on diverse architectures and cloud environments.
Using Kubeflow to spawn and manage Jupyter notebooks.
Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.
This instructor-led, live training in Rome (online or onsite) is designed for engineers who want to assess the current approaches and tools available, enabling them to make informed decisions about adopting MLOps within their organizations.
Upon completion of this training, participants will be able to:
Install and configure various MLOps frameworks and tools.
Build a team with the appropriate skills to construct and support an MLOps system.
Prepare, validate, and version data for use by ML models.
Understand the components of an ML Pipeline and the tools required to build one.
Experiment with different machine learning frameworks and servers for production deployment.
Operationalize the entire Machine Learning process to ensure it is reproducible and maintainable.
This instructor-led live training, available online or onsite, is aimed at machine learning engineers who wish to utilize Azure Machine Learning and Azure DevOps to support MLOps practices.
By the end of this training, participants will be able to:
Build reproducible workflows and machine learning models.
Manage the machine learning lifecycle.
Track and report model version history, assets, and more.
Deploy production ready machine learning models anywhere.
Online MLOps training in Rome, MLOps training courses in Rome, Weekend MLOps courses in Rome, Evening MLOps training in Rome, MLOps instructor-led in Rome, MLOps trainer in Rome, Evening MLOps courses in Rome, Online MLOps training in Rome, MLOps boot camp in Rome, MLOps classes in Rome, MLOps private courses in Rome, MLOps instructor-led in Rome, Weekend MLOps training in Rome, MLOps coaching in Rome, MLOps on-site in Rome, MLOps one on one training in Rome, MLOps instructor in Rome
