MLOps Training in Turin

This instructor-led, live training in Turin (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.
• Develop reproducible workflows utilizing Kubeflow Pipelines.
• Execute scalable training jobs on Kubernetes.
• Deploy machine learning models efficiently with Kubeflow Serving.

Course Format

• Structured presentations coupled with collaborative discussions.
• Practical labs involving real-world Kubeflow components.
• Hands-on exercises to construct end-to-end ML workflows.

Course Customization Options

• Tailored versions of this training can be configured to match your team’s technology stack and specific project needs.

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 Turin (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.