MLOps Training in Curitiba

This instructor-led live training in Curitiba (online or onsite) targets AI engineers and data scientists at an advanced level who possess intermediate-to-advanced experience and aim to enhance DeepSeek model performance, reduce latency, and deploy AI solutions efficiently using modern MLOps practices.

Upon completing this training, participants will be able to:

• Optimize DeepSeek models for efficiency, accuracy, and scalability.
• Apply best practices for MLOps and model versioning.
• Deploy DeepSeek models across cloud and on-premise infrastructure.
• Effectively monitor, maintain, and scale AI solutions.

MLOps on Kubernetes is a framework designed to automate the training, validation, packaging, and deployment of machine learning models through containerized pipelines and GitOps workflows.

This instructor-led, live training (available online or on-site) is targeted at intermediate-level practitioners looking to build automated, scalable MLOps pipelines on Kubernetes.

Upon completing this training, participants will be able to:

• Design end-to-end CI/CD pipelines for machine learning.
• Implement GitOps workflows for model deployment and versioning.
• Automate the 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 designed to streamline building, training, and deploying machine learning workloads on Kubernetes.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to build reliable ML workflows using Kubeflow.

Upon completion of this training, attendees will gain the skills to:

• Navigate the Kubeflow ecosystem and core components.
• Build reproducible workflows with Kubeflow Pipelines.
• Run scalable training jobs on Kubernetes.
• Serve machine learning models efficiently using Kubeflow Serving.

Format of the Course

• Guided presentations and collaborative discussions.
• Hands-on labs with real Kubeflow components.
• Practical exercises to build end-to-end ML workflows.

Course Customization Options

• Customized versions of this training can be arranged to align with your team’s technology stack and project requirements.

Docker serves as a containerization platform designed to build reproducible, portable, and scalable environments for machine learning systems.

This instructor-led live training, available either online or on-site, targets intermediate to advanced technical professionals looking 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 using 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 supported by practical demonstrations.
• Hands-on exercises focused on constructing real-world ML pipeline components.
• Live lab implementation for end-to-end containerized workflows.

Customization Options

• For training tailored to specific ML infrastructure requirements, please contact us to discuss available options.

This instructor-led, live training in Curitiba (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.
• Use Kubeflow to spawn and manage Jupyter notebooks.
• Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training session in Curitiba (available online or in-person) is designed for engineers who wish to evaluate the approaches and tools currently available, helping them make informed decisions on how to proceed with adopting MLOps within their organizations.

Upon completing this training, participants will be able to:

• Install and set up various MLOps frameworks and tools.
• Build a team with the appropriate skills to construct and support an MLOps infrastructure.
• Prepare, validate, and version data for use by Machine Learning models.
• Comprehend the components of a Machine Learning Pipeline and identify the necessary tools to create 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.

This instructor-led live training (online or onsite) is designed for machine learning engineers who wish to use Azure Machine Learning and Azure DevOps to facilitate 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.