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Gdynia

Hotel Nadmorski, Ejsmonda 2, Gdynia, Poland, 81-409

The training room is located just 3 kilometers from the PKP/PKS Station in Gdynia, making it easily accessible for participants traveling by train or bus. Additionally, it is only 400 meters away from the bus stop, facilitating access even for those using public transportation. It is equipped with necessary training tools such as a projector, screen, and flipchart, providing comfortable conditions for both participants and the trainer.

Gdańsk

Hotel Fahrenheit, Grodzka 19, Gdańsk, Poland, 80-841

The training room is located in the very heart of the picturesque Gdansk Old Town, making the surroundings not only inspiring but also exceptionally attractive for participants. Within close proximity, you can find the railway and bus stations, facilitating arrival for those traveling by both train and bus. Additionally, the airport and port are also within reach, making this location convenient for individuals coming from distant places, both domestically and internationally.

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Kubeflow

Kubeflow

Explore Our Courses

DeepSeek: Advanced Model Optimization and Deployment

 14 Hours

This instructor-led, live training in Trojmiasto (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.
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MLOps on Kubernetes: CI/CD Pipelines for Machine Learning

 14 Hours
This live training in Trojmiasto helps intermediate practitioners build automated MLOps pipelines on Kubernetes. Participants design CI/CD workflows, implement GitOps strategies, and deploy ML models using containerized infrastructure for scalable, reproducible machine learning operations.
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Kubeflow Essentials: Build, Train & Serve with Kubernetes

 14 Hours
This hands-on training in Trojmiasto equips you with the skills to build, train, and serve machine learning models on Kubernetes using Kubeflow. You will learn to navigate the ecosystem, author scalable pipelines, and manage production-ready workloads with best practices.
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Docker for MLOps: End-to-End Pipeline Containerization

 21 Hours
This instructor-led training in Trojmiasto guides professionals through containerizing complete ML pipelines using Docker. Participants will master building reproducible environments, orchestrating training and inference workloads, and implementing CI/CD for scalable MLOps deployments.
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Kubeflow Fundamentals

 28 Hours

This instructor-led, live training in Trojmiasto (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.
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MLOps: CI/CD for Machine Learning

 35 Hours

This instructor-led, live training in Trojmiasto (online or onsite) is aimed at engineers who wish to evaluate the approaches and tools available today to make an intelligent decision on the path forward in adopting MLOps within their organization.

By the end of this training, participants will be able to:

  • Install and configure various MLOps frameworks and tools.
  • Assemble the right kind of team with the right skills for constructing and supporting an MLOps system.
  • Prepare, validate and version data for use by ML models.
  • Understand the components of an ML Pipeline and the tools needed to build one.
  • Experiment with different machine learning frameworks and servers for deploying to production.
  • Operationalize the entire Machine Learning process so that it's reproduceable and maintainable.
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