These interactive, instructor-led live MLOps courses, available both online and onsite, provide hands-on experience in utilizing MLOps tools to automate and enhance the deployment and maintenance of ML systems in production environments.
MLOps training is offered in two formats: "online live training" (also known as "remote live training") and "onsite live training." Online sessions are conducted via an interactive remote desktop, while onsite training can take place either at the customer's location in Plovdiv or at NobleProg's corporate training centers in Plovdiv.
NobleProg -- Your Local Training Provider
Business Center Plovdiv
Han Kubrat St 1, Plovdiv, Bulgaria, 4017
This is the most modern business center in the city, with all the necessary functionalities, while being located in a green part of the city.
It is about 20 minutes by bus from the main train station as well as the city center.
This instructor-led, live training in Plovdiv (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 Plovdiv 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 to achieve scalable and reproducible machine learning operations.
This hands-on training in Plovdiv provides the skills needed to build, train, and serve machine learning models on Kubernetes using Kubeflow. You will learn to navigate the ecosystem, create scalable pipelines, and manage production-ready workloads following best practices.
This instructor-led training in Plovdiv supports technical professionals in containerizing complete ML pipelines using Docker. Learners will gain proficiency in creating reproducible environments, orchestrating training and inference workloads, and establishing CI/CD processes for scalable MLOps deployments.
This live, instructor-led training in Plovdiv, offered online or on-site, is designed for developers and data scientists seeking to build, deploy, and manage machine learning workflows on Kubernetes.
By the end of this session, participants will gain the capability to:
Install and configure Kubeflow in on-premises and cloud settings.
Develop, deploy, and control ML workflows leveraging Docker containers and Kubernetes.
Operate full machine learning pipelines across diverse architectures and cloud environments.
Utilize Kubeflow to create and manage Jupyter notebooks.
Establish ML training, hyperparameter tuning, and serving workloads across various platforms.
This instructor-led live training, conducted in Plovdiv (online or onsite), is designed for engineers who wish to evaluate current approaches and tools. It aims to support intelligent decision-making on how to proceed with adopting MLOps in their organizations.
By the end of this training, participants will be able to:
Install and configure various MLOps frameworks and tools.
Assemble 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 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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