Technologies / Artificial Intelligence

Kubeflow on AWS - Machine Learning in the Cloud

Advanced training on implementing and managing machine learning solutions in an AWS environment using the Kubeflow platform. The training program combines practical aspects of ML task orchestration with advanced techniques for deploying models in a production environment. Through an intensive workshop, participants will learn the full lifecycle of an ML project, from data preparation to model training to deployment and monitoring in production. The training uses real-world use cases and MLOps best practices, providing a comprehensive understanding of the manufacturing process in machine learning projects.

Issues

Kubeflow Architecture
ML experiment management
Distributed model training
Data processing pipelines
Model versioning
Performance monitoring
Serving predictions
Optimization of resources
ML process automation
Scaling up solutions
Environmental management
MLOps best practices

Benefits

The training will develop practical skills for implementing ML projects in an AWS production environment
Upon completion of the course, efficiency in managing the lifecycle of machine learning models will increase
The experience gained will enable the implementation of efficient and scalable MLOps solutions
Practical knowledge will enable optimization of ML model training and deployment processes
The acquired skills will accelerate the organization's transformation towards mature ML practices
Of the Kubeflow platform will increase efficiency in automating ML processes

Who is this training for?

ML engineers involved in model implementations

Data Scientists moving into production roles

MLOps and DevOps specialists

AI/ML solution architects

ML application developers

Data engineers working with ML projects

Teams responsible for ML platforms

Prerequisites

Basic knowledge of machine learning
Experience with Kubernetes
Knowledge of AWS basics
Python programming skills

Training program

Configuring the environment
Integration with AWS services
Management of computing resources
Data processing and experimentation
Data processing pipelines
Experiment management

Data and code versioning
Model training and optimization

Hyperparameter tuning
GPU resource management
Performance monitoring
Implementation and monitoring
Model deployment strategies

Performance monitoring
Model version management

Delivery Methods

Online

Convenience of participating from anywhere
Interactive live sessions with trainer
Materials available for 30 days
No travel costs

On-site

Direct contact with trainer and group
Intensive hands-on workshops
Networking with other participants
Full focus on learning

Frequently asked questions

Who is the Kubeflow on AWS - Machine Learning in the Cloud training for?

The target audience includes ML engineers involved in model implementations, Data Scientists moving into production roles, and MLOps and DevOps specialists. Level: advanced.

How long is the Kubeflow on AWS - Machine Learning in the Cloud training?

The training lasts 4 days. It is delivered online or on-site.

Will I receive a certificate?

Yes — every participant receives a completion certificate confirming acquired competencies. EITT is ISO 9001 certified.

Can this training be conducted for a closed group?

Yes — we offer dedicated closed trainings for companies. We customize the program to your team's needs. Contact us for an individual quote.