Online or onsite, instructor-led live MLOps training courses demonstrate through interactive hands-on practice how to use MLOps tools to automate and optimize the deployment and maintenance of ML systems in production.
MLOps training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live MLOps training can be carried out locally on customer premises in Cairo or in NobleProg corporate training centers in Cairo.
NobleProg -- Your Local Training Provider
Regus - Cairo Nile City Towers
North Tower, Nile City Towers, Cairo, egypt, 11624
Cornich El Nile, Ramelt Beaulac, North Tower, Nile City Towers, 22nd Floor, Cairo, 11624
The Nile City Towers Centre provides office space on the 22nd and 23rd floors of one of the most prestigious business addresses in the Egyptian capital next to the Nile.
Nile City Towers Center's facilities include video-conferencing and hotels and restaurants are close by. There is easy access by car and public transport. The city is served by an international airport.
Cairo - Four Seasons Hotel
1089 Corniche El Nil, Cairo, egypt
In the heart of Cairo on the banks of the River Nile, our Hotel opens the door to discovering Egypt’s 5,000-year-old ancient mysteries. Just a short drive away, set out to explore the majestic pyramids, the 1,000-year old market, the 12th-century citadel and the ancient Egyptian museum. Later, experience Cairo’s breathtaking sunset from the Nile on a traditional felucca, dine at our nine acclaimed restaurants, relive the pharaoh’s beauty secrets at the Spa or simply take an art tour to see the collection of contemporary local artwork that fills our halls.
Facilities: State-of-the-art audiovisual system
Motorized drop-down screens
Remote-controlled lighting
Wired or wireless high-speed e-mail and internet access
Cairo - Le Méridien Heliopolis
51 El Orouba Street, Cairo, egypt, 11341
Cairo International Airport (CAI)
Hotel direction: 5.0 miles E
Airport shuttle service, reservation required, complimentary
24 Hours, 7 days a week
Shuttle Phone: +20 2 22905055 Ext 0
Alternate transportation: General Authority For Roads; fee: 0.45 USD (one way) ;scheduled
Estimated taxi fare: 8 USD (one way)
Visit CAI airport website
Driving directions
Take Airport Rd and El-Orouba Continue a head ; Drive to al Galla bridge then straight until you find on your right hand Mobile station then U TUREN DRIVE A Head THEN here is the hotel on the right hand.
For any close bus station please note Almaza Bus Station
1.2 miles N from hotel
This instructor-led, live training in Cairo (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 instructor-led, live training in Cairo (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 using AWS EKS (Elastic Kubernetes Service).
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.
This instructor-led, live training in Cairo (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.
This instructor-led, live training in Cairo (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to an AWS EC2 server.
By the end of this training, participants will be able to:
Install and configure Kubernetes, Kubeflow and other needed software on AWS.
Use EKS (Elastic Kubernetes Service) to simplify the work of initializing a Kubernetes cluster on AWS.
Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
Leverage other AWS managed services to extend an ML application.
This instructor-led, live training in Cairo (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to Azure cloud.
By the end of this training, participants will be able to:
Install and configure Kubernetes, Kubeflow and other needed software on Azure.
Use Azure Kubernetes Service (AKS) to simplify the work of initializing a Kubernetes cluster on Azure.
Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
Leverage other AWS managed services to extend an ML application.
This instructor-led, live training in (online or onsite) is aimed at data scientists who wish to go beyond building ML models and optimize the ML model creation, tracking, and deployment process.
By the end of this training, participants will be able to:
Install and configure MLflow and related ML libraries and frameworks.
Appreciate the importance of trackability, reproducability and deployability of an ML model
Deploy ML models to different public clouds, platforms, or on-premise servers.
Scale the ML deployment process to accommodate multiple users collaborating on a project.
Set up a central registry to experiment with, reproduce, and deploy ML models.
This instructor-led, live training in Cairo (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.
This instructor-led, live training in (online or onsite) is aimed at 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.
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Testimonials (2)
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
Guillaume Gautier - OLEA MEDICAL | Improved diagnosis for life TM
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