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 śląskie or in NobleProg corporate training centers in śląskie.
NobleProg -- Your Local Training Provider
Bielsko-Biała
ZIAD, Armii Krajowej 220, Bielsko Biała, Poland, 43-316
NobleProg training rooms in Bielsko-Biała are located in the ZIAD building on the ground floor (Zakład I...
NobleProg training rooms in Bielsko-Biała are located in the ZIAD building on the ground floor (Zakład Informatyki, Automatyki i Doskonalenia Zawodowego (on Aleja Armii Krajowej 220 a. ZIAD buildings are located at the foot of the Szyndzielnia slope.) The building has a ramp for disabled people From the bus station, go to the Warszawska Dworzec station, north (on the same side as the bus station) Bus number 8 in the direction of Szyndzielnia goes about 22 minutes (9 stops). The target stop is the Home Army ZIAD, which I am located 400 meters from the ZIAD training room, in the same way, you can get to ZIAD from the Bielsko Biała Główna railway station.
Częstochowa
Częstochowa Industrial and Technological Park, Wały Dwernickiego 117/121, Częstochowa, Poland, 42-202
The training room, located in a convenient location near the city center, in the Tysiąclecia district. It...
The training room, located in a convenient location near the city center, in the Tysiąclecia district. Its close proximity to the PKP railway station and the PKS bus station makes it exceptionally convenient for all participants traveling by both train and bus. Additionally, the room is equipped with air conditioning, Wi-Fi internet, a projector, a built-in screen, a flipchart, and an audio mixer, providing conducive conditions for effective training delivery and participant comfort.
Gliwice
Zwycięstwa 10, Gliwice, Poland, 44-100
The entrance to the facility is located on Dolnych Wałów Street. The rooms are situated in the center of Gliwice, just a 2-...
The entrance to the facility is located on Dolnych Wałów Street. The rooms are situated in the center of Gliwice, just a 2-minute walk from the market square and 5 minutes from the train station. Access from the bus station is possible by bus lines number: 187, 702, 692. There is also an option for parking at a paid parking lot on the main street.
Katowice
Centrum Szkoleniowe Moniuszki 7, Moniuszki 7, Katowice, Poland, 40-004
The training room is located in the heart of Katowice. Its attractive location in the city center guarante...
The training room is located in the heart of Katowice. Its attractive location in the city center guarantees easy access for all participants. The main railway station is just 500 meters away from our venue, and bus and tram stops are only 100 meters away. Additionally, it has excellent connections to the outbound route towards the A4 motorway to Krakow - Wroclaw and Pyrzowice Airport, making it an ideal venue for participants arriving from various parts of Poland and abroad.
This instructor-led, live training in śląskie (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 śląskie (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 śląskie (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 śląskie (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 śląskie (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 śląskie (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to Google Cloud Platform (GCP).
By the end of this training, participants will be able to:
Install and configure Kubernetes, Kubeflow and other needed software on GCP and GKE.
Use GKE (Kubernetes Kubernetes Engine) to simplify the work of initializing a Kubernetes cluster on GCP.
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 GCP services to extend an ML application.
This instructor-led, live training in śląskie (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to IBM Cloud Kubernetes Service (IKS).
By the end of this training, participants will be able to:
Install and configure Kubernetes, Kubeflow and other needed software on IBM Cloud Kubernetes Service (IKS).
Use IKS to simplify the work of initializing a Kubernetes cluster on IBM Cloud.
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 IBM Cloud services to extend an ML application.
This instructor-led, live training in (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to an OpenShift on-premise or hybrid cloud.
By the end of this training, participants will be able to:
Install and configure Kubernetes and Kubeflow on an OpenShift cluster.
Use OpenShift to simplify the work of initializing a Kubernetes cluster.
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.
Call public cloud services (e.g., AWS services) from within OpenShift to extend an ML application.
This instructor-led, live training in śląskie (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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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™
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