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. Pune onsite live MLOps trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Pune, SB Road - Classroom
5, Senapati Bapat Rd, Shivaji Co operative Housing Society, Bhageerath, Gokhalenagar, Pune, india, 411016
Once known to be a prime residential locale, Senapati Bapat Road today has metamorphosed into a major IT and commercial hu...
Once known to be a prime residential locale, Senapati Bapat Road today has metamorphosed into a major IT and commercial hub of Pune. Stretching from the crossing of BMCC Road and Law College Road, meandering through an uphill pass carved out from the surrounding green hills, Senapati Bapat Road terminates at the Ganeshkhind Road, just off the prestigious University of Pune. En route, the road houses elite educational institutes such as Symbiosis College of Arts, Commerce and Science, and Symbiosis Institute of Management Studies, in addition to the Regional Passport Office (RPO), a museum dedicated to Dr. Babasaheb Ambedkar, along with a Buddhist pagoda, the Ratna Memorial Hospital, and the historically significant Chaturshringi Devi Temple.
Nearby is significantly dotted with the presence of major IT companies. Senapati Bapat Road serves as a major link road that can be accessed very easily, and connects Pune in every direction. It is just 8km from Koregaon Park, 5 km from Railway Station and 6 km from M.G. Road.
Details about Classroom
All the centres have state of the art audio/video webinars/seminars and presentation solutions. We also provide one-on-one Training sessions upon request. We encourage our students and delegates to bring along their own laptops during the exercises, as this helps in retaining the examples and handson exercises in their systems for post-session reference.
For Sales Enquires and Meetings
All our centres have batches running on weekdays and weekends hence, please note that, in most cases, usually we are not able to organise ad hoc sales meetings, especially on our classrooms as they are all occupied with ongoing training sessions . Please contact us by e-mail or phone at least one day earlier to make an appointment with one of our consultants at our corporate offices.
Pune, Koregaon Park - T1
36 Koregaon Park Annexe, Mundhwa Road, Ghorpadi, Pune, Next to Hotel Westin, Pune, india, 411001
Koregaon Park is connected with Kalyani Nagar, via the Aga Khan Bridge. Passage to Mundhawa and Magarpatta has been m...
Koregaon Park is connected with Kalyani Nagar, via the Aga Khan Bridge. Passage to Mundhawa and Magarpatta has been made possible by a recently constructed road. Koregaon Park is located approximately 06 kilometers from the Pune International Airport and about 04 kilometers from the city's main railway station. This region is surrounded by Kalyani Nagar, Yerwada, Ghorpadi, Magarpatta and Sangamwadi area.
Details about Classroom
All the centres have state of the art audio/video webinars/seminars and presentation solutions. We also provide one-on-one Training sessions upon request. We encourage our students and delegates to bring along their own laptops during the exercises, as this helps in retaining the examples and handson exercises in their systems for post-session reference.
For Sales Enquires and Meetings
All our centres have batches running on weekdays and weekends hence, please note that, in most cases, usually we are not able to organise ad hoc sales meetings, especially on our classrooms as they are all occupied with ongoing training sessions . Please contact us by e-mail or phone at least one day earlier to make an appointment with one of our consultants at our corporate offices.
This instructor-led, live training in Pune (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 Pune (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 Pune (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 Pune (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 Pune (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 (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 Pune (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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Testimonials (1)
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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