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Cloud Platform Training

Cloud Platform Training

Due to the COVID-19 our training courses will be taught via an online classroom.

Receive in-depth knowledge from industry professionals, test your skills with hands-on assignments & demos, and get access to valuable resources and tools.

As datasets have grown more rapidly than (local) computing power and storage during the past decade, cloud infrastructure has become more and more important for scalability, access management, storage, and monitoring. This is where Amazon (AWS), Microsoft (Azure) and Google (GCP) come in. But which one is best for your organizational needs? This training is perfect for everyone that wants to learn about the main cloud providers and what are the advantages of cloud vs on-premise data storage.

Are you interested? Contact us and we will get in touch with you.

 

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About the training & classes

The Cloud Platform training is split in 3 days. Click below to see a detailed description of each class: 

 
 
AWS Cloud

This training focuses on the Amazon corner of the cloud universe and aims to give an overview of their most important services and their relevance for Data Science and Machine Learning.

We start the theoretical part of this training by going into the history and background of Cloud Infrastructure in general, and Amazon Web Services in specific. Then, we discuss their solutions for access management, storage, compute, monitoring, etc before moving to Machine Learning services such as Sagemaker (ML), Rekognition (Image and Vision) and Comprehend (NLP) and concluding by mentioning some interesting others.

Having learned about this ecosystem of services we then gain hands-on experience during the lab session, in which we tie multiple components together and eventually train a prediction model for beer preferences based on a dataset of customer reviews.

The training includes theory, demos, and hands-on exercises. After this training you will have gained knowledge about:

  • Cloud Infrastructure history and background
  • Amazon Web Services background
  • Data center regions
  • IAM: Access management
  • S3: Simple Storage Service
  • EC2: Elastic Compute Cloud
  • Lambda: Serverless Compute
  • Cloudwatch: Monitoring
  • API gateway
  • Sagemaker: Machine Learning
  • Rekognition: Image and Video
  • Comprehend: Insights and relationships in text
  • Other services such as: Polly (Text to Speech), Transcribe (Speech Recognition) and Translate
  • Lab session to get hands-on experience with Amazon Cloud infrastructure
Microsoft Azure

Microsoft Azure is one of the most popular cloud computing services today, offering dozens of capabilities ranging from storage and databases to scalable data processing. This training provides an overview of the Azure landscape with a focus on IaaS, PaaS and Serverless services like VM’s, Networking (IaaS), storage, databases, serverless functions, and more. During the training, participants set up their own Virtual Machine and build a small data pipeline which automatically rescales images put in storage.

The training includes theory and practical exercises. After this training you will have gained knowledge about:

  • Azure datacenters (regions and availability zones)
  • Azure portal
  • Virtual Machines (VM’s) and virtual networks
  • Storage accounts with redundancy, tiering, and pricing options
  • Databases as a service (DBaaS), both SQL and NoSQL - Azure (serverless) apps
  • Azure container services
GCP

This training focuses on the Google corner of the cloud universe and aims to give an overview of their most important services and their relevance for Data Science and Machine Learning.

We start the theoretical part of this training by going into the history and background of Cloud Infrastructure in general, and Google Cloud Platform in specific. During this lesson on Google Cloud Platform you will learn about several topics that touch upon what a Machine Learning Engineer needs. We will do some data processing with Apache Beam, train and deploy a model and publish the results on Pub/Sub.

Having learned about this ecosystem of services we then gain hands-on experience during the lab session, in which we tie multiple components together and eventually train a prediction model for beer preferences based on a dataset of customer reviews.

The training includes theory, demos, and hands-on exercises. After this training you will have gained knowledge about:

  • Cloud Infrastructure history and background
  • GCP background
  • Apache Beam
  • Pub/Sub
  • Lab session to get hands-on experience with Google Cloud Platform infrastructure