Cloud Platform Fundamentals
 

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

Are you wondering which is the best Cloud Platform for the data strategy of your business? Join our 3-day Cloud Platform training in which we cover the big three.

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. This is where Amazon (AWS), Microsoft (Azure) and Google (GCP) come in. But which one is best for your needs?

These classes are perfect for companies of all sizes that want to close the data gap and train their employees. You can follow the schedule below in our offices or contact us for a tailor-made program that meets your needs.

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

Close the Gap with this Cloud Platform training

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What you will learn

Participants in this training typically have a background in software or data engineering or IT architect. In order to get most value out of the practical part of the training, you should understand the fundamentals of file storage, databases and virtual machines and have some command line experience (ssh connections, keys, etc). Basic knowledge about machine learning workflow comes in handy. 

During the training you will learn: 

  • The foundation to build a data platform in AWS, Azure and GCP;
  • Setting up storage, access management and monitoring; 
  • Apply Data and Machine Learning technologies in public cloud. 

After the training you receive a Certificate of Completion. 

Training Dates

This training consists of 3 Classes. Join the Cloud Platform Training on one of these dates in our office or contact us for a tailored training:

Class

  • AWS Cloud
  • Google Cloud Platform
  • Microsoft Azure

Available Dates

May 15, 2020
July 03, 2020  |  October 09, 2020
August 07, 2020  |  November 13, 2020

Detailed description of the Classes

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
Google Cloud Platform 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 via Kubeflow 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
  • Kubeflow
  • Pub/Sub
  • Lab session to get hands-on experience with Google Cloud Platform 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

For more information or to book your training

Are you interested in the Cloud Platform Fundamentals training or do you have questions? Fill out the form and we will contact you personally.