François Chung, Ph.D.

Tag: cloud computing

Google Cloud: big data and machine learning

Google Cloud: big data and machine learning

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Coursera training, MOOC (2020). This online training introduces the big data and machine learning (ML) capabilities of Google Cloud Platform (CGP). Through a combination of presentations, demos and hands-on labs, the training gives an overview of Google Cloud and a detailed view of the data processing and ML solutions, such as BigQuery, Cloud SQL, Dataproc, Pub/Sub, Dataflow and Data Studio.

Week 1: Big data and ML fundamentals

Main topics:

  • Exploring a BigQuery public dataset;
  • Choosing the right solution approach;
  • Recommending products using Cloud SQL and Spark;
  • Predicting visitor purchases using BigQuery ML.

Week 2: Modern data pipeline challenges

Main topics:

  • Real-time IoT dashboards;
  • Creating a streaming data pipeline;
  • ML on unstructured datasets;
  • Classifying images with pre-built ML models.

References

Azure: fundamentals, machine learning and Power BI

Azure: fundamentals, machine learning and Power BI

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Microsoft Docs training, MOOC (2020). These 3 online courses present Microsoft Azure and Power BI. The training teaches the basic cloud concepts, along with hands-on exercises, and provides an overview of Azure services, such as Azure Machine Learning (ML), which is a cloud platform for training, deploying, managing and monitoring ML models. Furthermore, the training explains how to use Power BI and build business intelligence reports.

Course 1: Azure fundamentals

Main topics:

  • Principles of cloud computing;
  • Azure architecture and service guarantees;
  • Compute, data storage and networking;
  • Security, responsibility and trust;
  • Infrastructure standards with Azure Policy;
  • Azure resources with Azure Resource Manager.

Course 2: Azure machine learning

Main topics:

  • Working with data;
  • Orchestrate ML with pipelines;
  • Deploy ML models;
  • Automate model selection;
  • Tune hyperparameters;
  • Monitor models and data drift.

Course 3: Power BI

Main topics:

  • Get started building with Power BI;
  • Get data with Power BI Desktop;
  • Model and explore data;
  • Use visuals, publish and share.

References

Training

Microsoft Docs (badges and trophies)

Related articles

Google Cloud: big data and machine learning (Coursera training)
AWS: foundations and machine learning (AWS training)
Neural networks and deep learning (Coursera training)

Learn more

Microsoft Docs (Azure fundamentals)
Microsoft Docs (Azure machine learning)
Microsoft Docs (Power BI)

AWS: foundations and machine learning

AWS: foundations and machine learning

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AWS training, MOOC (2020). These online courses provide an overall understanding of AWS cloud, with an overview of cloud concepts, services, security, architecture, pricing and support. Specific courses to AWS partners teach best practices to address customer business priorities around costs, agility, compliance, innovation and growth. Machine Learning (ML) is also covered, with a deep dive into the same ML curriculum used to train Amazon’s developers and data scientists.

AWS Cloud Practitioner Essentials

Main topics:

  • AWS core services;
  • AWS integrated services;
  • AWS architecture;
  • AWS security;
  • Pricing and support.

AWS Partner Solutions: Business foundations

Main topics:

  • Build your business with AWS;
  • What matters to AWS customers;
  • Security, identity and compliance;
  • Pricing and licensing;
  • Migration and cloud adoption;
  • Opportunity management.

AWS Partner Solutions: Technical foundations

Main topics:

  • AWS solution architects;
  • AWS architectural concepts;
  • Building blocks;
  • AWS Well-Architected Framework;
  • Architecting an AWS solution;
  • Engaging customers and architecting solutions.

AWS Machine Learning: Decision maker

Main topics:

  • Demystifying AI/ML/DL;
  • ML for business challenges;
  • ML terminology;
  • Exploring the ML toolset.

AWS Machine Learning: Data scientist

Main topics:

  • Math for ML;
  • Linear and logistic regression;
  • Elements of data science;
  • Real world ML decisions.

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