This course introduces you to important concepts and terminology for working with Google Cloud Platform (GCP). You learn about, and compare, many of the computing and storage services available in Google Cloud Platform, including Google App Engine, Google Compute Engine, Google Kubernetes Engine, Google Cloud Storage, Google Cloud SQL, and BigQuery. You learn about important resource and policy management tools, such as the Google Cloud Resource Manager hierarchy and Google Cloud Identity and Access Management. Hands-on labs give you foundational skills for working with GCP.

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It is an honor I have given a chance by DSC SEA Google to learn Google cloud fundamentals. I am thankful to my manager Shad Roi for giving me this awesomeness opportunity.

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Data pipelines typically fall under one of the Extra-Load, Extract-Load-Transform or Extract-Transform-Load paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud Platform for data transformation including BigQuery, executing Spark on Cloud Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Cloud Dataflow. Learners will get hands-on experience building data pipeline components on Google Cloud Platform using QwikLabs.

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This training course introduces participants to migrating workloads to Google Cloud Platform. The course explains the strategies to migrate from the source environment to the cloud. Participants are introduced to Google Cloud Platform's fundamental concepts and more in depth topics, like managing virtual machines and configuring networks. In addition to learning how to use Google Cloud Platform, the participants learn how to leverage Google's workload mobility application, Migrate for Compute Engine. The training covers the installation and migration process, including special features like test clones and wave migrations. The course also covers topics related to governance, including resource hierarchy, user identities in the cloud, and Active Directory. Lastly, participants learn how to monitor and log their environment with Stackdriver, leverage the elasticity of the cloud with managed instance groups, and use a global load balancer.

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This is the third course of the Advanced Machine Learning on GCP specialization. In this course,
We will take a look at different strategies for building an image classifier using convolutional neural networks. We'll improve the model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting our data. We will also look at practical issues that arise, for example, when you don't have enough data and how to incorporate the latest research findings into our models.

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Incorporating machine learning into data pipelines increases the ability of businesses to extract insights from their data. This course covers several ways machine learning can be included in data pipelines on Google Cloud Platform depending on the level of customization required. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces AI Platform Notebooks and BigQuery Machine Learning. Also, this course covers how to productionalize machine learning solutions using Kubeflow. Learners will get hands-on experience building machine learning models on Google Cloud Platform using QwikLabs.

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What is cloud technology or data science and what's all the hype about? More importantly, what can it do for you, your team, and your business?

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