This is a Google Cloud Self-Paced Lab. In this lab you will use native derived tables to answer complex questions with LookML.

In many IT organizations, incentives are not aligned between developers, who strive for agility, and operators, who focus on stability. Site reliability engineering, or SRE, is how Google aligns incentives between development and operations and does mission-critical production support. Adoption of SRE cultural and technical practices can help improve collaboration between the business and IT. This course introduces key practices of Google SRE and the important role IT and business leaders play in the success of SRE organizational adoption. Primary audience: IT leaders and business leaders who are interested in embracing SRE philosophy. Roles include, but are not limited to CTO, IT director/manager, engineering VP/director/manager. Secondary audience: Other product and IT roles such as operations managers or engineers, software engineers, service managers, or product managers may also find this content useful as an introduction to SRE.

Machine learning is the foundation for predictive modeling and artificial intelligence. If you want to learn about both the underlying concepts and how to get into building models with the most common machine learning tools this path is for you. In this course, you will learn the core principles of machine learning and how to use common tools and frameworks to train, evaluate, and use machine learning models. This course is designed to prepare you for roles that include planning and creating a suitable working environment for data science workloads on Azure. You will learn how to run data experiments and train predictive models. In addition, you will manage, optimize, and deploy machine learning models into production. From the most basic classical machine learning models, to exploratory data analysis and customizing architectures, you’ll be guided by easy -to-digest conceptual content and interactive Jupyter notebooks. If you already have some idea what machine learning is about or you have a strong mathematical background this course is perfect for you. These modules teach some machine learning concepts, but move fast so they can get to the power of using tools like scikit-learn, TensorFlow, and PyTorch. This learning path is also the best one for you if you're looking for just enough familiarity to understand machine learning examples for products like Azure ML or Azure Databricks. It's also a good place to start if you plan to move beyond classic machine learning and get an education in deep learning and neural networks, which we only introduce here. This program consists of 5 courses to help prepare you to take the Exam DP-100: Designing and Implementing a Data Science Solution on Azure. The certification exam is an opportunity to prove knowledge and expertise operate machine learning solutions at cloud scale using Azure Machine Learning. This specialization teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure . Each course teaches you the concepts and skills that are measured by the exam.

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will use the APIs Explorer tool to create Cloud Storage buckets, upload data to the bucket, and remove content from buckets.

This is a self-paced lab that takes place in the Google Cloud console. A step-by-step guide through the process to configure multiple methods to ingest Google Cloud data into Splunk. In this hands-on lab you'll learn how to configure Google Cloud to send logging and other infrastructure data to Splunk Cloud via Dataflow, the Splunk Add-on for Google Cloud Platform, and Splunk Connect for Kubernetes (SC4K).

This course it the first part of the Python for Cybersecurity Specialization. Learners will get an introduction and overview of the course format and learning objectives.

This is a self-paced lab that takes place in the Google Cloud console. In this advanced lab you will create and run an Apache Airflow workflow in Cloud Composer that exports tables from a BigQuery dataset located in Cloud Storage bucktes in the US to buckets in Europe, then import th0se tables to a BigQuery dataset in Europe.

Explore the C and C++ languages. Look at the specificity of the C/C++ languages and how this impacts security, ways C/C++ can interact with the external world, error handling, the execution environment and much more.

This is a self-paced lab that takes place in the Google Cloud console. In this lab you will learn how to use Dialogflow tools to troubleshoot your Virtual Agent.

In this one-hour project, you will learn how to use the Amazon Web Services Platform and its Kubernetes Service to deploy a Web Application in a high availability environment, using the power of containers and Kubernetes in a real-world use case. Once you're done with this project, you will be able to clone a project, create a docker container image and deploy this container like a Kubernetes POD using the Elastic Kubernetes Services with just a few steps.