What is machine learning, and what kinds of problems can it solve? Why are neural networks so popular right now? How can you improve data quality and perform exploratory data analysis? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent? In this course, you’ll learn how to write distributed machine learning models that scale in Tensorflow 2.x, perform feature engineering in BQML and Keras, evaluate loss curves and perform hyperparameter tuning, and train models at scale with Cloud AI Platform.
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In this GCP-MPGC: ML Pipelines on Google Cloud course, you will learn about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. The first few modules discuss pipeline components, pipeline orchestration with TFX, how you can automate your pipeline through CI/CD, and how to manage ML metadata.
Then we will discuss how to automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use Cloud Composer to orchestrate your continuous training pipelines, and MLflow for managing the complete machine learning life cycle.
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This GCP-MLOF: MLOps (Machine Learning Operations) Fundamentals course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production.
Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
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The Logging, Monitoring and Observability in Google Cloud training course teaches participants techniques for monitoring, troubleshooting, and improving infrastructure and application performance in Google Cloud.
Learn how to monitor, troubleshoot, and improve your infrastructure and application performance. Guided by the principles of Site Reliability Engineering (SRE), this official Google Cloud course features a combination of lectures, demos, hands-on labs, and real-world case studies. In this course, you’ll gain experience with full-stack monitoring, real-time log management and analysis, debugging code in production, and profiling CPU and memory usage.
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In this course, you’ll learn the fundamentals and best practices of SRE, the importance of adopting an SRE culture, and how SRE can improve collaboration between IT and business leaders—and help the entire organization succeed.
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The most automated and scalable managed Kubernetes platform.
Learn how to create and deploy containerized applications on Google Kubernetes Engine (GKE). This GCP-GSGKE: Getting Started with Google Kubernetes Engine course features a combination of lectures, demos, and hands-on labs to help you explore and deploy solution elements —including infrastructure components like pods, and containers.
Are you currently retrenched? If yes, check out our PERKESO EIS: Get Back into the Workforce through Upskilling program.
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This GCP-AMLTF: Advanced Machine Learning with TensorFlow on Google Cloud course will give you hands-on experience optimizing, deploying, and scaling a variety of production ML models. You’ll learn how to build recommendation systems
and scalable, accurate, and production-ready models for structured data, image data, time series, and natural language text. -
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This course introduces the artificial intelligence (AI) and machine learning (ML) offerings on Google Cloud that support the data-to-AI lifecycle through AI foundations, AI development, and AI solutions.
It explores the technologies, products, and tools available to build an ML model, an ML pipeline, and a generative AI project. You learn how to build AutoML models without writing a single line of code; build BigQuery ML models using SQL, and build Vertex AI custom training jobs by using Keras and TensorFlow. You also explore data preprocessing techniques
and feature engineering.Take 40% off this in-demand course – now only RM5,760 to level up your ML skills.
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Discover how to modernize, manage, and observe applications using Kubernetes—whether the application is deployed on-premises or on Google Cloud. This course uses lectures and hands-on labs to help you explore and deploy using Kubernetes Engine (GKE), GKE Connect, Istio service mesh, and Anthos Config Management capabilities that will enable you to work with modern applications, even when they are split among multiple clusters hosted by multiple providers, or on-premises. This is a continuation of Architecting with GKE, and you’ll need hands-on experience with the technologies covered in that course in order to benefit from this course.
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Learn how to design, develop, and deploy applications that seamlessly integrate components from the Google Cloud ecosystem. This course uses lectures, demos, and hands-on labs to show you how to use Google Cloud services and pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native applications.
In this course, application developers learn how to design and develop cloud-native applications that seamlessly integrate managed services from Google Cloud. Participants learn how to apply best practices for application development and use the appropriate Google Cloud storage services for object storage, relational data, caching, and analytics.
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Learn best practices in cloud security and how the Google Cloud security model can help protect your technology stack. Security Engineers actively assess existing Google Cloud implementations, identifying potential security issues, and prioritizing solutions.
This Google Cloud certification uses lectures, demos, and hands-on labs to teach you about a variety of Google Cloud security controls and techniques. You’ll explore the components of Google Cloud and deploy a secure solution on the platform. You’ll also learn how to mitigate attacks at several points in a Google Cloud-based infrastructure, including distributed denial-of-service attacks, phishing attacks, and threats involving content classification and use.
Explore more about cybersecurity certifications with our cybersecurity training and certifications guide.
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This official Google Cloud course will help you prepare for the Google Professional Data Engineer certification exam. This session uses lectures, quizzes, and discussions to help you become familiar with the structure and format of the exam. You’ll take part in exam question reasoning and case comprehension, get tips from certified experts, and review topics from the Data Engineering curriculum.
At the end of the course, you’ll take an ungraded practice exam and then a graded practice exam that will simulate the exam-taking experience.
