María Fernández
Menjadi anggota sejak 2023
Memodernisasi Infrastruktur dan Aplikasi dengan Google Cloud
Earned Mar 5, 2023 EST
Penskalaan dengan Google Cloud Operations
Earned Mar 3, 2023 EST
Transformasi Digital dengan Google Cloud
Earned Mar 3, 2023 EST
Menjelajahi Transformasi Data dengan Google Cloud
Earned Mar 2, 2023 EST
Build Batch Data Pipelines on Google Cloud
Earned Mar 1, 2023 EST
[DEPRECATED] Feature Engineering
Earned Mar 1, 2023 EST
Machine Learning in the Enterprise
Earned Mar 1, 2023 EST
Launching into Machine Learning
Earned Feb 28, 2023 EST
How Google Does Machine Learning
Earned Feb 26, 2023 EST
[Depricated] Build, Train and Deploy ML Models with Keras on Google Cloud
Earned Feb 26, 2023 EST
Natural Language Processing on Google Cloud
Earned Feb 26, 2023 EST
Analyzing and Visualizing Data in Looker
Earned Feb 21, 2023 EST
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Feb 13, 2023 EST
Creating New BigQuery Datasets and Visualizing Insights
Earned Feb 9, 2023 EST
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals
Earned Feb 3, 2023 EST
Running SAP Workloads on Google Cloud: Fundamentals
Earned Jan 24, 2023 EST
Banyak perusahaan tradisional menggunakan sistem dan aplikasi lama yang tidak dapat mengikuti ekspektasi pelanggan modern. Para pemimpin bisnis sering kali harus memilih antara mempertahankan sistem IT lama mereka atau berinvestasi pada produk dan layanan baru. Kursus ini mengeksplorasi tantangan tersebut dan menawarkan solusi untuk mengatasinya dengan menggunakan teknologi cloud.
Sebagai bagian dari alur pembelajaran Cloud Digital Leader, kursus ini bertujuan membantu individu berkembang dalam peran mereka dan membangun masa depan bisnis mereka.
Pelajari lebih lanjut
Organisasi dalam berbagai ukuran memanfaatkan kekuatan dan fleksibilitas cloud untuk mentransformasi cara mereka beroperasi. Namun, pengelolaan dan penskalaan resource cloud secara efektif bisa menjadi tugas yang rumit. Kursus ini membahas konsep dasar operasi modern, keandalan, dan ketahanan di cloud, serta cara Google Cloud dapat membantu mendukung upaya ini.
Sebagai bagian dari alur pembelajaran Cloud Digital Leader, kursus ini bertujuan membantu individu berkembang dalam peran mereka dan membangun masa depan bisnis mereka.
Pelajari lebih lanjut
Transformasi digital adalah perjalanan penting bagi organisasi modern, dan membangun dasar yang kuat dalam cloud computing merupakan langkah pertama untuk mendorong inovasi yang bermakna. Transformasi Digital dengan Google Cloud memperkenalkan teknologi inti dan framework strategis yang membantu organisasi memodernisasi operasi mereka. Kursus ini membahas konsep cloud dasar, infrastruktur jaringan global, dan model tanggung jawab bersama untuk membantu para pemimpin menentukan jalur mereka ke cloud dengan percaya diri.
Sebagai bagian dari alur pembelajaran Cloud Digital Leader, kursus ini bertujuan membantu individu berkembang dalam peran mereka dan membangun masa depan bisnis mereka.
Pelajari lebih lanjut
Teknologi cloud adalah aset yang sangat canggih. Jika dipasangkan dengan data, teknologi ini akan menjadi pendorong inovasi dan peningkatan pengalaman pelanggan. Menjelajahi Transformasi Data dengan Google Cloud membahas cara organisasi memanfaatkan cloud untuk membuat data mereka lebih mudah diakses, ditindaklanjuti, dan bernilai.
Sebagai bagian dari alur pembelajaran Cloud Digital Leader, kursus ini bertujuan membantu individu berkembang dalam peran mereka dan membangun masa depan bisnis mereka.
Pelajari lebih lanjut
In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting.
Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.
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This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.
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This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases. The team must understand the tools required for data management and governance and consider the best approach for data preprocessing.
The team is presented with three options to build ML models for two use cases. The course explains why they would use AutoML, BigQuery ML, or custom training to achieve their objectives.
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The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.
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This course explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models.
The course discusses the five phases of converting a candidate use case to be driven by machine learning, and why it’s important to not skip them. The course ends with recognizing the biases that ML can amplify and how to recognize them.
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This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.
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This course introduces the products and solutions to solve NLP problems on Google Cloud. Additionally, it explores the processes, techniques, and tools to develop an NLP project with neural networks by using Vertex AI and TensorFlow.
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In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done primarily by SQL developers or analysts. Upon completion of this course, you will be able to leverage Looker's modern analytics platform to find and explore relevant content in your organization’s Looker instance, ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision making.
Pelajari lebih lanjut
Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.
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This is the second course in the Data to Insights course series. Here we will cover how to ingest new external datasets into BigQuery and visualize them with Looker Studio. We will also cover intermediate SQL concepts like multi-table JOINs and UNIONs which will allow you to analyze data across multiple data sources.
Note: Even if you have a background in SQL, there are BigQuery specifics (like handling query cache and table wildcards) that may be new to you.
After completing this course, enroll in the Achieving Advanced Insights with BigQuery course.
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This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
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Ready to build an AI-ready future for your SAP applications? Modernization isn't just about moving to the cloud anymore—it’s about centralizing your enterprise data so AI agents have the context to make accurate and real-time decisions. In this course, you'll walk away with a clear mental model of the different options to deploy your SAP landscape, centralize your enterprise data, and build impactful agentic solutions.
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