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Georgii Zemlianyi

Jest członkiem od 2023

Liga srebrna

42770 pkt.
Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation Earned kwi 8, 2025 EDT
Machine Learning Operations (MLOps) for Generative AI Earned kwi 8, 2025 EDT
Vertex AI Search for Commerce Earned kwi 8, 2025 EDT
BI Reporting: Looker Visualization on BigQuery Earned sty 30, 2025 EST
Machine Learning Operations (MLOps) with Vertex AI: Manage Features Earned sty 28, 2025 EST
Machine Learning in the Enterprise Earned sty 28, 2025 EST
Introduction to AI and Machine Learning on Google Cloud Earned sty 27, 2025 EST
Build, Train and Deploy ML Models with Keras on Google Cloud Earned sty 24, 2025 EST
Serverless Data Processing with Dataflow: Operations Earned paź 28, 2024 EDT
Production Machine Learning Systems Earned sie 28, 2024 EDT
Feature Engineering Earned lip 27, 2024 EDT
Serverless Data Processing with Dataflow: Develop Pipelines Earned kwi 1, 2024 EDT
Serverless Data Processing with Dataflow: Foundations Earned mar 16, 2024 EDT
Building Resilient Streaming Systems on Google Cloud Platform Earned mar 11, 2024 EDT
Modernizing Data Lakes and Data Warehouses with Google Cloud Earned lut 26, 2024 EST
Data Lake Modernization on Google Cloud: Cloud Data Fusion Earned sty 30, 2024 EST
Data Lake Modernization on Google Cloud Earned sty 30, 2024 EST
Data Lake Modernization on Google Cloud: Migrate Workflows Earned sty 21, 2024 EST
Data Lake Modernization on Google Cloud: Cloud Composer Earned sty 19, 2024 EST
Data Lake Modernization on Google Cloud: Data Governance Earned sty 19, 2024 EST
Data Lake Modernization on Google Cloud: Intro to Data Lakes Earned sty 16, 2024 EST
Migrate Cloudera to Google Cloud Earned sty 3, 2024 EST
Cloudera to Google Cloud Earned lis 30, 2023 EST
Encoder-Decoder Architecture Earned maj 24, 2023 EDT
Create Image Captioning Models Earned maj 24, 2023 EDT
Introduction to Image Generation Earned maj 24, 2023 EDT
Transformer Models and BERT Model Earned maj 24, 2023 EDT
Attention Mechanism Earned maj 23, 2023 EDT
Introduction to Large Language Models - Polski Earned maj 23, 2023 EDT
Introduction to Generative AI - Polski Earned maj 23, 2023 EDT

This course equips machine learning practitioners with the essential tools, techniques, and best practices for evaluating both generative and predictive AI models. Model evaluation is a critical discipline for ensuring that ML systems deliver reliable, accurate, and high-performing results in production. Participants will gain a deep understanding of various evaluation metrics, methodologies, and their appropriate application across different model types and tasks. The course will emphasize the unique challenges posed by generative AI models and provide strategies for tackling them effectively. By leveraging Google Cloud's Vertex AI platform, participants will learn how to implement robust evaluation processes for model selection, optimization, and continuous monitoring.

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This course is dedicated to equipping you with the knowledge and tools needed to uncover the unique challenges faced by MLOps teams when deploying and managing Generative AI models, and exploring how Vertex AI empowers AI teams to streamline MLOps processes and achieve success in Generative AI projects.

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This on-demand course provides partners the skills required to design, deploy, and monitor Vertail AI Search for Commerce solutions including retail search and recommendation AI for enterprise customers.

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This workload aims to upskill Google Cloud partners to perform specific tasks for modernization using LookML on BigQuery. A proof-of-concept will take learners through the process of creating LookML visualizations on BigQuery. During this course, learners will be guided specifically on how to write Looker modeling language, also known as LookML and create semantic data models, and learn how LookML constructs SQL queries against BigQuery. At a high level, this course will focus on basic LookML to create and access BigQuery objects, and optimize BigQuery objects with LookML.

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This 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. Learners will get hands-on practice using Vertex AI Feature Store's streaming ingestion at the SDK layer.

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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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This course introduces the AI and machine learning (ML) offerings on Google Cloud that build both predictive and generative AI projects. It explores the technologies, products, and tools available throughout the data-to-AI life cycle, encompassing AI foundations, development, and solutions. It aims to help data scientists, AI developers, and ML engineers enhance their skills and knowledge through engaging learning experiences and practical hands-on exercises.

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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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In the last installment of the Dataflow course series, we will introduce the components of the Dataflow operational model. We will examine tools and techniques for troubleshooting and optimizing pipeline performance. We will then review testing, deployment, and reliability best practices for Dataflow pipelines. We will conclude with a review of Templates, which makes it easy to scale Dataflow pipelines to organizations with hundreds of users. These lessons will help ensure that your data platform is stable and resilient to unanticipated circumstances.

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This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators. This is the second course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Image Understanding with TensorFlow on Google Cloud course.

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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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In this second installment of the Dataflow course series, we are going to be diving deeper on developing pipelines using the Beam SDK. We start with a review of Apache Beam concepts. Next, we discuss processing streaming data using windows, watermarks and triggers. We then cover options for sources and sinks in your pipelines, schemas to express your structured data, and how to do stateful transformations using State and Timer APIs. We move onto reviewing best practices that help maximize your pipeline performance. Towards the end of the course, we introduce SQL and Dataframes to represent your business logic in Beam and how to iteratively develop pipelines using Beam notebooks.

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This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.

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This 1-week, accelerated on-demand course builds upon Google Cloud Platform Big Data and Machine Learning Fundamentals. Through a combination of video lectures, demonstrations, and hands-on labs, you'll learn to build streaming data pipelines using Google cloud Pub/Sub and Dataflow to enable real-time decision making. You will also learn how to build dashboards to render tailored output for various stakeholder audiences.

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The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment. This is the first course of the Data Engineering on Google Cloud series. After completing this course, enroll in the Building Batch Data Pipelines on Google Cloud course.

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Welcome to Cloud Data Fusion, where we discuss how to use Cloud Data Fusion to build complex data pipelines.

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This course focuses on how you can bring your on-premises data lakes and workloads to Google Cloud to unlock cost savings and scale.

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Welcome to Migrate Workflows, where we discuss how to migrate Spark and Hadoop tasks and workflows to Google Cloud.

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Welcome to Cloud Composer, where we discuss how to orchestrate data lake workflows with Cloud Composer.

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Welcome to Data Governance, where we discuss how to implement data governance on Google Cloud.

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Welcome to Intro to Data Lakes, where we discuss how to create a scalable and secure data lake on Google Cloud that allows enterprises to ingest, store, process, and analyze any type or volume of full fidelity data.

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This skill badge aims to evaluate a partner's ability to migrate data from Cloudera to Google Cloud Platform. Learners will gain hands-on experience through labs and achieve comprehensive knowledge and practical skills for migrating data from Cloudera to Google Cloud Platform.

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This workload aims to upskill Google Cloud partners to perform specific tasks associated with priority workloads. Learners will perform the tasks of migrating data from five products hosted on Cloudera or Hortonworks to corresponding Google Cloud services and hosted products. The migration solutions addressed will be: HDFS data to Google Cloud Dataproc and Cloud Storage Hive data to Cloud Dataproc and the Cloud Dataproc Metastore Hive data to Google Cloud BigQuery Impala data to Google Cloud BigQuery HBase to Google Cloud Bigtable Sample data will be used during all five migrations. Learners will complete several labs that focus on the process of transferring schema, data and related processes to corresponding Google Cloud products.There will be one or more challenge labs that will test the learners understanding of the topics.

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This course gives you a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, you’ll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.

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This course teaches you how to create an image captioning model by using deep learning. You learn about the different components of an image captioning model, such as the encoder and decoder, and how to train and evaluate your model. By the end of this course, you will be able to create your own image captioning models and use them to generate captions for images

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This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI.

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This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference.This course is estimated to take approximately 45 minutes to complete.

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This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering. This course is estimated to take approximately 45 minutes to complete.

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To szybkie szkolenie dla początkujących wyjaśnia, czym są duże modele językowe (LLM) oraz jakie są ich zastosowania. Przedstawia również możliwości zwiększenia ich wydajności przez dostrajanie przy użyciu promptów oraz narzędzia Google, które pomogą Ci tworzyć własne aplikacje korzystające z generatywnej AI.

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Celem tego szybkiego szkolenia dla początkujących jest wyjaśnienie, czym jest generatywna AI oraz jakie są jej zastosowania. Szkolenie przedstawia również różnice pomiędzy tą technologią a tradycyjnymi systemami uczącymi się, a także narzędzia Google, które pomogą Ci tworzyć własne aplikacje korzystające z generatywnej AI.

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