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Wykorzystuj swoje umiejętności w konsoli Google Cloud

Asad Enver

Jest członkiem od 2024

Liga brązowa

9270 pkt.
Build a Data Warehouse with BigQuery Earned gru 14, 2024 EST
Work with Gemini Models in BigQuery Earned lis 10, 2024 EST
Engineer Data for Predictive Modeling with BigQuery ML Earned paź 27, 2024 EDT
Introduction to Large Language Models - Polski Earned paź 13, 2024 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud Earned paź 13, 2024 EDT
Vector Search and Embeddings Earned paź 4, 2024 EDT
Inspect Rich Documents with Gemini Multimodality and Multimodal RAG Earned wrz 30, 2024 EDT

Complete the intermediate Build a Data Warehouse with BigQuery skill badge course to demonstrate skills in the following: joining data to create new tables, troubleshooting joins, appending data with unions, creating date-partitioned tables, and working with JSON, arrays, and structs in BigQuery.

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This course demonstrates how to use AI/ML models for generative AI tasks in BigQuery. Through a practical use case involving customer relationship management, you learn the workflow of solving a business problem with Gemini models. To facilitate comprehension, the course also provides step-by-step guidance through coding solutions using both SQL queries and Python notebooks.

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Complete the intermediate Engineer Data for Predictive Modeling with BigQuery ML skill badge to demonstrate skills in the following: building data transformation pipelines to BigQuery using Dataprep by Trifacta; using Cloud Storage, Dataflow, and BigQuery to build extract, transform, and load (ETL) workflows; and building machine learning models using BigQuery ML.

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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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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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Explore AI-powered search technologies, tools, and applications in this course. Learn semantic search utilizing vector embeddings, hybrid search combining semantic and keyword approaches, and retrieval-augmented generation (RAG) minimizing AI hallucinations as a grounded AI agent. Gain practical experience with Vertex AI Vector Search to build your intelligent search engine.

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Complete the intermediate Inspect Rich Documents with Gemini Multimodality and Multimodal RAG skill badge course to demonstrate skills in the following: using multimodal prompts to extract information from text and visual data, generating a video description, and retrieving extra information beyond the video using multimodality with Gemini; building metadata of documents containing text and images, getting all relevant text chunks, and printing citations by using Multimodal Retrieval Augmented Generation (RAG) with Gemini.

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