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Apply your skills in Google Cloud console

Tony Ng

Member since 2021

Silver League

17185 points
Machine Learning Operations (MLOps): Getting Started Earned Jul 3, 2024 EDT
Recommendation Systems on Google Cloud Earned Jun 29, 2024 EDT
Natural Language Processing on Google Cloud Earned May 19, 2024 EDT
Machine Learning in the Enterprise Earned Apr 18, 2024 EDT
Feature Engineering Earned Mar 10, 2024 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud Earned Feb 3, 2024 EST
Launching into Machine Learning Earned Jan 7, 2024 EST
Introduction to Image Generation Earned Jun 4, 2023 EDT
Attention Mechanism Earned Jun 4, 2023 EDT
Transformer Models and BERT Model Earned Jun 4, 2023 EDT
[Accelerate 2022]: TechCon Lab Bash 2022 Earned Jan 14, 2022 EST

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. 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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In this course, you apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine. This is the fifth and final course of the Advanced Machine Learning on Google Cloud series.

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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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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 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 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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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 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 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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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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Welcome to the TechCon Lab Bash 2022 hands-on lab event! Below, you are presented with a series of labs ranging from Level 100 to Level 400. Level 100 labs are video walkthroughs of the lab content. Level 200 labs are traditional Learning Labs which provide you with step-by-step instructions. Level 300 are Challenge Labs which provide you with limited instructions and a hands-on technical scenario to solve. Level 400 are break/fix labs where you must identify the issues in the environment and resolve them.

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