Dołącz Zaloguj się

Ritesh Sinha

Jest członkiem od 2023

Liga brązowa

3110 pkt.
Production Machine Learning Systems Earned wrz 11, 2023 EDT
Machine Learning Operations (MLOps): Getting Started Earned wrz 2, 2023 EDT
Recommendation Systems on Google Cloud Earned sie 30, 2023 EDT
Natural Language Processing on Google Cloud Earned sie 29, 2023 EDT
[DEPRECATED] Feature Engineering Earned sie 26, 2023 EDT
Machine Learning in the Enterprise Earned sie 20, 2023 EDT
[Depricated] Build, Train and Deploy ML Models with Keras on Google Cloud Earned sie 8, 2023 EDT
Launching into Machine Learning Earned sie 1, 2023 EDT
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals Earned lip 30, 2023 EDT
How Google Does Machine Learning Earned lip 13, 2023 EDT
Introduction to Responsible AI - Polski Earned lip 11, 2023 EDT
Introduction to Large Language Models - Polski Earned cze 29, 2023 EDT
Introduction to Generative AI - Polski Earned cze 29, 2023 EDT

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.

Więcej informacji

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.

Więcej informacji

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.

Więcej informacji

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.

Więcej informacji

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.

Więcej informacji

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.

Więcej informacji

This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.

Więcej informacji

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.

Więcej informacji

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.

Więcej informacji

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.

Więcej informacji

Celem tego szybkiego szkolenia dla początkujących jest wyjaśnienie, czym jest odpowiedzialna AI i dlaczego jest ważna, oraz przedstawienie, jak Google wprowadza ją w swoich usługach. Szkolenie zawiera także wprowadzenie do siedmiu zasad Google dotyczących sztucznej inteligencji.

Więcej informacji

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.

Więcej informacji

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.

Więcej informacji