GCP Data Engineer Training in Ameerpet | GCP Training in Hyderabad

Google Cloud Big Data and Machine Learning Fundamentals

Google Cloud Data Engineer is a professional responsible for designing, building, maintaining, and optimizing data processing systems on the Google Cloud Platform (GCP). Google Cloud offers a variety of services for big data and machine learning that enable users to process and analyze large datasets and build advanced machine learning models. - GCP Data Engineer Online Course

Here's an overview of the fundamentals in Google Cloud's offerings for big data and machine learning:

Big Data:

1. Google Cloud Storage: Google Cloud Storage provides a scalable and durable object storage solution for storing and retrieving data. It is commonly used as a data lake for big data analytics.

2. BigQuery: BigQuery is a fully-managed, serverless data warehouse that enables super-fast SQL queries using the processing power of Google's infrastructure. It's suitable for interactive analysis of large datasets.

3. Dataprep: Cloud Dataprep by Trifacta is a cloud-based service for exploring, cleaning, and preparing large datasets for analysis. It helps transform raw data into a format suitable for analytics. - Google Cloud Data Engineer Training

4. Dataflow: Apache Beam, which is the basis for Google Cloud Dataflow, allows you to design and execute both batch and streaming data processing pipelines. Dataflow is a fully-managed service for stream and batch processing.

5. Dataproc: Cloud Dataproc is a fast, easy-to-use, fully-managed cloud service for running Apache Spark and Apache Hadoop clusters. It simplifies the process of deploying and managing big data clusters.

6. Composer: Cloud Composer is a fully-managed workflow orchestration service built on Apache Airflow. It allows you to automate, schedule, and monitor workflows across various Google Cloud services. - GCP Online Training

Machine Learning:

1. AI Platform: Google Cloud AI Platform provides tools and services for building, training, and deploying machine learning models at scale. It includes features like AutoML for automating model training and deployment.

2. TensorFlow and PyTorch: Google Cloud supports popular machine learning frameworks like TensorFlow and PyTorch. Users can run distributed training on Google Cloud infrastructure for large-scale model training.

3. Vertex AI: Vertex AI is a unified AI platform that enables users to build, deploy, and manage machine learning models. It supports multiple frameworks and provides tools for data preprocessing, model training, and deployment. - GCP Data Engineer Training in Ameerpet

4. AI Hub: AI Hub is a centralized repository for sharing and discovering machine learning resources such as pre-trained models, notebooks, and pipelines. It fosters collaboration among data scientists and ML engineers.

5. Vision AI and Video AI: Google Cloud offers Vision AI and Video AI APIs for computer vision tasks. These APIs can be used to analyze images and videos, extract information, and detect objects and activities.

 

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