Which of the following are true about the use of machine learning in a cloud...

Machine learning is a subset of artificial intelligence that enables a system to autonomously learn and improve using neural networks and deep learning, without being explicitly programmed, by feeding it large amounts of data1. Machine learning can be used in a cloud environment to leverage the benefits of cloud computing, such as scalability, flexibility, and cost-effectiveness. Some of the ways that machine learning can use cloud processes are:

Specialized machine learning algorithms can be deployed to optimize results for specific scenarios. Depending on the use case, an organization may choose different cloud services to support their machine learning projects, such as artificial intelligence as a service (AIaaS) or GPU as a service (GPUaaS)2. AIaaS provides pre-trained models for common tasks, such as image recognition, natural language processing, or sentiment analysis, while GPUaaS provides access to high-performance computing resources for training custom models. These services can help organizations achieve better results faster and more efficiently.

Machine learning can leverage processes in a cloud environment through the use of cloud storage and auto-scaling. Cloud storage provides a scalable and secure way to store and access large amounts of data, which is essential for machine learning. Cloud storage also enables data integration and collaboration across different sources and platforms3. Auto-scaling is a feature of cloud computing that automatically adjusts the amount of resources allocated to a machine learning application based on the demand and workload. This helps optimize the performance and cost of machine learning in the cloud4.

The other options are false because:

Machine learning can just be hosted in the cloud for managed services. This is not true because machine learning can also be used in a hybrid or multi-cloud environment, where some components of the machine learning project are hosted on-premises or on different cloud providers. This can provide more flexibility and control over the machine learning process, as well as address security and compliance issues2.

Just one type of cloud storage is available in the cloud for machine learning workloads. This is not true because there are different types of cloud storage available for machine learning workloads, such as object storage, block storage, or file storage. Each type of storage has its own advantages and disadvantages, depending on the data format, size, and access frequency. For example, object storage is suitable for storing unstructured data, such as images or videos, while block storage is suitable for storing structured data, such as databases or files3.

Machine learning requires a specialized IT team to create the machine learning models from scratch. This is not true because machine learning does not always require a specialized IT team to create the models from scratch. There are many tools and services available in the cloud that can help simplify and automate the machine learning process, such as data preparation, modelbuilding, testing, deployment, and monitoring. For example, Google Cloud AutoML is a service that allows users to create custom machine learning models with minimal coding and expertise4.

Using machine learning solutions in the cloud removes the data-gathering step from the learning process. This is not true because using machine learning solutions in the cloud does not remove the data-gathering step from the learning process. Data-gathering is a crucial step in machine learning, as it provides the input for the machine learning models to learn from. Data-gathering involves collecting, cleaning, labeling, and transforming data from various sources, such as sensors, databases, or web pages. Using machine learning solutions in the cloud can help with data-gathering, but it does not eliminate it3.