There are a number of terms and concepts in cloud computing, and not everyone is familiar with all of them. To help, we’ve put together a list of common questions, and the meanings of a few of those acronyms. You can find all these, and many more, in our learning resources.

What are containers?

Containers are packages of software that contain all of the necessary elements to run in any environment. In this way, containers virtualize the operating system and run anywhere, from a private data center to the public cloud or even on a developer’s personal laptop. Containerization allows development teams to move fast, deploy software efficiently, and operate at an unprecedented scale. Read more.

Containers vs. VMs: What’s the difference?

You might already be familiar with VMs: a guest operating system such as Linux or Windows runs on top of a host operating system with access to the underlying hardware. Containers are often compared to virtual machines (VMs). Like virtual machines, containers allow you to package your application together with libraries and other dependencies, providing isolated environments for running your software services. However, the similarities end here as containers offer a far more lightweight unit for developers and IT Ops teams to work with, carrying a myriad of benefits. Containers are much more lightweight than VMs, virtualize at the OS level while VMs virtualize at the hardware level, and share the OS kernel and use a fraction of the memory VMs require. Read more.

What is Kubernetes?

With the widespread adoption of containers among organizations, Kubernetes, the container-centric management software, has become the de facto standard to deploy and operate containerized applications. Google Cloud is the birthplace of Kubernetes—originally developed at Google and released as open source in 2014. Kubernetes builds on 15 years of running Google’s containerized workloads and the valuable contributions from the open source community. Inspired by Google’s internal cluster management system, Borg, Kubernetes makes everything associated with deploying and managing your application easier. Providing automated container orchestration, Kubernetes improves your reliability and reduces the time and resources attributed to daily operations. Read more.

What is microservices architecture?

Microservices architecture (often shortened to microservices) refers to an architectural style for developing applications. Microservices allow a large application to be separated into smaller independent parts, with each part having its own realm of responsibility. To serve a single user request, a microservices-based application can call on many internal microservices to compose its response. Containers are a well-suited microservices architecture example, since they let you focus on developing the services without worrying about the dependencies. Modern cloud-native applications are usually built as microservices using containers. Read more.

What is ETL?

ETL stands for extract, transform, and load and is a traditionally accepted way for organizations to combine data from multiple systems into a single database, data store, data warehouse, or data lake. ETL can be used to store legacy data, or—as is more typical today—aggregate data to analyze and drive business decisions. Organizations have been using ETL for decades. But what’s new is that both the sources of data, as well as the target databases, are now moving to the cloud. Additionally, we’re seeing the emergence of streaming ETL pipelines, which are now unified alongside batch pipelines—that is, pipelines handling continuous streams of data in real time versus data handled in aggregate batches. Some enterprises run continuous streaming processes with batch backfill or reprocessing pipelines woven into the mix. Read more.

What is a data lake?

A data lake is a centralized repository designed to store, process, and secure large amounts of structured, semistructured, and unstructured data. It can store data in its native format and process any variety of it, ignoring size limits. Read more.

What is a data warehouse?

Data-driven companies require robust solutions for managing and analyzing large quantities of data across their organizations. These systems must be scalable, reliable, and secure enough for regulated industries, as well as flexible enough to support a wide variety of data types and use cases. The requirements go way beyond the capabilities of any traditional database. That’s where the data warehouse comes in. A data warehouse is an enterprise system used for the analysis and reporting of structured and semi-structured data from multiple sources, such as point-of-sale transactions, marketing automation, customer relationship management, and more. A data warehouse is suited for ad hoc analysis as well custom reporting and can store both current and historical data in one place. It is designed to give a long-range view of data over time, making it a primary component of business intelligence. Read more.

What is streaming analytics?

Streaming analytics is the processing and analyzing of data records continuously rather than in batches. Generally, streaming analytics is useful for the types of data sources that send data in small sizes (often in kilobytes) in a continuous flow as the data is generated. Read more.

What is machine learning (ML)?

Today’s enterprises are bombarded with data. To drive better business decisions, they have to make sense of it. But the sheer volume coupled with complexity makes data difficult to analyze using traditional tools. Building, testing, iterating, and deploying analytical models for identifying patterns and insights in data eats up employees’ time. Then after being deployed, such models also have to be monitored and continually adjusted as the market situation or the data itself changes. Machine learning is the solution. Machine learning allows businesses to enable the data to teach the system how to solve the problem at hand with machine learning algorithms—and how to get better over time. Read more.

What is natural language processing (NLP)?

Natural language processing (NLP) uses machine learning to reveal the structure and meaning of text. With natural language processing applications, organizations can analyze text and extract information about people, places, and events to better understand social media sentiment and customer conversations. Read more.

Learn more

This is just a sampling of frequently asked questions about cloud computing. To learn more, visit our resources page at cloud.google.com/learn.

First, What is Google Cloud Talent Solution?

Cloud Talent Solution is a service that brings machine learning to the job search experience, returning high quality results to job seekers far beyond the limitations of typical keyword-based methods. Once integrated with your job content, Cloud Talent Solution automatically detects and infers various kinds of data, such as related titles, seniority, and industry.

Show Me How it Works

Try it online now.

Show Me an Example of Who’s Using It

There’s a number of enterprises leveraging this service. Here are a few easy-to-watch examples

Watch how FedEx Ground Employs Google Cloud Talent Solution

Read how Johnson & Johnson is Reimagining Recruiting with Jibe and Google

How Much Does it Cost?

Ok, Let’s See How it Works

Recently, models with billions or trillions of parameters have shown significant advances in machine learning capabilities and accuracy. For example, Google’s LaMDA model is able to engage in a free-flowing conversation with users about a large variety of topics. There is enormous interest within the machine learning research and product communities in leveraging large models to deliver breakthrough capabilities. The high computational demand of these large models requires an increased focus on improving the efficiency of the model training process, and benchmarking is an important means to coalesce the ML systems community towards realizing higher efficiencies.

In the recently concluded MLPerf v1.1 Training round¹, Google submitted two large language model benchmarks into the Open division, one with 480 billion parameters and a second with 200 billion parameters. These submissions make use of publicly available infrastructure, including Cloud TPU v4 Pod slices and the Lingvo open source modeling framework. 

Traditionally, training models at these scales would require building a supercomputer at a cost of tens or even hundreds of millions of dollars – something only a few companies can afford to do. Customers can achieve the same results using exaflop-scale Cloud TPU v4 Pods without incurring the costs of installing and maintaining an on-premise system. 

Large model benchmarks

Google’s Open division submissions consist of a 480 billion parameter dense Transformer-based encoder-only benchmark using TensorFlow and a 200 billion-parameter JAX benchmark. These models are architecturally similar to MLPerf’s BERT model but with larger dimensions and number of layers. These submissions demonstrate large model scalability and high performance on TPUs across two distinct frameworks. Notably, these benchmarks, with their stacked transformer architecture, are fairly comparable in terms of their compute characteristics with other large language models.

Our two submissions were benchmarked on 2048-chip and 1024-chip TPU v4 Pod slices, respectively. We were able to achieve an end-to-end training time of ~55 hours for the 480B parameter model and ~40 hours for the 200B parameter model. Each of these runs achieved a computational efficiency of 63%- calculated as a fraction of floating point operations of the model together with compiler rematerialization over the peak FLOPs of the system used². 

Next-generation ML infrastructure for large Model training 

Achieving these impressive results required a combination of several cutting edge technologies. First, each TPU v4 chip provides more than 2X the compute power of a TPU v3 chip – up to 275 peak TFLOPS. Second, 4,096 TPU v4 chips are networked together into a Cloud TPU v4 Pod by an ultra-fast interconnect that provides 10x the bandwidth per chip at scale compared to typical GPU-based large scale training systems. Large models are very communication intensive: local computation often depends on results from remote computation that are communicated across the network. TPU v4’s ultra-fast interconnect has an outsized impact on computational efficiency of large models by eliminating latency and congestion in the network.

The performance numbers demonstrated by our submission also rely on our XLA linear algebra compiler and leverage the Lingvo framework. XLA transparently performs a number of optimizations, including GSPMD based automatic parallelization of many of the computation graphs that form the building blocks of the ML model. XLA also allows for reduction in latency by overlapping communication with the computations. Our two submissions demonstrate the versatility and performance of our software stack across two frameworks, TensorFlow and JAX.

Large models in MLPerf

Google’s submissions represent an important class of models that have become increasingly important in ML research and production, but are currently not represented in MLPerf’s Closed division benchmark suite. 

We believe that adding these models to the benchmark suite is an important next step and can inspire the ML systems community to focus on addressing the scalability challenges that large models present.

Our submissions demonstrate 63% computational efficiency, cutting edge in the industry. This high computational efficiency enables higher experimentation velocity through faster training. This directly translates into cost savings for Google’s Cloud TPU customers.  

Please visit the Cloud TPU homepage and documentation to learn more about leveraging Cloud TPUs using TensorFlow, PyTorch, and JAX.

^{1. The MLPerf name and logo are trademarks of MLCommons Association in the United States and other countries. All rights reserved. Unauthorized use is strictly prohibited. See www.mlcommons.org for more information.
2. Computational efficiency and end-to-end training time are not official MLPerf metrics}