Why use deepset Cloud?

deepset Cloud is an enterprise ML/NLP platform for building and integrating NLP features into your applications

Natural language processing (NLP) is more powerful than ever. The astonishing abilities of Transformer-powered systems have inspired visions of language as the primary interface to data. As a result, businesses are increasingly looking to integrate NLP into their own products.

However, while pre-trained language models are freely available, developing a working NLP backend service is not so simple. It involves choosing the right pipeline architecture and finding suitable models for your use case, implementing a database and other application infrastructure, and running a UI-driven demo for the end users — then, after a number of iterations, launching the system in production.

That’s why we’ve built deepset Cloud — a unified ML/NLP platform that takes care of the entire NLP product development lifecycle.

With deepset Cloud, you can:

  • Unite your data and NLP pipelines in a central location
  • Experiment with different pipeline configurations and track their performance, to identify the best setup for achieving your goals
  • Build fully functional NLP backend applications within minutes, and use them to build proof-of-concept or production systems
  • Collect user feedback and use it to improve your system
  • Integrate your NLP pipeline seamlessly into the final product
  • Automatically scale GPU resources depending on the traffic to your application

To any organization building an NLP-powered solution, deepset Cloud offers a full suite of production-ready tools and best practices to streamline their NLP-centric product development.

Introducing: deepset Cloud

deepset Cloud is here to make your team’s life much easier at every step of the NLP implementation cycle. In addition to hosting your files and evaluation data, deepset Cloud’s intuitive online interface lets you design, test, scale and monitor your semantic search or question answering system — all on one platform.

Set up different pipeline configurations

You can quickly build semantic document search and question answering pipelines in deepset Cloud. With so many Transformer models out there, you will probably want to experiment with different setups. In deepset Cloud, under “Pipelines,” you can create as many different configurations as you like. These will be connected automatically to your files stored under “Data.”

If you already know which pipeline design works for you, you can use the YAML editor. Otherwise, you might want to create different pipeline designs in a Jupyter notebook. In this setting, you can directly evaluate the performance of your pipelines to test and compare them against each other. An option for creating pipelines with a drag-and-drop interface will be available soon as well.

Evaluate and track your pipeline performance

Keeping track of different system configurations can get confusing quickly. In deepset Cloud, you’ll be able to track your experiments and keep score of how well a given configuration performed on your evaluation dataset.

Performance metrics provide an overall evaluation of your systems’ accuracy. For a more detailed account of your configurations’ strengths and weaknesses, you will soon be able to compare your systems’ answers to the evaluation set on a case-by-case basis. This way, you can get a better idea of which setup best fits your use case.

Set up a demo app and collect feedback from your team

When you’re working with machine learning models, it can be difficult at times to present your preliminary results in a way that makes your team members want to engage with what you’ve built.

In deepset Cloud, you can make any pipeline design directly available to your test users. After setting up your configurations, just send them a link to the “Search” interface, where they can use your setup to query the underlying data, and receive answers in an instant.

deepset Cloud exposes a powerful REST API that allows you to easily query and control your pipelines from any other application. In fact, anything that you can do in the UI, you can also do programmatically via the REST API. Just generate a new API key in deepset Cloud under “Connections” and start calling the API from your target application.

It’s common for an NLP application to experience different amounts of traffic. Whether you’re receiving hundreds or millions of requests — deepset Cloud’s big GPU cluster automatically scales to the amount of traffic that you receive.

Can I use Haystack and deepset Cloud in combination?

Yes! If you’re already a user of Haystack, we offer an SDK that lets you switch between the library and the browser interface seamlessly. With the SDK, you can design your model locally and export it to the cloud, or vice versa.

If you’re curious and want to learn more, check out the deepset Cloud documentation.

Ready to Build an NLP service?

Request a demo and an account for your organization today!

Trusted by

Frequently Asked Questions

  • deepset Cloud is our enterprise ML/NLP platform for building and integrating NLP features into your applications. It is a SaaS enabling modern product teams to build NLP applications and manage them across the whole lifecycle — from early prototyping to large-scale production

  • We’ve often seen teams starting to build a promising prototype system, only to be overwhelmed by the steps required to get it into a production environment and into the hands of customers while meeting scalability and compliance requirements. Common problems are: How do you find the most suitable language model for your own use case? How to evaluate and fine-tune a pre-trained Transformer model? How to get much needed user feedback as early as possible? How to bring an NLP service into production? How to ensure zero-downtime NLP pipeline deployments? Implementing an NLP-powered system can quickly snowball into a larger project that consumes many of your team’s resources.

  • An integrated data labeling/annotation functionality is in the deepset Cloud roadmap. At this time your team can use our Haystack annotation tool to work on your own datasets.

  • You can build an NLP backend service to add fully-fledged semantic search, question answering, or document similarity to your products in a matter of days, if not hours with deepset Cloud. You will also have a complete suite of tools to iterate quick, build user-facing demos, evaluate and fine-tune pre-trained language models and more.

  • deepset Cloud leverages our well-established open source technology a lot. Most of the pipeline architecture is heavily influenced by the Haystack components, and other infrastructure elements are also based on what Haystack NLP framework offers (e.g., the concept of DocumentStores). That said, deepset Cloud is its own complete ML/NLP platform to facilitate development and implementation of production-ready NLP backend applications. We've put many years of our collective experience and all of the expertise and know-how's related to the NLP product development into deepset Cloud.