LangOps

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Language Operations, commonly referred to as LangOps, is a methodology that has emerged from DevOps. Characterized by its holistic approach, LangOps integrates artificial intelligence and human expertise to facilitate seamless global dialogue. Its aim is to establish a unified language strategy across departments, bridging language and cultural barriers, and enhancing the overall customer experience. Often viewed as the next evolution of localization, LangOps moves beyond mere translation to an integrated, organization-wide management of multilingual communications.

History

LangOps emerged to address limitations of the previous paradigms, localization and internationalization, which were coined in the mid-1990s to adapt computer programs to different languages and regions. The term gained traction when João Graça, the co-founder of Unbabel, published an article in Forbes in April 2021, proposing LangOps as a new paradigm for the language industry, arguing that traditional terms like localization and internationalization have run their course. This evolution aligns with a broader trend of operationalizing various functions in organizations (e.g., DevOps for development operations), reflecting a cross-disciplinary recognition that leveraging technology and data can drive collaboration and efficiency across departments.

In September of 2022, Britta Aagaard, Bruno Herrmann, Jochen Hummel, and Vasco Pedro give an award-winning panel about LangOps - A New Industry Paradigm. November 2022, Britta, João, Jochen, and Vasco present 12 Principles of LangOps, the first draft of a LangOps Manifesto, at LangOps Universe. April 2023, Berlin Calling LangOps Pioneers. Generative AI and the advent of GPT4 make LangOps very concrete. October 2023. as business try to deploy Generative AI, LangOps becomes a key theme for many globalization conferences.

LangOps Manifesto

In December of 2022, localization industry magazine Multilingual published a LangOps manifesto created by Vasco Pedro, João Graça, Britta Aagaard, and Jochen Hummel. The manifesto summarized the main idea of what LangOps should look like and the ideas it should stick to. The manifesto was later released for public use and can be read below (referenced to the original source).

Understand all customers

Our highest priority is to understand every customer no matter what language they use and expand our reach to the widest audience possible.

Support all customer facing functions

We initially focus on the most urgent needs, but promote organizational progress and architect solutions which can eventually support all customer interactions.

Embrace data-centric AI

Data is the key to performant AI systems for language. We collect, create, structure, maintain, and leverage textual data to deliver efficient solutions.

Try AI first

AI systems are already good enough for many use cases and their performance will continue to improve. We always start by trying AI solutions to solve language problems.

Assess quality of AI

Quality is the pillar for trust in AI. Everything we create is proactively assessed through the lens of quality, so we can act before errors arise.

Respect the human-in-the-loop

We use human intelligence where needed. We value the human contribution which constantly improves our machines.

Expect transparency, control and scalability

We create scalable systems with transparency, control to change and improve the process, and simplicity to ease operation.

Process data in real time

The world is moving to dynamic content creation. All content is a stream with different cadences. We build with real time at the core.

Build language-agnostic

We design all processes and supporting systems with ease of scaling to new languages. Users do not need language expertise, but can resort to multilingual datasets and external human contributors.

Promote interdisciplinary knowledge

We train LangOps in the different areas required to be efficient. We continuously enable teams to evolve and learn new skills.

Leverage available data and tech

We make use of available tools and data collections. We strictly account for total cost of ownership in buy or build situations.

Be at the forefront

We invest time to research and study academic and commercial advances. We future proof LangOps by constantly pushing the boundaries.

LangOps and DevOps

LangOps, represents a shift in how language translation and multilingual operations are handled. Its close relations to DevOps methodology invites experts to apply a more technical approach when solving localization problems and seeking value that the discipline could bring to software development. Thinking about localization in terms of DevOps has created a number of valuable concepts that closely relate to how DevOps harmonize software development and IT operations.

Minimum Viable Product (MVP)

Applying DevOps concepts to localization has allowed experts to identify the concept of Minimum Viable Localization Product or MVLP. With the assistance of Machine Translation and AI Post Editing procedures, localization specialists are able to create an instantly localized product versions to add layers of data for AB testing and insights on local user behaviour.

Sprints and product iterations

Similarily to how sprints introduce a more complete product with every loop, LangOps sprints provide a more refined version of the localized product. Sprints can span from MVLP, human post-editing, to introducing SEO experts and subject matter specialists into the loop. Sprints are repeated and adjusted based on content intention, with each iteration serving its own purpose.

Continuous Integration and Continuous Delivery (CI/CD)

DevOps dictates that developers should automate as much as possible. While true for software development, for localization, automations and integrations are comparatively new concepts and only performed on a surface level for now. By following the example that DevOps has set forth for CI/CD, localization is set to strike a balance between optimized automation and human involvement in the very near future.

Analytics Based Decision Making

Advanced workflows and deepened use of integrations has allowed LangOps practitioners to make localization decisions based on local usege data. The introduction of Google Analytics data into the localization workflow that was first presented by a software development company Blackbird.io during LocWorlds Process Innovation Challenge in 2022, has revolutionized the approach and decision making around new market entries and can now be based on real time website traffic data.

LangOps Publications

This is a countinously updated list of LangOps related articles.

1. "LangOps: Pipe Dream, LSP´s Heaven or Just a New Hashtag?"
2. "On the Origin of LangOps. The evolution of the localization roadmap"
3. "The Human in the Loop: Respecting the Emotional Toll of LangOps"
4. "LangOps: The Vision and The Reality"
5. "The LangOps Paradigm. Perceptions of Machine Translation Within the Translation Industry"

List of References

This article "LangOps" is from Wikipedia. The list of its authors can be seen in its historical and/or the page Edithistory:LangOps. Articles copied from Draft Namespace on Wikipedia could be seen on the Draft Namespace of Wikipedia and not main one.

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