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Running AI in production

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Run AI in production seamlessly without the complexity of integrating multiple platforms, and easily scale up or down as needed.

The Problem

Running AI in production involves integrating an expensive toolchain, monitoring for model and data drift to maintain quality, and collecting feedback for continuous model improvement—an intensely manual process.

Run AI in production,
without the overhead

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Production-grade AI without the logistics

Scale compute across your entire AI workflow without the overhead of managing many tools, unlike AWS, Azure and Google Cloud.

  • Stop wasting time integrating a complex tool stack, and
automatically turn workflows on or off.
  • Optimize your compute resources by controlling AI operational and performance metrics.
  • Scale compute across your workflow without scaling your DevOps headcount.
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One tool to manage the entire AI workflow

Reduce issues by having one unified platform that combines every tool so you can ensure your AI applications are running smoothly.

  • Reduce interdependency risk by unifying your entire AI workflow to one integrated platform.
  • React faster to performance issues with clear and understandable pipeline error messages
  • Deliver more reliable applications by relying on multiple cloud platforms.
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Enable Active Learning in production

Close the learning loop to actively improve the quality of your models with Dataloop’s integrated platform.

  • Accelerate learning by automatically integrating feedback from production back into your models.
  • Combine all elements of Active Learning: Data management, RLHF, building data sets, and model training.
  • Eliminate manual work needed to close the learning loop and create high-quality iterations.
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Build on a solid foundation

Develop AI applications at the speed of market demand

 

Build anything with Dataloop

These are just some of the workflows our users build using the platform

Building a GenAI stack

Create single- or multi-modal AI applications, work with hundreds of datasets, chose any model you need to and replace them at will – all on the only truly-complete platform for GenAI development.

Multi-cloud Compute

Chain multiple cloud compute nodes together in a single pipeline. Use different types of compute and different compute vendors, including all major clouds, on-premise, NVIDIA’s DGX or Dataloop’s own compute offering.

Building AI agents

Dataloop offers all the essential components to develop robust, both task-specific and general-purpose agents. Input context during model configuration, then deploy the agents to multiple destinations from within Dataloop.

DataOps

Data curation, cleaning, versioning and management tools you can depend on. Dataloop pre-processes every single piece of unstructured data for easy retrieval and filtering, allowing quick & easy data selection.

FAQ

Dataloop is an AI development platform designed to empower data practitioners to collaborate and build exceptional AI solutions. It comes pre-packed with models, functions, datasets, and integrations with popular cloud platforms, ensuring you can hit the ground running when developing your AI applications.

Dataloop is an all-in-one solution, eliminating the need for multiple tools or cloud services to deliver complete, robust AI applications. Furthermore, Dataloop prioritizes RLHF, Active Learning, and other human-in-the-loop workflows, offering dedicated, state-of-the-art annotation studios for human reviewers to excel.

Dataloop offers a large Marketplace of models, datasets, pre-built workflow templates and more, and is highly-integrated with a variety of cloud platforms, data tools and more.

Read more about why Dataloop is a great choice for you in our dedicated page for Data Engineers.

Dataloop helps automate processes crucial to AI development, such as model training, human feedback (for RLHF and Active Learning) and more, and lets you focus on training your models instead of platform setup and configuration.

Read more about why Dataloop is a great choice for you in our dedicated page for Data Scientist.

In Dataloop, every piece of the pipeline can be created, modified and deleted using an API call, and our robust Python SDK allows for complete code-level control on the data pipelines you rely on to build your AI applications.

Read more about why Dataloop is a great choice for you in our dedicated page for Software Engineers.

Dataloop allow teams to focus on building AI applications, and not platform maintenance – while still allowing for complicated, human-in-the-loop flows and without compromising on quality or the speed of delivery.

Read more about why Dataloop is a great choice for you in our dedicated page for Data & AI Leaders.

To get started with Dataloop, you can talk to one of our AI experts.

At Dataloop, privacy and security are our top priorities. We adhere to leading industry standards and are dedicated to ensuring the security of your data with comprehensive governance throughout the entire platform. More specifically, Dataloop is compliant with SOC 2 Type II, GDPR, ISO 27001 and ISO 27701, and offers RBAC, 2-factor authentication, AES-256 encryption and ongoing tracking of all system resources and actions that occur within the platform.

You can read more about our security controls in our dedicated security resource.