Kasm Workspaces streams your workplace environment directly to your web browser…on any device and from any location.
Kasm is revolutionizing the way businesses deliver digital workspaces. We use our open-source web native container streaming technology to create a modern devops delivery of Desktop as a Service, application streaming, and browser isolation.
Kasm is more than a service. It is a platform that is highly configurable and has a robust API that can be customized to your needs at any scale. Workspaces can be deployed wherever the work is. It can be deployed on-premise (including Air-Gapped Networks), in the cloud (Public and Private), or in a hybrid.
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Linxup is an ideal GPS tracking and dash cam solution for small and medium-sized businesses with fleets that want a powerful, easy-to-use system without overpaying for technology they don't need. Linxup's live, U.S.-based support and coaching team is dedicated to ensuring customers get maximum value to help improve safety, reduce risk and costs, and get the most out of their teams in the field.
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DagsHub
DagsHub serves as a collaborative platform tailored for data scientists and machine learning practitioners to effectively oversee and optimize their projects. By merging code, datasets, experiments, and models within a cohesive workspace, it promotes enhanced project management and teamwork among users. Its standout features comprise dataset oversight, experiment tracking, a model registry, and the lineage of both data and models, all offered through an intuitive user interface. Furthermore, DagsHub allows for smooth integration with widely-used MLOps tools, which enables users to incorporate their established workflows seamlessly. By acting as a centralized repository for all project elements, DagsHub fosters greater transparency, reproducibility, and efficiency throughout the machine learning development lifecycle. This platform is particularly beneficial for AI and ML developers who need to manage and collaborate on various aspects of their projects, including data, models, and experiments, alongside their coding efforts. Notably, DagsHub is specifically designed to handle unstructured data types, such as text, images, audio, medical imaging, and binary files, making it a versatile tool for diverse applications. In summary, DagsHub is an all-encompassing solution that not only simplifies the management of projects but also enhances collaboration among team members working across different domains.
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DVC
Data Version Control (DVC) is an open-source system specifically designed for managing version control in data science and machine learning initiatives. It provides a Git-like interface that allows users to systematically organize data, models, and experiments, making it easier to oversee and version various types of files such as images, audio, video, and text. This system helps structure the machine learning modeling process into a reproducible workflow, ensuring consistency in experimentation. DVC's integration with existing software engineering tools is seamless, empowering teams to articulate every facet of their machine learning projects through human-readable metafiles that detail data and model versions, pipelines, and experiments. This methodology promotes adherence to best practices and the use of well-established engineering tools, thus bridging the gap between the realms of data science and software development. By utilizing Git, DVC facilitates the versioning and sharing of complete machine learning projects, encompassing source code, configurations, parameters, metrics, data assets, and processes by committing the DVC metafiles as placeholders. Furthermore, its user-friendly approach encourages collaboration among team members, enhancing productivity and innovation within projects.
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