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Keepsake

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What is Soterre?

Motio leverages extensive expertise to enhance and automate business intelligence within Qlik Sense. Recognizing that Qlik Sense lacks version control and has an inefficient deployment process, we address these shortcomings to significantly amplify its capabilities. Our advanced versioning and change tracking systems offer comprehensive insights that effectively tackle your auditing challenges. In the realm of analytics, we prioritize self-service solutions that demand both auditability and adherence to best practices. With our innovative tools, tracking modifications and fostering a robust, sustainable environment becomes a seamless process. This leads to deployments that are more streamlined, less time-intensive, and safer for all stakeholders. Furthermore, our solution enables connectivity between multiple environments, facilitating the targeted promotion of content. Your team can achieve one-click deployments, thereby eliminating the cumbersome need to access the server for file transfers, resulting in heightened efficiency and collaboration. Ultimately, we empower organizations to harness the full potential of Qlik Sense while ensuring a controlled and manageable workflow.

What is Keepsake?

Keepsake is an open-source Python library tailored for overseeing version control within machine learning experiments and models. It empowers users to effortlessly track vital elements such as code, hyperparameters, training datasets, model weights, performance metrics, and Python dependencies, thereby facilitating thorough documentation and reproducibility throughout the machine learning lifecycle. With minimal modifications to existing code, Keepsake seamlessly integrates into current workflows, allowing practitioners to continue their standard training processes while it takes care of archiving code and model weights to cloud storage options like Amazon S3 or Google Cloud Storage. This feature simplifies the retrieval of code and weights from earlier checkpoints, proving to be advantageous for model re-training or deployment. Additionally, Keepsake supports a diverse array of machine learning frameworks including TensorFlow, PyTorch, scikit-learn, and XGBoost, which aids in the efficient management of files and dictionaries. Beyond these functionalities, it offers tools for comparing experiments, enabling users to evaluate differences in parameters, metrics, and dependencies across various trials, which significantly enhances the analysis and optimization of their machine learning endeavors. Ultimately, Keepsake not only streamlines the experimentation process but also positions practitioners to effectively manage and adapt their machine learning workflows in an ever-evolving landscape. By fostering better organization and accessibility, Keepsake enhances the overall productivity and effectiveness of machine learning projects.