Active learning tools are specialized software solutions crafted to augment the development of machine learning (ML) models. They operate within a supervised framework, strategically optimizing data annotation, labeling, and model training. Unlike broader ML or MLOps platforms, these tools are specifically engineered to establish an iterative feedback loop that directly informs the model training process, pinpointing edge cases, and diminishing the label requirement. This targeted feedback harnesses model uncertainty to identify the most valuable data for annotation, thereby enhancing model performance with a smaller yet more relevant dataset. Diverging from conventional data labeling software, active learning tools place a primary emphasis on the annotation process, as well as on managing and selecting the most appropriate data for labeling. Furthermore, they transcend the functionalities of data science and machine learning platforms by not merely deploying models, but actively refining them through continuous learning cycles. These tools boast unique features that automatically identify errors and outliers, furnish actionable insights for model enhancement, and enable intelligent data selection—critical for fine-tuning pre-existing models to suit specific use cases. The significance of active learning tools has burgeoned with the emergence of open-source models provided by AI organizations, as they cater to a broader spectrum of users seeking to customize these models for their distinct requirements. These tools serve AI teams, computer vision specialists, ML engineers, and data scientists alike, aiding in the creation of efficient active learning loops, which are markedly distinct from the broader ML frameworks or data storage and interconnectivity services proffered by MLOps platforms. For a product to be considered for inclusion in the Active Learning Tools category, it must: 1. Facilitate the establishment of an iterative loop between data annotation and model training. 2. Possess capabilities for automatically identifying model errors, outliers, and edge cases. 3. Offer insights into model performance and guide the annotation process to enhance it. 4. Enable the selection and management of training data for effective model optimization.
Hantar Aplikasi Baharu
Galileo AI
usegalileo.ai
Galileo AI ialah alat AI untuk reka bentuk antara muka. Ia menghasilkan reka bentuk UI daripada arahan teks, termasuk ilustrasi, imej dan salinan produk, dan mengurangkan kerja berulang.
Modal
modal.com
Modal membantu orang menjalankan kod di awan dengan menyediakan pengkomputeran serverless berasaskan kontena tanpa perlu mengurus infrastruktur sendiri.
Labelbox
labelbox.com
Labelbox ialah platform berfokus data untuk membina dan guna aplikasi AI: latih/haluskan model dan automasi dengan LLM. Ia menggunakan kuki untuk fungsi, keutamaan dan analitik pihak ketiga.
V7
v7labs.com
V7 ialah enjin data AI untuk penglihatan komputer dan AI generatif yang menyediakan pelabelan imej/video/teks, alur kerja, auto-annotate, DICOM, OCR, pengurusan dataset dan kolaborasi pasukan.
Dataloop
dataloop.ai
Dataloop ialah platform pembangunan AI untuk mengurus, melabel, menganalisis dan menyebarkan dataset serta model, menyokong integrasi storan awan dan eksport format seperti COCO/Yolo.
Encord
encord.com
Encord ialah platform untuk mengurus data latihan AI: anotasi visual, aliran pembelajaran aktif, penilaian kualiti, serta latihan dan penalaan model berskala.
Lightly AI
lightly.ai
Lightly membantu pasukan pembelajaran mesin memilih dan mengurus data latihan menggunakan pembelajaran aktif: menapis sampel, menganalisis kualiti/diversiti, dan memantau prestasi model.
Cleanlab
cleanlab.ai
Cleanlab memeriksa dan membetulkan masalah data (entri salah, label salah, outlier, duplikasi) pada dataset imej/teks/tabular, auto-label dan hasilkan dataset bersih serta model automatik tanpa kod.
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