Alternatives to TabFM
Compare TabFM alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to TabFM in 2026. Compare features, ratings, user reviews, pricing, and more from TabFM competitors and alternatives in order to make an informed decision for your business.
-
1
MLBox
Axel ARONIO DE ROMBLAY
MLBox is a powerful Automated Machine Learning python library. It provides the following features fast reading and distributed data preprocessing/cleaning/formatting, highly robust feature selection and leak detection, accurate hyper-parameter optimization in high-dimensional space, state-of-the art predictive models for classification and regression (Deep Learning, Stacking, LightGBM), and prediction with models interpretation. MLBox main package contains 3 sub-packages: preprocessing, optimization and prediction. Each one of them are respectively aimed at reading and preprocessing data, testing or optimizing a wide range of learners and predicting the target on a test dataset. -
2
Amazon SageMaker Autopilot
Amazon
Amazon SageMaker Autopilot eliminates the heavy lifting of building ML models. You simply provide a tabular dataset and select the target column to predict, and SageMaker Autopilot will automatically explore different solutions to find the best model. You then can directly deploy the model to production with just one click or iterate on the recommended solutions to further improve the model quality. You can use Amazon SageMaker Autopilot even when you have missing data. SageMaker Autopilot automatically fills in the missing data, provides statistical insights about columns in your dataset, and automatically extracts information from non-numeric columns, such as date and time information from timestamps. -
3
Reka
Reka
Our enterprise-grade multimodal assistant carefully designed with privacy, security, and efficiency in mind. We train Yasa to read text, images, videos, and tabular data, with more modalities to come. Use it to generate ideas for creative tasks, get answers to basic questions, or derive insights from your internal data. Generate, train, compress, or deploy on-premise with a few simple commands. Use our proprietary algorithms to personalize our model to your data and use cases. We design proprietary algorithms involving retrieval, fine-tuning, self-supervised instruction tuning, and reinforcement learning to tune our model on your datasets. -
4
DuckDB
DuckDB
Processing and storing tabular datasets, e.g. from CSV or Parquet files. Large result set transfer to client. Large client/server installations for centralized enterprise data warehousing. Writing to a single database from multiple concurrent processes. DuckDB is a relational database management system (RDBMS). That means it is a system for managing data stored in relations. A relation is essentially a mathematical term for a table. Each table is a named collection of rows. Each row of a given table has the same set of named columns, and each column is of a specific data type. Tables themselves are stored inside schemas, and a collection of schemas constitutes the entire database that you can access. -
5
T5
Google
With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task, including machine translation, document summarization, question answering, and classification tasks (e.g., sentiment analysis). We can even apply T5 to regression tasks by training it to predict the string representation of a number instead of the number itself. -
6
Webix Grid
Webix
Webix Grid is a standalone JavaScript DataGrid component (table/grid UI widget) that is optimized for high-performance, large-dataset scenarios, and is designed to be dropped into web applications where tabular data needs to be displayed, edited, filtered, sorted, etc. Key positioning points: Lightweight: you don’t have to bring in the full Webix UI library if you only need the grid. Focused on “just the grid” use-case rather than a full UI framework. Feature-rich, offering a wide set of capabilities for enterprise-style data apps: - Virtual scrolling - Frozen columns - Inline editing - Sorting - Filtering - Grouping - Column resizing - Column reordering - Multi-line headers - Row selection - Cell selection - Copy/paste - Excel export - PDF export - CSV export - Paging - Validation - Undo/redo - Drag and drop - Spanning cells - Context menus - Keyboard navigation - Conditional formatting - Column summaries - Custom rendering - Live updatesStarting Price: $749 per project -
7
Mistral OCR 4
Mistral AI
Mistral OCR 4 is a document extraction and understanding model built for enterprise search, RAG, domain-specific retrieval pipelines, and production-grade document intelligence. It extracts and structures content from a wide range of documents, moving beyond clean text and tables to return a structured representation of each page. Alongside extracted text, OCR 4 provides bounding boxes, typed-block classification, and inline confidence scores, helping downstream systems understand not only what the document says, but where each element sits, what role it plays, and how confident the model is in each region. Bounding boxes make in-context highlighting and reliable data pipelines possible, while block types and confidence scores support source-grounded citations, redactions, and human-in-the-loop verification. OCR 4 accepts common enterprise formats, including PDF, DOC, PPT, and OpenDocument, and supports 170 languages across 10 language groups.Starting Price: $2 per 1000 pages -
8
PanGu-Σ
Huawei
Significant advancements in the field of natural language processing, understanding, and generation have been achieved through the expansion of large language models. This study introduces a system which utilizes Ascend 910 AI processors and the MindSpore framework to train a language model with over a trillion parameters, specifically 1.085T, named PanGu-{\Sigma}. This model, which builds upon the foundation laid by PanGu-{\alpha}, takes the traditionally dense Transformer model and transforms it into a sparse one using a concept known as Random Routed Experts (RRE). The model was efficiently trained on a dataset of 329 billion tokens using a technique called Expert Computation and Storage Separation (ECSS), leading to a 6.3-fold increase in training throughput via heterogeneous computing. Experimentation indicates that PanGu-{\Sigma} sets a new standard in zero-shot learning for various downstream Chinese NLP tasks. -
9
Tabular
Tabular
Tabular is an open table store from the creators of Apache Iceberg. Connect multiple computing engines and frameworks. Decrease query time and storage costs by up to 50%. Centralize enforcement of data access (RBAC) policies. Connect any query engine or framework, including Athena, BigQuery, Redshift, Snowflake, Databricks, Trino, Spark, and Python. Smart compaction, clustering, and other automated data services reduce storage costs and query times by up to 50%. Unify data access at the database or table. RBAC controls are simple to manage, consistently enforced, and easy to audit. Centralize your security down to the table. Tabular is easy to use plus it features high-powered ingestion, performance, and RBAC under the hood. Tabular gives you the flexibility to work with multiple “best of breed” compute engines based on their strengths. Assign privileges at the data warehouse database, table, or column level.Starting Price: $100 per month -
10
Runway Aleph
Runway
Runway Aleph is a state‑of‑the‑art in‑context video model that redefines multi‑task visual generation and editing by enabling a vast array of transformations on any input clip. It can seamlessly add, remove, or transform objects within a scene, generate new camera angles, and adjust style and lighting, all guided by natural‑language instructions or visual prompts. Built on cutting‑edge deep‑learning architectures and trained on diverse video datasets, Aleph operates entirely in context, understanding spatial and temporal relationships to maintain realism across edits. Users can apply complex effects, such as object insertion, background replacement, dynamic relighting, and style transfers, without needing separate tools for each task. The model’s intuitive interface integrates directly into Runway’s existing Gen‑4 ecosystem, offering an API for developers and a visual workspace for creators. -
11
Evo 2
Arc Institute
Evo 2 is a genomic foundation model capable of generalist prediction and design tasks across DNA, RNA, and proteins. It utilizes a frontier deep learning architecture to model biological sequences at single-nucleotide resolution, achieving near-linear scaling of compute and memory relative to context length. Trained with 40 billion parameters and a 1 megabase context length, Evo 2 processes over 9 trillion nucleotides from diverse eukaryotic and prokaryotic genomes. This extensive training enables Evo 2 to perform zero-shot function prediction across multiple biological modalities, including DNA, RNA, and proteins, and to generate novel sequences with plausible genomic architecture. The model's capabilities have been demonstrated in tasks such as designing functional CRISPR systems and predicting disease-causing mutations in human genes. Evo 2 is publicly accessible via Arc's GitHub repository and is integrated into the NVIDIA BioNeMo framework. -
12
Intelligence Magic
Intelligence Magic
Say goodbye to messy research. Meet Intelligence Magic — the friendly AI that turns websites into organized, tabular insights in minutes! In 3 easy steps your everyday online research gets automated: 1. Describe Your Research: Tell us what data you need to gather across multiple websites. No coding required! 2. Define Your Columns: Specify exactly what columns you want in your spreadsheet - dates, prices, names, or any other data points. 3. Get Organized Results: Receive a clean, structured dataset ready for analysis or export.Starting Price: €50/month -
13
DeepInfra
DeepInfra
DeepInfra is an AI inference cloud that makes it simple to run the latest machine learning models at scale, including LLMs, vision models, embeddings, image generation, video generation, speech, and more. It provides serverless inference through simple APIs, allowing developers to integrate production-ready AI models without managing GPU infrastructure, autoscaling, deployment complexity, or model hosting operations. DeepInfra supports OpenAI-compatible APIs for LLMs and embeddings, making it easier to switch from existing OpenAI-style integrations while accessing a broad catalog of open and commercial models. Its Native API gives access to every model type available on the platform, including image generation, speech recognition, object detection, token classification, fill-mask, image classification, zero-shot image classification, and text classification. DeepInfra is optimized for scalable, low-latency inference and runs models on high-performance GPU infrastructure.Starting Price: $1.98 per hour -
14
Augment your cross-channel DLP with AI-powered classification. Proofpoint Intelligent Classification and Protection is an AI-powered approach to classifying your business-critical data. It recommends actions based on risk accelerating your enterprise DLP program. Our Intelligent Classification and Protection solution helps you understand your unstructured data in a fraction of the time required by legacy approaches. It categorizes a sample of your files using a pre-trained AI-model. And it does this across file repositories both in the cloud and on-premises. With our two-dimensional classification, you get the business context and confidentiality level you need to better protect your data in today’s hybrid world.
-
15
BilberryDB
BilberryDB
BilberryDB is an enterprise-grade vector-database platform designed for building AI applications that handle multimodal data, including images, video, audio, 3D models, tabular data, and text, across one unified system. It supports lightning-fast similarity search and retrieval via embeddings, allows few-shot or no-code workflows to create powerful search/classification capabilities without large labelled datasets, and offers a developer SDK (such as TypeScript) as well as a visual builder for non-technical users. The platform emphasises sub-second query performance at scale, seamless ingestion of diverse data types, and rapid deployment of vector-search-enabled apps (“Deploy as an App”) so organisations can build AI-driven search, recommendation, classification, or content-discovery systems without building infrastructure from scratch.Starting Price: Free -
16
VertiPaq Analyzer
SQLBI
VertiPaq Analyzer is useful to analyze VertiPaq storage structures for a data model in Power BI and Analysis Services Tabular. Added measures for segments and partitions: pageable, resident, refresh date, last access. Analysis Services provides many Dynamic Management Views (DMV) to collect information about memory used by a data model. For example, DISCOVER_OBJECT_MEMORY_USAGE is a DMV that provides information about all the objects in memory. You can use such a DMV also to monitor a Multidimensional instance of Analysis Services. Kasper de Jonge created a sample model (BISM Memory Report) that organizes this data in a hierarchical way, making it easy to find the most expensive databases, tables, and columns on a server. If you want to analyze a particular database, you probably want to look at more detailed information, which are available in other DMVs. -
17
thinkdeeply
Think Deeply
Discover from a variety of assets to jump-start your AI project. The AI hub provides a rich collection of artifacts that your project may need - industry AI starter kits, datasets, notebooks, pre-trained models, deployment-ready solutions & pipelines. Get access to the best resources from external parties, or created by your organization. Prepare and manage your data for model training. Collect, organize, tag, or select features, and prepare datasets for training with simple drag and drop UI. Collaborate with multiple team members to tag large datasets. Implement a quality control process to ensure dataset quality. Build models with simple clicks using the model wizards. No data science knowledge required. The system selects the best models for the problem and optimizes their training parameters. Advanced users, however, can fine-tune the models and their hyper-parameters. One-click deployment to production inference enviornments. -
18
Nixtla
Nixtla
Nixtla is a platform for time-series forecasting and anomaly detection built around its flagship model TimeGPT, described as the first generative AI foundation model for time-series data. It was trained on over 100 billion data points spanning domains such as retail, energy, finance, IoT, healthcare, weather, web traffic, and more, allowing it to make accurate zero-shot predictions across a wide variety of use cases. With just a few lines of code (e.g., via their Python SDK), users can supply historical data and immediately generate forecasts or detect anomalies, even for irregular or sparse time series, and without needing to build or train models from scratch. TimeGPT supports advanced features like handling exogenous variables (e.g., events, prices), forecasting multiple time-series at once, custom loss functions, cross-validation, prediction intervals, and model fine-tuning on bespoke datasets.Starting Price: Free -
19
Qvu Data Service
rbtdesign
Qvu Data Service is an ad-hoc query and api data service design tool that allows users to create and save query designs in a user-friendly, web-based UI. Qvu Data Service provides REST API endpoints for users and applications to execute saved query documents and return results in tabular or JSON formatted result sets. Qvu Data Service provides role-based data source, table column and document group access control and supports both Basic and OIDC authentication.Starting Price: $0 -
20
Lightning Rod
Lightning Rod
Lightning Rod is an AI platform designed to transform messy, unstructured real-world data into verified, production-ready training datasets and domain-specific AI models without requiring manual labeling. It enables users to generate high-quality, citable question–answer pairs from sources such as news articles, financial filings, and internal documents, turning raw historical data into structured datasets that can be used for supervised fine-tuning or reinforcement learning. It operates through an agent-driven workflow where users describe their goal, and the system automatically gathers sources, generates questions, resolves outcomes based on real-world events, and adds contextual grounding before training a model. A key innovation is its “future-as-label” methodology, which uses actual outcomes as training signals, allowing AI systems to learn directly from real-world results at scale instead of relying on synthetic or manually annotated data. -
21
Port
Port IO
Port is a platform for building no-code, holistic, Internal Developer Portals. Port's software catalog covers microservices, resources, custom assets and fits any data model, with in-context maturity scorecards. Its portals support any developer self-service action and workflow automation. -
22
DataGen
DataGen
DataGen is a leading AI platform specializing in synthetic data generation and custom generative AI models for machine learning projects. Their flagship product, SynthEngyne, supports multi-format data generation including text, images, tabular, and time-series data, ensuring privacy-compliant, high-quality training datasets. The platform offers scalable, real-time processing and advanced quality controls like deduplication to maintain dataset fidelity. DataGen also provides professional AI development services such as model deployment, fine-tuning, synthetic data consulting, and intelligent automation systems. With flexible pricing plans ranging from free tiers for individuals to custom enterprise solutions, DataGen caters to a wide range of users. Their solutions serve diverse industries including healthcare, finance, automotive, and retail. -
23
GPT-J
EleutherAI
GPT-J is a cutting-edge language model created by the research organization EleutherAI. In terms of performance, GPT-J exhibits a level of proficiency comparable to that of OpenAI's renowned GPT-3 model in a range of zero-shot tasks. Notably, GPT-J has demonstrated the ability to surpass GPT-3 in tasks related to generating code. The latest iteration of this language model, known as GPT-J-6B, is built upon a linguistic dataset referred to as The Pile. This dataset, which is publicly available, encompasses a substantial volume of 825 gibibytes of language data, organized into 22 distinct subsets. While GPT-J shares certain capabilities with ChatGPT, it is important to note that GPT-J is not designed to operate as a chatbot; rather, its primary function is to predict text. In a significant development in March 2023, Databricks introduced Dolly, a model that follows instructions and is licensed under Apache.Starting Price: Free -
24
Amundsen
Amundsen
Discover & trust data for your analysis and models. Be more productive by breaking silos. Get immediate context into the data and see how others are using it. Search for data within your organization by a simple text search. A PageRank-inspired search algorithm recommends results based on names, descriptions, tags, and querying/viewing activity on the table/dashboard. Build trust in data using automated and curated metadata, descriptions of tables and columns, other frequent users, when the table was last updated, statistics, a preview of the data if permitted, etc. Easy triage by linking the ETL job and code that generated the data. Update tables and columns with descriptions, reduce unnecessary back and forth about which table to use and what a column contains. See what data fellow co-workers frequently use, own or have bookmarked. Learn what most common queries for a table look like by seeing dashboards built on a given table. -
25
VideoPoet
Google
VideoPoet is a simple modeling method that can convert any autoregressive language model or large language model (LLM) into a high-quality video generator. It contains a few simple components. An autoregressive language model learns across video, image, audio, and text modalities to autoregressively predict the next video or audio token in the sequence. A mixture of multimodal generative learning objectives are introduced into the LLM training framework, including text-to-video, text-to-image, image-to-video, video frame continuation, video inpainting and outpainting, video stylization, and video-to-audio. Furthermore, such tasks can be composed together for additional zero-shot capabilities. This simple recipe shows that language models can synthesize and edit videos with a high degree of temporal consistency. -
26
Cleanlab
Cleanlab
Cleanlab Studio handles the entire data quality and data-centric AI pipeline in a single framework for analytics and machine learning tasks. Automated pipeline does all ML for you: data preprocessing, foundation model fine-tuning, hyperparameter tuning, and model selection. ML models are used to diagnose data issues, and then can be re-trained on your corrected dataset with one click. Explore the entire heatmap of suggested corrections for all classes in your dataset. Cleanlab Studio provides all of this information and more for free as soon as you upload your dataset. Cleanlab Studio comes pre-loaded with several demo datasets and projects, so you can check those out in your account after signing in. -
27
InfraWorks
Autodesk
InfraWorks® conceptual design software lets architecture, engineering, and construction professionals model, analyze, and visualize infrastructure design concepts within the context of the built and natural environment—improving decision making and accelerating project approvals. Aggregate large amounts of data to generate a rich context model. Seamlessly integrate design with geospatial GIS data. Model existing conditions that represent the built and natural environment. Visually explore conceptual design options in-context. Use analysis and simulation tools to explore important aspects of your project. Generate compelling and immersive visual experiences to communicate design intent. Establish existing site conditions and extract linear features to accelerate design.Starting Price: $1,825 per year -
28
NoSQL
NoSQL
NoSQL is a domain-specific programming language used for accessing, managing, and manipulating non-tabular databases. A NoSQL (originally referring to "non-SQL" or "non-relational") database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. Such databases have existed since the late 1960s, but the name "NoSQL" was only coined in the early 21st century, triggered by the needs of Web 2.0 companies. NoSQL databases are increasingly used in big data and real-time web applications.NoSQL systems are also sometimes called Not only SQL to emphasize that they may support SQL-like query languages or sit alongside SQL databases in polyglot-persistent architectures. Many NoSQL stores compromise consistency (in the sense of the CAP theorem) in favor of availability, partition tolerance, and speed. Barriers to the greater adoption of NoSQL stores include the use of low-level query languages. -
29
Florence-2
Microsoft
Florence-2-large is an advanced vision foundation model developed by Microsoft, capable of handling a wide variety of vision and vision-language tasks, such as captioning, object detection, segmentation, and OCR. Built with a sequence-to-sequence architecture, it uses the FLD-5B dataset containing over 5 billion annotations and 126 million images to master multi-task learning. Florence-2-large excels in both zero-shot and fine-tuned settings, providing high-quality results with minimal training. The model supports tasks including detailed captioning, object detection, and dense region captioning, and can process images with text prompts to generate relevant responses. It offers great flexibility by handling diverse vision-related tasks through prompt-based approaches, making it a competitive tool in AI-powered visual tasks. The model is available on Hugging Face with pre-trained weights, enabling users to quickly get started with image processing and task execution.Starting Price: Free -
30
Yi-Large
01.AI
Yi-Large is a proprietary large language model developed by 01.AI, offering a 32k context length with both input and output costs at $2 per million tokens. It stands out with its advanced capabilities in natural language processing, common-sense reasoning, and multilingual support, performing on par with leading models like GPT-4 and Claude3 in various benchmarks. Yi-Large is designed for tasks requiring complex inference, prediction, and language understanding, making it suitable for applications like knowledge search, data classification, and creating human-like chatbots. Its architecture is based on a decoder-only transformer with enhancements such as pre-normalization and Group Query Attention, and it has been trained on a vast, high-quality multilingual dataset. This model's versatility and cost-efficiency make it a strong contender in the AI market, particularly for enterprises aiming to deploy AI solutions globally.Starting Price: $0.19 per 1M input token -
31
RazorSQL
RazorSQL
RazorSQL is an SQL query tool, database browser, SQL editor, and database administration tool for Windows, macOS, Mac OS X, Linux, and Solaris. RazorSQL has been tested on over 40 databases, can connect to databases via either JDBC or ODBC. Browse database objects such as schemas, tables, columns, primary and foreign keys, views, indexes, procedures, functions, and more. Visual tools to create, alter, describe, execute, and drop database objects such as tables, views, indexes, stored procedures, functions, triggers, and more. Includes multi-tabular display of queries with options for filtering, sorting, searching, and much more. Import data from various formats such as delimited files, Excel spreadsheets, and fixed-width files. Includes a robust relational database (HSQLDB) that is up and running with no manual configuration out of the box.Starting Price: $99.95 one-time payment -
32
FLUX.1 Kontext
Black Forest Labs
FLUX.1 Kontext is a suite of generative flow matching models developed by Black Forest Labs, enabling users to generate and edit images using both text and image prompts. This multimodal approach allows for in-context image generation, facilitating seamless extraction and modification of visual concepts to produce coherent renderings. Unlike traditional text-to-image models, FLUX.1 Kontext unifies instant text-based image editing with text-to-image generation, offering capabilities such as character consistency, context understanding, and local editing. Users can perform targeted modifications on specific elements within an image without affecting the rest, preserve unique styles from reference images, and iteratively refine creations with minimal latency. -
33
SSAS
Microsoft
Installed as an on-premises server instance, SQL Server Analysis Services supports tabular models at all compatibility levels (depending on version), multidimensional models, data mining, and Power Pivot for SharePoint. A typical implementation workflow includes installing a SQL Server Analysis Services instance, creating a tabular or multidimensional data model, deploying the model as a database to a server instance, processing the database to load it with data, and then assigning permissions to allow data access. When ready to go, the data model can be accessed by any client application supporting Analysis Services as a data source. Models are populated with data from external data systems, usually data warehouses hosted on a SQL Server or Oracle relational database engine (Tabular models support additional data source types). -
34
DataCebo Synthetic Data Vault (SDV)
DataCebo
The Synthetic Data Vault (SDV) is a Python library designed to be your one-stop shop for creating tabular synthetic data. The SDV uses a variety of machine learning algorithms to learn patterns from your real data and emulate them in synthetic data. The SDV offers multiple models, ranging from classical statistical methods (GaussianCopula) to deep learning methods (CTGAN). Generate data for single tables, multiple connected tables, or sequential tables. Compare the synthetic data to the real data against a variety of measures. Diagnose problems and generate a quality report to get more insights. Control data processing to improve the quality of synthetic data, choose from different types of anonymization, and define business rules in the form of logical constraints. Use synthetic data in place of real data for added protection, or use it in addition to your real data as an enhancement. The SDV is an overall ecosystem for synthetic data models, benchmarks, and metrics.Starting Price: Free -
35
RoBERTa
Meta
RoBERTa builds on BERT’s language masking strategy, wherein the system learns to predict intentionally hidden sections of text within otherwise unannotated language examples. RoBERTa, which was implemented in PyTorch, modifies key hyperparameters in BERT, including removing BERT’s next-sentence pretraining objective, and training with much larger mini-batches and learning rates. This allows RoBERTa to improve on the masked language modeling objective compared with BERT and leads to better downstream task performance. We also explore training RoBERTa on an order of magnitude more data than BERT, for a longer amount of time. We used existing unannotated NLP datasets as well as CC-News, a novel set drawn from public news articles.Starting Price: Free -
36
Olmo 3
Ai2
Olmo 3 is a fully open model family spanning 7 billion and 32 billion parameter variants that delivers not only high-performing base, reasoning, instruction, and reinforcement-learning models, but also exposure of the entire model flow, including raw training data, intermediate checkpoints, training code, long-context support (65,536 token window), and provenance tooling. Starting with the Dolma 3 dataset (≈9 trillion tokens) and its disciplined mix of web text, scientific PDFs, code, and long-form documents, the pre-training, mid-training, and long-context phases shape the base models, which are then post-trained via supervised fine-tuning, direct preference optimisation, and RL with verifiable rewards to yield the Think and Instruct variants. The 32 B Think model is described as the strongest fully open reasoning model to date, competitively close to closed-weight peers in math, code, and complex reasoning.Starting Price: Free -
37
Automaton AI
Automaton AI
With Automaton AI’s ADVIT, create, manage and develop high-quality training data and DNN models all in one place. Optimize the data automatically and prepare it for each phase of the computer vision pipeline. Automate the data labeling processes and streamline data pipelines in-house. Manage the structured and unstructured video/image/text datasets in runtime and perform automatic functions that refine your data in preparation for each step of the deep learning pipeline. Upon accurate data labeling and QA, you can train your own model. DNN training needs hyperparameter tuning like batch size, learning, rate, etc. Optimize and transfer learning on trained models to increase accuracy. Post-training, take the model to production. ADVIT also does model versioning. Model development and accuracy parameters can be tracked in run-time. Increase the model accuracy with a pre-trained DNN model for auto-labeling. -
38
Accessibility Cloud
Accessibility Cloud
Test & monitor your websites and documents against WCAG 2.1 and EN 301 549. Accessibility Cloud offers automatic and manual accessibility tests, monitoring and compliance management within the same powerful platform. Automatic testing: Discover errors, occurrences, impact to users with disabilities, whether they are violations, see potential issues, solution suggestions, get in-context learning opportunities, scan documents, highlight problems on top of your site, and so much more. Manual testing: Do manual accessibility tests with industry’s most comprehensive WCAG-EM testing suite which supports automatically assisted tests, multiple guidelines, markdown, in-context learning material, powerful import and export options and so much more. Monitoring: Invite your colleagues, define who gets an email notification for what and let Accessibility Cloud do the rest. It will monitor your site continuously and inform you the moment new accessibility problems are discovered.Starting Price: €49 per month -
39
BIMx
Graphisoft
Bridge the gap between the design studio and the construction site with award-winning BIMx, the most popular presentation and coordination app for all project stakeholders. BIMx features the ‘BIM Hyper-model’ – a game-like navigation tool that helps even non-professionals easily explore the building model and understand project deliverables. Real-time model cut-throughs, in-context measuring and project markups in the model context make BIMx your best on-site BIM companion. Simple, game-like navigation makes BIMx the best on-site design and presentation tool on the market. Drive the design narrative on the building site for fast, specific client feedback. A digital model means no more paper on the building site or at client meetings. All the relevant model data is at your fingertips for easy access and sharing. Boost your sales! Promote your design in a unique way by embedding your design on your own website with BIMx Web Viewer. -
40
NXG Logic Explorer
NXG Logic
NXG Logic Explorer is a Windows-based machine learning package designed for data analytics, predictive analytics, unsupervised class discovery, supervised class prediction, and simulation. It enhances productivity by reducing the time required for various procedures, enabling users to identify novel patterns in exploratory datasets and perform hypothesis testing, simulations, and text mining to extract meaningful insights. Key features include automatic de-stringing of messy Excel input files, parallel feature analysis for generating summary statistics, Shapiro-Wilk tests, histograms, and count frequencies for multiple continuous and categorical variables. It allows simultaneous execution of ANOVA, Welch ANOVA, chi-squared, and Bartlett's tests on multiple variables, and automatically generates multivariable linear, logistic, and Cox proportional hazards regression models based on a default p-value criterion for filtering from univariate models. -
41
neptune.ai
neptune.ai
Neptune.ai is a machine learning operations (MLOps) platform designed to streamline the tracking, organizing, and sharing of experiments and model-building processes. It provides a comprehensive environment for data scientists and machine learning engineers to log, visualize, and compare model training runs, datasets, hyperparameters, and metrics in real-time. Neptune.ai integrates easily with popular machine learning libraries, enabling teams to efficiently manage both research and production workflows. With features that support collaboration, versioning, and experiment reproducibility, Neptune.ai enhances productivity and helps ensure that machine learning projects are transparent and well-documented across their lifecycle.Starting Price: $49 per month -
42
Weights & Biases
Weights & Biases
Experiment tracking, hyperparameter optimization, model and dataset versioning with Weights & Biases (WandB). Track, compare, and visualize ML experiments with 5 lines of code. Add a few lines to your script, and each time you train a new version of your model, you'll see a new experiment stream live to your dashboard. Optimize models with our massively scalable hyperparameter search tool. Sweeps are lightweight, fast to set up, and plug in to your existing infrastructure for running models. Save every detail of your end-to-end machine learning pipeline — data preparation, data versioning, training, and evaluation. It's never been easier to share project updates. Quickly and easily implement experiment logging by adding just a few lines to your script and start logging results. Our lightweight integration works with any Python script. W&B Weave is here to help developers build and iterate on their AI applications with confidence. -
43
NVIDIA NeMo Megatron
NVIDIA
NVIDIA NeMo Megatron is an end-to-end framework for training and deploying LLMs with billions and trillions of parameters. NVIDIA NeMo Megatron, part of the NVIDIA AI platform, offers an easy, efficient, and cost-effective containerized framework to build and deploy LLMs. Designed for enterprise application development, it builds upon the most advanced technologies from NVIDIA research and provides an end-to-end workflow for automated distributed data processing, training large-scale customized GPT-3, T5, and multilingual T5 (mT5) models, and deploying models for inference at scale. Harnessing the power of LLMs is made easy through validated and converged recipes with predefined configurations for training and inference. Customizing models is simplified by the hyperparameter tool, which automatically searches for the best hyperparameter configurations and performance for training and inference on any given distributed GPU cluster configuration. -
44
Codebashing
Checkmarx
Codebashing is Checkmarx’s in-context eLearning platform that sharpens the skills developers need to fix vulnerabilities and write secure code. Expanding on the learn-by-doing concept, Codebashing teaches developers the principles of secure coding and helps them sharpen application security skills in the most efficient way. Give your developers the skills they need to increase security and reduce risk right from the start. Transform developer security training into an ongoing experience that integrates seamlessly into daily workflows, making learning continuous, personalized, and directly aligned with developers’ evolving needs. Personalized secure code training journeys are carefully crafted to equip developers with role-specific knowledge, making security training both relevant and effective. This custom learning path includes 85 lessons, covering all SDLC aspects, designed to help security-minded developers become security champions for your enterprise. -
45
Universal Sentence Encoder
Tensorflow
The Universal Sentence Encoder (USE) encodes text into high-dimensional vectors that can be utilized for tasks such as text classification, semantic similarity, and clustering. It offers two model variants: one based on the Transformer architecture and another on Deep Averaging Network (DAN), allowing a balance between accuracy and computational efficiency. The Transformer-based model captures context-sensitive embeddings by processing the entire input sequence simultaneously, while the DAN-based model computes embeddings by averaging word embeddings, followed by a feedforward neural network. These embeddings facilitate efficient semantic similarity calculations and enhance performance on downstream tasks with minimal supervised training data. The USE is accessible via TensorFlow Hub, enabling seamless integration into various applications. -
46
ShotGrid
Autodesk
ShotGrid, formerly Shotgun Software, streamlines workflows for creative studios. Bring creative visions to life and deliver on time and budget with powerful project tracking tools. Boost collaboration with media playback and review tools. Run productions your way with customizable workflows, application integrations, and an open ecosystem. Track every step of your project including shots and assets as they move through the pipeline. Remove business guesswork with reporting tools. Easily scale creative projects in size and complexity. Maximize resources with superior project planning and scheduling capabilities. Receive updates with automatically tracked versions and note history. Give effective feedback and easily collaborate with in-context notes and annotations.Starting Price: $330 per year -
47
Modern CSV
Gallium Digital
Modern CSV is a tabular file editor/viewer with advanced editing features and large file handling. It makes up for the weaknesses of spreadsheet programs in handling CSV files while incorporating the strengths of the best text editors. Its features include: multiple cell/row/column editing, fast load times, customizable keyboard shortcuts, data analysis, light and dark themes, regex find/replace, and multiple encoding and delimiter handling.Starting Price: $39 -
48
Prevision
Prevision.io
Building a model is an iterative process that can take weeks, months, or even years, and reproducing model results, maintaining version control, and auditing past work are complex. Model building is an iterative process. Ideally, you record not only each step but also how you arrived there. A model shouldn’t be a file hidden away somewhere, but instead a tangible object that all parties can track and analyze consistently. Prevision.io allows you to record each experiment as you train it along with its characteristics, automated analyses, and versions as your project progress, whether you created it using our AutoML or your own tools. Automatically experiment with dozens of feature engineering strategies and algorithm types to build highly performant models. In a single command, the engine automatically tries out different feature engineering strategies for every type of data (e.g. tabular, text, images) to maximize the information in your datasets. -
49
Reka Flash 3
Reka
Reka Flash 3 is a 21-billion-parameter multimodal AI model developed by Reka AI, designed to excel in general chat, coding, instruction following, and function calling. It processes and reasons with text, images, video, and audio inputs, offering a compact, general-purpose solution for various applications. Trained from scratch on diverse datasets, including publicly accessible and synthetic data, Reka Flash 3 underwent instruction tuning on curated, high-quality data to optimize performance. The final training stage involved reinforcement learning using REINFORCE Leave One-Out (RLOO) with both model-based and rule-based rewards, enhancing its reasoning capabilities. With a context length of 32,000 tokens, Reka Flash 3 performs competitively with proprietary models like OpenAI's o1-mini, making it suitable for low-latency or on-device deployments. The model's full precision requires 39GB (fp16), but it can be compressed to as small as 11GB using 4-bit quantization. -
50
DbFace
DbFace
The best platform for everyone to explore and visualize data from any data sources. We are building the fastest and express database backend web applications builder. Type SQL, get reports applications, put these applications into elastic dashboard, and many more. DbFace might be the fastest way to build your SQL database frontend, you do not need coding PHP or any front-end HTML, CSS, just follow the robust application builder way to create full features database applications. DbFace intuitive Drag & Drop interface provides you with all the flexibility and advanced visualization options you need to create meaningful charts and tables. Not only static charts & reports. You can also build applications that accept user's input. DbFace can do anything that SQL do. Tabular report, Pivot tables, Summary Report, Line Chart, Pie Chart, Bar Chart, Column Chart, Number Report, Treemap, Word cloud, Dashboard and counting.Starting Price: $9 per month
