Alternatives to Symbolica

GAMS (General Algebraic Modeling System), a leading mathematical modeling software, is known for its ease of use, high performance and scalability. GAMSPy, the official release of GAMS, allows users to integrate GAMS and Python. This allows for flexible and powerful models to be created directly within Python. GAMS' efficient algebraic modeling allows users to express optimization problems in a simple and straightforward way. It offers optimal solutions by using top-tier mathematical solvers. GAMS MIRO is a graphical interface for GAMS models that facilitates local and cloud deployment. It also includes advanced visualization features. GAMS Engine is a reliable SaaS-based solution for scalable model solutions. Models can be solved either on-premises, or in the cloud. GAMS also offers workshops, training and consulting services that help users create, improve and deploy decision support solutions.

Amazon SageMaker, a fully managed service, provides data scientists and developers with the ability to quickly build, train, deploy, and deploy machine-learning (ML) models. SageMaker takes the hard work out of each step in the machine learning process, making it easier to create high-quality models. Traditional ML development can be complex, costly, and iterative. This is made worse by the lack of integrated tools to support the entire machine learning workflow. It is tedious and error-prone to combine tools and workflows. SageMaker solves the problem by combining all components needed for machine learning into a single toolset. This allows models to be produced faster and with less effort. Amazon SageMaker Studio is a web-based visual interface that allows you to perform all ML development tasks. SageMaker Studio allows you to have complete control over each step and gives you visibility.

Amazon SageMaker Clarify is a machine learning (ML), development tool that provides purpose-built tools to help them gain more insight into their ML training data. SageMaker Clarify measures and detects potential bias using a variety metrics so that ML developers can address bias and explain model predictions. SageMaker Clarify detects potential bias in data preparation, model training, and in your model. You can, for example, check for bias due to age in your data or in your model. A detailed report will quantify the different types of possible bias. SageMaker Clarify also offers feature importance scores that allow you to explain how SageMaker Clarify makes predictions and generates explainability reports in bulk. These reports can be used to support internal or customer presentations and to identify potential problems with your model.

Monitor your LLMs during production and discover and correct hallucinations and errors related to accuracy and quality with LLM outputs. Check your outputs to see if they contain hallucinations, misinformation or other issues. Configurable for any LLM application. Segment data to analyze in depth your cost, accuracy and response times. To debug generation, you can search, sort and filter your inference calls and trace your queries, retrievals and responses. Explore your conversations to learn what your users feel and what they are saying. You can also find out which conversations were unsuccessful. Compare your performance metrics between different models and prompts. Our insights will guide you to the best model for each use case. Our evaluators analyze and improve the outputs by using your data, configurations and feedback.

Coral is a knowledge assistant that helps enterprises boost the productivity of their strategic teams. Coral can be asked a question to find answers in your documents, backed up with citations. The generated responses can be verified with citations, reducing the risk of hallucinations. Explain large language models for a non-technical retailer executive. Coral can be tailored to fit your specific job functions, whether you are in finance, support, or sales. Connect data sources to its knowledge base to make it more powerful. Coral integrates with your ecosystem via 100+ integrations that span CRMs, collaboration tools and databases. Manage Coral in your own secure cloud, either through cloud partners (AWS GCP OCI etc.). Virtual private clouds. Coral data will never be sent to Cohere. It stays in your own environment. Coral's responses are based on your documents and data. Citations will be displayed to show where the responses are based.

Character.AI brings science-fiction dreams of open-ended conversations with computers to life. We are developing the next generation dialog agents. This includes a wide range of applications that span entertainment, education, and general question-answering. Our proprietary technology powers our dialog agents. It is based on large language models that were built with conversation in mind. Character.AI beta is built on neural language models. A supercomputer can read huge amounts of text and learn to anticipate what words will be next in any given situation. These models have many uses, including machine translation and auto-complete. Character.AI allows you to collaborate with the computer in order to create a dialog. You write one character's lines and the computer creates another character's lines. This gives you the illusion that your talking with the other character.

You can use our algebra calculator to solve your algebra questions step-by-step, or you can practice with our lessons and practice. You can use our algebra calculator on the MathPapa site or the MathPapa app. You can learn algebra at your own speed and build a solid foundation of math knowledge. We will help you achieve your goal. Regular practice with our exercises will help you to solidify your algebraic skills. Your personal goals to master algebra. MathPapa can help you with your math homework by solving your equations and showing you the work. Solve linear equations and Quadratic Equations. Solves linear and quadratic inequality. Graphs equations. Factors quadratic expressions. Order of operations step by step. Evaluates expressions. Solve systems of two equations. MathPapa aims to help you learn algebra by step. MathPapa's algebra calculator can help you with your algebra problems.

Command's outputs are accompanied by clear citations, which reduce the risk of hallucinations. They also allow for the retrieval of additional context in the source material. Command can help you write product descriptions, draft emails, provide example press releases and more. Ask Command a series of questions about a particular document to assign it a category, extract information, or answer an overall question. Answering a few questions can save you minutes, but doing it for thousands can save an entire company years. This family of scalable AI models balances high accuracy with high efficiency to allow enterprises to move beyond proof-of-concept into production grade AI.

Automaton AI's Automaton AI's DNN model and training data management tool, ADVIT, allows you to create, manage, and maintain high-quality models and training data in one place. Automated optimization of data and preparation for each stage of the computer vision pipeline. Automate data labeling and streamline data pipelines in house Automate the management of structured and unstructured video/image/text data and perform automated functions to refine your data before each step in the deep learning pipeline. You can train your own model with accurate data labeling and quality assurance. DNN training requires hyperparameter tuning such as batch size, learning rate, and so on. To improve accuracy, optimize and transfer the learning from trained models. After training, the model can be put into production. ADVIT also does model versioning. Run-time can track model development and accuracy parameters. A pre-trained DNN model can be used to increase the accuracy of your model for auto-labeling.

Claude Pro is a large language model that can handle complex tasks with a friendly and accessible demeanor. It is trained on high-quality, extensive data and excels at understanding contexts, interpreting subtleties, and producing well structured, coherent responses to a variety of topics. Claude Pro is able to create detailed reports, write creative content, summarize long documents, and assist with coding tasks by leveraging its robust reasoning capabilities and refined knowledge base. Its adaptive algorithms constantly improve its ability learn from feedback. This ensures that its output is accurate, reliable and helpful. Whether Claude Pro is serving professionals looking for expert support or individuals seeking quick, informative answers - it delivers a versatile, productive conversational experience.

A new category of AI. Most AI solutions today are designed using a mathematical and statistical lens. We took a different approach. Intelligent Artifacts' team has created a new type of AI based on information theory. It is a true AGI that eliminates the current shortcomings in machine intelligence. Our framework separates the intelligence layer from the data and application layers, allowing it to learn in real time and allowing it to make predictions down to the root cause. A truly integrated platform is required for AGI. Intelligent Artifacts will allow you to model information, not data. Predictions and decisions can be made across multiple domains without the need for rewriting code. Our dynamic platform and specialized AI consultants will provide you with a tailored solution that quickly provides deep insights and better outcomes from your data.

Lamini allows enterprises to transform proprietary data into next-generation LLM capabilities by offering a platform that allows in-house software teams the opportunity to upgrade to OpenAI level AI teams, and build within the security provided by their existing infrastructure. Optimised JSON decoding guarantees a structured output. Fine-tuning retrieval-augmented retrieval to improve photographic memory. Improve accuracy and reduce hallucinations. Inferences for large batches can be highly parallelized. Parameter-efficient finetuning for millions of production adapters. Lamini is the sole company that allows enterprise companies to develop and control LLMs safely and quickly from anywhere. It uses the latest research and technologies to create ChatGPT, which was developed from GPT-3. These include, for example, fine-tuning and RLHF.

Neural Designer is a data-science and machine learning platform that allows you to build, train, deploy, and maintain neural network models. This tool was created to allow innovative companies and research centres to focus on their applications, not on programming algorithms or programming techniques. Neural Designer does not require you to code or create block diagrams. Instead, the interface guides users through a series of clearly defined steps. Machine Learning can be applied in different industries. These are some examples of machine learning solutions: - In engineering: Performance optimization, quality improvement and fault detection - In banking, insurance: churn prevention and customer targeting. - In healthcare: medical diagnosis, prognosis and activity recognition, microarray analysis and drug design. Neural Designer's strength is its ability to intuitively build predictive models and perform complex operations.

Censius, an innovative startup in machine learning and AI, is a pioneering company. We provide AI observability for enterprise ML teams. With the extensive use machine learning models, it is essential to ensure that ML models perform well. Censius, an AI Observability platform, helps organizations of all sizes to make their machine-learning models in production. The company's flagship AI observability platform, Censius, was launched to help bring accountability and explanation to data science projects. Comprehensive ML monitoring solutions can be used to monitor all ML pipelines and detect and fix ML problems such as drift, skew and data integrity. After integrating Censius you will be able to: 1. Keep track of the model vitals and log them 2. By detecting problems accurately, you can reduce the time it takes to recover. 3. Stakeholders should be able to understand the issues and recovery strategies. 4. Explain model decisions 5. Reduce downtime for end-users 6. Building customer trust

Jukebox is a neural net that generates music. It can also rudimentary sing. We are releasing the code and model weights, as well as a tool to help you explore the generated samples. Jukebox generates a new music sample from scratch when you provide genre, artist, lyrics, and other inputs. Jukebox can produce a wide variety of music and singing styles, and it can also generalize to lyrics that were not present during training. All of the below lyrics were co-written by OpenAI researchers and a language model. Jukebox creates songs that are very different from the original songs when it is trained using lyrics learned during training. Jukebox provides 12 seconds of audio for conditioning and then completes the rest in a specific style. We chose music to work on because we want to push the boundaries of generative modeling. Jukebox's autoencoder compresses audio into a discrete space using a quantization-based method called VQ-VAE.

We have been pioneers in the development of clinical NLP platforms and their applications for over 15 years. This has resulted in high precision and accuracy. Our core competency is to interpret unstructured notes accurately and at scale. Tested on billions of real clinical notes and documents. AI that can explain with context, reasoning, and evidence for output. NLP with medical knowledge infused with 4M+ entities and 50M+ relationships. Innovative Machine Learning (ML), & Deep Learning(DL) models were used to build this NLP. Use a foundation of rich ontologies and clinician-specific terminologies. We can understand, interpret, and extract context & significance from the inconsistent, inconsistent, and non-standard data contained in medical documents. Our clinical domain experts continually infuse knowledge graphs to our NLP by mapping all clinical entities and their relationship between them. We have more than 4,000,000 entities and 50,000,000 relationships.

Keymakr’s advanced video annotation tools, along with our professional in-house team of annotation experts, ensure the best possible results for your computer vision data needs. Annotating video while tracking objects across multiple frames. Each object in the video can be tracked and recognized even if it is captured by different cameras or video segments. We use a variety of media to create precise and human-annotated datasets. Our expertise in video annotation and labeling allows us to create custom machine-learning data sets that meet your project's needs. Improve the accuracy of existing models. We check and correct all algorithmic outputs including bounding boxes, polygon annotating, instance segmentation and semantic segmentation. Computer vision development is a complex process that requires quality training data.

OpenPipe provides fine-tuning for developers. Keep all your models, datasets, and evaluations in one place. New models can be trained with a click of a mouse. Automatically record LLM responses and requests. Create datasets using your captured data. Train multiple base models using the same dataset. We can scale your model to millions of requests on our managed endpoints. Write evaluations and compare outputs of models side by side. You only need to change a few lines of code. OpenPipe API Key can be added to your Python or Javascript OpenAI SDK. Custom tags make your data searchable. Small, specialized models are much cheaper to run than large, multipurpose LLMs. Replace prompts in minutes instead of weeks. Mistral and Llama 2 models that are fine-tuned consistently outperform GPT-4-1106 Turbo, at a fraction the cost. Many of the base models that we use are open-source. You can download your own weights at any time when you fine-tune Mistral or Llama 2.

Use streamlined workflows to change the way you view and work with data. Quickly generate cross sections and use tools to integrate your models with engineering plans. Rapid creation and updating geological models will increase the productivity of your subsurface 3D modelling. Your models and outputs, such as cross sections, are automatically updated when new data is added. This saves both time and money. 3D subsurface modeling offers unparalleled accuracy and efficiency when it comes to understanding ground conditions. You can identify and assess risks earlier in the project's lifecycle. Subsurface insights in 3D bring clarity to complex data and give you a better understanding. Visual 3D subsurface models are more effective in helping you understand ground conditions.

CubeBot Pro allows organizations to create personalized AI chatbots using their own data. CubeBot Pro uses advanced AI models to provide a better user-experience and accurate responses, without any hallucinations. It's more than a chatbot. It's a professional solution that offers comprehensive support for customizations, integrations and integrations. Key Features • Custom chatbots based on the latest technologies and your content. • A quick and accurate response based on the data you provide, without hallucinations. • Upload your documents easily and train the chatbot. • We can upload more than 100 different types of documents. • Support for +90 languages is available. • We use advanced AI models for accurate responses. • Use the chatbot on your website or application. • We provide API access. • Dashboard with many features for managing the chatbot. • Microsoft Azure Cloud: All data is secure.

Deep reinforcement learning (DRL), combines the benefits of deep learning with the power of reinforcement learning by combining the power of sequential decision making with reinforcement learning. Learnable's DRL AI can self-produce extensive simulation data, which allows for continuous self-upgrades and optimal predictive output. Learnable has created three types of AI models, each with different capabilities, using DRL technologies. Interactive AI learns by interacting with humans. Interactive AI's sophisticated ability to understand different types of human feedback allows it to build a cognitive system that can interpret human intent through interactions. eXplainableAI (XAI), is able to understand the deep logic behind events, actions, and rewards, much like the human brain. XAI is able to explain its decisions, which is different from other forms AI.

Our templates make it easy to create questions quickly. Import photos, add graphics, and highlight important information. With PowerPoint, anyone can create questions that allow peer-to-peer game development. Television game shows can be entertaining, but training modules must be structured so that they are easy to learn from. The game show host must explain the answers and explain why they were wrong. This Jeopardy-style format divides questions into categories, with increasing points values for fast-paced team play. These categories are great for covering large amounts of facts and details. For an exciting team ring-in, add slammers. Make classroom and virtual training engaging, fun, and measurable. AllPlay is fun for everyone, so everyone is involved.

Silent Install Builder was created to allow users to create silent setup packages that allow them install multiple applications on multiple computers. Silent Install Builder allows you to create multiple installers. These are recognized by the program. The supported categories are InstallShield (Inno Setup), Wise and NSIS. It is easy to get used to the application thanks to its two-sided interface. The left pane shows the components of your project in a well-organized manner. While the right side allows you to edit the properties of each component. You can provide detailed information about your package, including the name of the product, version, manufacturing company and a brief file description. A welcome message is also included in the final output. It is also necessary to specify the location of the actual installer files.

Our Marketing Analytics and Optimization software provides a modern integrated environment for advanced investment analytics. The data visualization module allows users to examine and understand the visuals and statistics required to verify the data as well as form initial insights and hypothesis on what is driving demand. The core Modeling Module provides a comprehensive user-interface (UI) to specify a range dynamic linear panel models across all levels of client business. The user-specified models are integrated into the model database, which performs any required variable transformation. This is then transferred to OxMetrics' proprietary model estimation algorithm.

The ability of large language models to learn in zero- and few shots, despite being trained for hundreds of thousands or even millions of days, has been remarkable. These models are expensive to replicate, due to their high computational cost. The few models that are available via APIs do not allow access to the full weights of the model, making it difficult to study. Open Pre-trained Transformers is a suite decoder-only pre-trained transforms with parameters ranging from 175B to 125M. We aim to share this fully and responsibly with interested researchers. We show that OPT-175B has a carbon footprint of 1/7th that of GPT-3. We will also release our logbook, which details the infrastructure challenges we encountered, as well as code for experimenting on all of the released model.

Your web application will have beautifully formatted text. Trix is a text editor that can be used to create messages, comments, articles and lists. These are the basic documents that most web apps are built from. It supports embedded attachments and produces consistent HTML outputs. WYSIWYG editors rely on wrappers for HTML's contenteditable, execCommand APIs. These APIs were created by Microsoft to allow live editing of web pages using Internet Explorer 5.5. They can be reverse-engineered and then copied by other browsers. These APIs were not fully documented or specified, and WYSIWYG HTML editors have a large scope, so each browser's implementation will have its own bugs and quirks. JavaScript developers are left to fix these inconsistencies. Trix avoids these inconsistencies by treating contenteditable like an I/O device. When input reaches the editor, Trix converts it into an editing operation on its internal model.

Gemma is the family of lightweight open models that are built using the same research and technology as the Gemini models. Gemma was developed by Google DeepMind, along with other teams within Google. The name is derived from the Latin gemma meaning "precious stones". We're also releasing new tools to encourage developer innovation, encourage collaboration, and guide responsible use of Gemma model. Gemma models are based on the same infrastructure and technical components as Gemini, Google's largest and most powerful AI model. Gemma 2B, 7B and other open models can achieve the best performance possible for their size. Gemma models can run directly on a desktop or laptop computer for developers. Gemma is able to surpass much larger models in key benchmarks, while adhering our rigorous standards of safe and responsible outputs.

Google is a software organization located in the United States that was started in 1998 and provides software named Gemini 2.0. Gemini 2.0 includes training through documentation. Cost begins at Free. Gemini 2.0 has a free version. Gemini 2.0 is a type of AI art generators software. Gemini 2.0 is offered as SaaS software. Some alternatives to Gemini 2.0 are ChatGPT, Gemini, and SinCode AI.

The workflow automation platform that automates complex, mission-critical data processing and ML processes at large scale. Flyte makes it simple to create machine learning and data processing workflows that are concurrent, scalable, and manageable. Flyte is used for production at Lyft and Spotify, as well as Freenome. Flyte is used at Lyft for production model training and data processing. It has become the de facto platform for pricing, locations, ETA and mapping, as well as autonomous teams. Flyte manages more than 10,000 workflows at Lyft. This includes over 1,000,000 executions per month, 20,000,000 tasks, and 40,000,000 containers. Flyte has been battle-tested by Lyft and Spotify, as well as Freenome. It is completely open-source and has an Apache 2.0 license under Linux Foundation. There is also a cross-industry oversight committee. YAML is a useful tool for configuring machine learning and data workflows. However, it can be complicated and potentially error-prone.

Azure AI Language is an Azure managed service that allows you to develop applications for natural language processing. You can identify key terms and phrases, analyze emotion, summarize text and build conversational interfaces. Use Language to annotate AI models, train them, evaluate them, and deploy them with minimal machine learning expertise. Out-of-the-box capabilities, such as predefined entity categories for each business or text analytics for healthcare domains can help you get up and running quickly. You can also customize and optimize them when necessary. To train your machine-learning model, provide a few labeled example sentences. Multilingual models can be created in one language, and then used for many other languages. Language Studio allows you to scan your content and quickly suggest labels using advanced language models powered by GPT. Text can be categorized and labelled to extract vital information.

The docBrain platform combines machine learning, data science and DevOps to create a document-centric productive purpose. Deep learning technology makes it possible to build AI models from scratch and create unique solutions for your specific document problems. docBrain's pre-trained models provide years of learning and a low return on investment. Whether you use the pre-trained models or train them yourself, docBrain's solutions will seamlessly integrate with your business systems. docBrain was developed in-house to address Moonoia's document processing problems. These were mainly caused by manual data validation errors that were costly and slowing down the end-to-end process, making automation impossible. OCR technologies on the market were not able to achieve the accuracy required for straight-through processing, especially when dealing with handwritten, unstructured, or low-quality documents.

E2E modeling that combines Business perspective and subject matter expertise with Data science (Statistical Models +ML + Business context & objectives). This combination is vital to the health and competitive advantage for the BFSI. • Models created on KuantSol can be used for long periods of times and are stable, optimal, and standardized. • Submission-ready standardized model documentation for federal regulators • Executives can easily understand the end model thanks to purpose-built configuration options at each decision step. For example, the top ML/AI vendors offer a few model options as well as selection criteria. While consulting firms may offer more, it would take more time and expertise. KuantSol offers 150+ • KuantSol advanced configuration enables auto model development.

Symflower improves software development through the integration of static, dynamic and symbolic analyses, as well as Large Language Models. This combination takes advantage of the precision of deterministic analysis and the creativity of LLMs to produce higher quality and faster software. Symflower helps identify the best LLM for a specific project by evaluating models against real-world scenarios. This ensures alignment with specific environments and workflows. The platform solves common LLM problems by implementing automatic post- and pre-processing. This improves code quality, functionality, and efficiency. Symflower improves LLM performance by providing the right context via Retrieval - Augmented Generation (RAG). Continuous benchmarking ensures use cases are effective and compatible with latest models. Symflower also offers detailed reports that accelerate fine-tuning, training, and data curation.

TrainingCheck was developed by a group of experienced learning and development professionals as part a large-scale project that involved a variety of employers and training providers. We had to create a web-based tool for training evaluation that was both intuitive and grounded in theory. Participants from participating companies said that the tool should have ready-made content that could be used to help them evaluate training effectiveness at all levels. This could include participant satisfaction, business impact, and even business impact. Participants also stated that they wanted the tool to allow them to create and adapt their content to meet their specific needs. TrainingCheck was launched in summer 2011. It had over 1000 users the first year. We have grown strongly since then, and we are striving harder to provide the best functionality, service, and value for money.

2markdown makes it easy to convert URLs, HTML, and code into structured markdown. This gives AI models access to rich website contents. 2markdown is the best way to ensure seamless operation, whether you're enhancing AI, training datasets, integrating content into apps, or enhancing AI context. 2markdown's simple interface and efficient processing ensures accurate and quick data extraction. It's ideal for AI developers, researchers and analysts who want to streamline the preparation and analysis of web data.

StableCode is a unique tool that helps developers become more efficient in their coding by using three models. The base model was trained first on a diverse collection of programming languages using the stack-dataset from BigCode, and then further trained with popular languages such as Python, Go, Java, Javascript, C, markdown and C++. We trained our models using 560B tokens on our HPC Cluster. After the base model was established, the instruction models were tuned for specific usecases to help solve complex programming problems. To achieve this result, 120,000 code instructions/response pair in Alpaca format was trained on the base. StableCode is a great tool for anyone who wants to learn more about programming. The long-context model is a great assistant for ensuring that autocomplete suggestions for single- and multiple-line input are available to the user. This model was designed to handle a large amount of code at once.

We use AI to create an excellent profile photo for you. We use your photos to create an AI model that is unique to you. We create AI avatars/profile photos for you. The output is very realistic and your model is not shared. You can delete both your model and photos at any time. You can download, share, delete, sell, get tattoos on your chest or build giant heroic stone sculptures. Our reputation for treating your data with care is the foundation of our business.

Standardize your data across multiple formats and inputs by extracting, normalizing, and standardizing it. Normalize all information, both business and personal, structured or unstructured. The supervised machine learning neural networks-based techniques are used to precisely understand the structure and variations in different types of information. Data is automatically parsed using these techniques. Spectrum Quality is well-suited for global clients who require multi-level data standardization, transliteration, and translation for multiple languages and culturally relevant terms. This includes those in Arabic and Chinese, Japanese, Korean, and Japanese. Our text-processing technology allows information extraction from any input text in natural languages and assigns categories to unstructured texts. You can use pre-trained models and machine-learning based algorithms to extract entities and further train your models to customize your models to identify specific entities of any type or domain.

Gemini models are a family of light-open, state-of-the art models that was created using the same research and technology as Gemini models. These models include comprehensive security measures, and help to ensure responsible and reliable AI through selected data sets. Gemma models have exceptional comparative results, even surpassing some larger open models, in their 2B and 7B sizes. Keras 3.0 offers seamless compatibility with JAX TensorFlow PyTorch and JAX. Gemma 2 has been redesigned to deliver unmatched performance and efficiency. It is optimized for inference on a variety of hardware. The Gemma models are available in a variety of models that can be customized to meet your specific needs. The Gemma models consist of large text-to text lightweight language models that have a decoder and are trained on a large set of text, code, or mathematical content.

Large Language Models (LLMs), which are based on vast datasets that may include sensitive information, can learn from them. This can lead to unintentional data disclosure and exploitation from unethical LLM providers, or malicious actors. ZeroTrusted.ai’s LLM Firewall was designed to protect you against these threats. Our sanitization is intelligent enough for GDPR, PHI and PII. It is constantly trained using compliance requirements data from NIST. You can automate the process while meeting your compliance requirements. There are many other features, such as prompt optimization, hallucination reduction and data injection with our LLM ensemble technique. These features are available only on the business plan.

Vensim®, the software of choice of thousands of analysts and consultants worldwide, is used to build high-quality simulations in business, scientific and environmental systems. Vensim integrates a powerful set of tools to develop, test, interpret, and distribute models. These tools include cause and effect diagramming, graphical or textual model constructions, easy reproductions of model structure by using subscripts and arrays, Monte Carlo sensitivity analyses, optimization, data management, and application interfaces. Additional features allow for advanced model resolution and fidelity. Vensim uses patented techniques for error detection, prevention, and rapid understanding of complex results.

Test and verify data management for IBM iSeries, IBM iSeries and AS/400. Complex IBM i applications need to be tested from top to bottom and into the data wherever it is. TestBench IBM I is a proven, comprehensive solution for test data management, verification, and unit testing that integrates with other solutions to ensure application quality. Stop copying all the data and focus on what you need. You can select or sample data that has been kept in full referential integrity. You can choose which fields should be protected and then use a variety obfuscation techniques to protect your data. Track every insert, update, and delete. You can create rules to ensure that data failures are automatically flagged. Avoid painful save/restores, and stop trying to explain bad test results that were based on poor data. Although it is a proven method of verifying your test results, comparing outputs can be tedious and prone to error. This unique solution can save you hours.

Automated variable selection allows you to identify key variables and their interactions. Insightful visualization of data & model dynamics. Execution of batch commands. SQL queries and data browsing. Pre & post-processing for creating variables, constraining outputs, etc. Models can be easily deployed using ActiveX (i.e. OCX) controls and DLLs. Regression, neural networks and self-organizing maps are all powerful modeling algorithms. Predictive Dynamix is a computational intelligence software that can be used for forecasting, prediction modeling, pattern recognition and classification, as well as optimization applications. It can be used in all industries. Modern neural network technology is a powerful computational structure that can solve difficult problems such as pattern recognition and forecasting. Multi-layer perceptron neural network architecture allows multiple coefficients for each input variable.

With a suite observability tools, you can confidently evaluate, test and ship LLM apps across your development and production lifecycle. Log traces and spans. Define and compute evaluation metrics. Score LLM outputs. Compare performance between app versions. Record, sort, find, and understand every step that your LLM app makes to generate a result. You can manually annotate and compare LLM results in a table. Log traces in development and production. Run experiments using different prompts, and evaluate them against a test collection. You can choose and run preconfigured evaluation metrics, or create your own using our SDK library. Consult the built-in LLM judges to help you with complex issues such as hallucination detection, factuality and moderation. Opik LLM unit tests built on PyTest provide reliable performance baselines. Build comprehensive test suites for every deployment to evaluate your entire LLM pipe-line.

Understanding the relationships between data is key to unlocking the true value of continuously changing business data. A graph database, unlike other databases, puts relationships first. It uses Linear Algebra and graph theory to explore and show how complex data webs, sources, and points relate. TIBCO®, Graph Database allows users to store, transform, and interpret complex dynamic data into meaningful insights. Users can quickly build data and computational models that create dynamic relationships between organizational silos. These knowledge graphs provide value by connecting the vast array of data in your organization and revealing relationships that allow you to optimize assets and processes. OLTP and OLAP features combined in a single enterprise-grade data base. Optimistic ACID-level transaction properties with native storage access.

Wallaroo is the last mile of your machine-learning journey. It helps you integrate ML into your production environment and improve your bottom line. Wallaroo was designed from the ground up to make it easy to deploy and manage ML production-wide, unlike Apache Spark or heavy-weight containers. ML that costs up to 80% less and can scale to more data, more complex models, and more models at a fraction of the cost. Wallaroo was designed to allow data scientists to quickly deploy their ML models against live data. This can be used for testing, staging, and prod environments. Wallaroo supports the most extensive range of machine learning training frameworks. The platform will take care of deployment and inference speed and scale, so you can focus on building and iterating your models.

SQLAlchemy, the Python SQL toolkit and the object-relational mapping program that gives developers the full power of SQL, is SQLAlchemy. SQL databases behave less as object collections when performance and size start to matter. Object collections behave less like rows and tables the more abstraction starts mattering. SQLAlchemy is designed to accommodate both these principles. SQLAlchemy views the database as a relational algebra engine and not just a collection table. Rows can be selected not only from tables, but also joins or select statements. Any of these units can be combined into a larger structure. This idea is the basis of SQLAlchemy’s expression language. SQLAlchemy's object-relational mappingper (ORM) is the most well-known component. This optional component provides the data mapper pattern.

It reduces cloud costs and frees users from vendor-lock-in. Configure your hardware resources such as GPU and memory and specify whether you prefer to use spot instances. dstack provision cloud resources, fetches code and forwards ports to secure access. You can access the cloud dev environment using your desktop IDE. Configure your hardware resources (GPU, RAM, etc.). Indicate whether you would like to use spot instances or on-demand instances. dstack automatically provision cloud resources, forward ports and secure access. Pre-train your own models and fine-tune them in any cloud, easily and cost-effectively. Do you want cloud resources to be provisioned automatically based on your configurations? You can access your data and store outputs artifacts by using declarative configurations or the Python SDK.

Open IFC Viewer, a robust and open-source tool, is used to view and navigate IFC (Industry Foundation Classes), which are commonly used for building information modeling. This software offers multiple visualization options. A tree view allows users to navigate IFC models' hierarchical structure, revealing the attributes of each node. Users can use advanced search functions to find specific instances based on their type or global identification and track references between model elements. Open IFC Viewer comes with a validation plug-in that verifies model accuracy using schema standards such as attribute consistency and structure integrity. Users can highlight, hide, or isolate objects in the scene, and customize lighting parameters for enhanced model visualization. The interactive instance viewer in the software allows users to explore entity definitions and attribute value values specified in IFC files.

Core ML creates a model by applying a machine-learning algorithm to a collection of training data. A model is used to make predictions using new input data. Models can perform a variety of tasks which would be difficult to code or impractical. You can train a model, for example, to categorize images or detect specific objects in a photo based on its pixels. After creating the model, you can integrate it into your app and deploy on the device of the user. Your app uses Core ML and user data to make forecasts and train or fine-tune a model. Create ML, which is bundled with Xcode, allows you to build and train a ML model. Create ML models are Core ML formatted and ready to be used in your app. Core ML Tools can be used to convert models from other machine learning libraries into Core ML format. Core ML can be used to retrain a model on the device of a user.