Alternatives to Picterra

Speechmatics is the most accurate and inclusive speech-to-text API ever released. Speechmatics is the world’s leading expert in Speech Technology, combining the latest breakthroughs in AI and ML to unlock the business value in human speech. Businesses use Speechmatics worldwide to accurately understand and transcribe human-level speech into text regardless of demographic, age, gender, accent, dialect, or location in real-time and on recorded media. Combining these transcripts with the latest AI-driven speech capabilities, businesses build products that utilize summarization, topic detection, sentiment analysis, translation, and more. How is Speechmatics different? * The most accurate speech recognition on the market * 55 languages with vast accent and dialect coverage * Cloud-based or on-premises deployment options for data security * Real-time transcription with low latency and high accuracy * Real-time translation with 69 language pairs * Speech Understanding features such as Summaries, Sentiment, Topic Detection, Chapters, Audio Events * Fast and secure transcriptions for pre-recorded audio * Automatic translation and language identification * A culture of R&D in deep learning and speech recognition

Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case. Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection. Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.

Union.ai Benefits: - Accelerated Data Processing & ML: Union.ai significantly speeds up data processing and machine learning. - Built on Trusted Open-Source: Leverages the robust open-source project Flyte™, ensuring a reliable and tested foundation for your ML projects. - Kubernetes Efficiency: Harnesses the power and efficiency of Kubernetes along with enhanced observability and enterprise features. - Optimized Infrastructure: Facilitates easier collaboration among Data and ML teams on optimized infrastructures, boosting project velocity. - Breaks Down Silos: Tackles the challenges of distributed tooling and infrastructure by simplifying work-sharing across teams and environments with reusable tasks, versioned workflows, and an extensible plugin system. - Seamless Multi-Cloud Operations: Navigate the complexities of on-prem, hybrid, or multi-cloud setups with ease, ensuring consistent data handling, secure networking, and smooth service integrations. - Cost Optimization: Keeps a tight rein on your compute costs, tracks usage, and optimizes resource allocation even across distributed providers and instances, ensuring cost-effectiveness.

Manage unstructured data to develop AI solutions in record time. Enterprise-grade data platform with vision AI. Dataloop offers a single-stop-shop for building and deploying powerful data pipelines for computer vision, data labeling, automation of data operations, customizing production pipelines, and weaving in the human for data validation. Our vision is to make machine-learning-based systems affordable, scalable and accessible for everyone. Explore and analyze large quantities of unstructured information from diverse sources. Use automated preprocessing to find similar data and identify the data you require. Curate, version, cleanse, and route data to where it's required to create exceptional AI apps.

The training data platform for AI teams. A machine learning model can only be as good as the training data it uses. Labelbox is an integrated platform that allows you to create and manage high quality training data in one place. It also supports your production pipeline with powerful APIs. A powerful image labeling tool for segmentation, object detection, and image classification. You need precise and intuitive image segmentation tools when every pixel is important. You can customize the tools to suit your particular use case, including custom attributes and more. The performant video labeling editor is for cutting-edge computer visual. Label directly on the video at 30 FPS, with frame level. Labelbox also provides per-frame analytics that allow you to create faster models. It's never been easier to create training data for natural language intelligence. You can quickly and easily label text strings, conversations, paragraphs, or documents with fast and customizable classification.

NovaFori We are a cutting-edge technology firm based in London, Malaga. With a decade of experience in business analysis, market design, development, and data science, we can offer you the best. Our technology supports B2B clients in Europe, North America, and Asia. We have processed more than $11 billion in GMV through our platforms since our inception. The Platform Our data science-powered auction and trading platform is used in multiple industries including procurement, financial services, and logistics. The technology platform is modular, flexible, scalable, and modular. It was designed with the B2C user in mind and complex product attributes for the B2B market. Data Science Machine learning algorithms are used to analyse data and predict future trends, optimise market performance, and leverage data.

Deep Block is a no-code platform to train and use your own AI models based on our patented Machine Learning technology. Have you heard of mathematic formulas such as Backpropagation? Well, I had once to perform the process of converting an unkindly written system of equations into one-variable equations. Sounds like gibberish? That is what I and many AI learners have to go through when trying to grasp basic and advanced deep learning concepts and when learning how to train their own AI models. Now, what if I told you that a kid could train an AI as well as a computer vision expert? That is because the technology itself is very easy to use, most application developers or engineers only need a nudge in the right direction to be able to use it properly, so why do they need to go through such a cryptic education? That is why we created Deep Block, so that individuals and enterprises alike can train their own computer vision models and bring the power of AI to the applications they develop, without any prior machine learning experience. You have a mouse and a keyboard? You can use our web-based platform, check our project library for inspiration, and choose between out-of-the-box AI training modules.

FlyPix AI is a platform for advanced object detection that analyzes satellite and drone images. FlyPix AI allows users to detect, segment and locate objects and areas. It also tracks changes and detects anomalies in geospatial data. Its intuitive and user-friendly interface allows users to create customized use case and extract valuable data from earth observation data without any coding knowledge.

OpenCV (Open Source Computer Vision Library), is an open-source machine learning and computer vision software library. OpenCV was created to provide a common infrastructure to support computer vision applications and accelerate machine perception in commercial products. OpenCV is a BSD-licensed product that makes it easy to modify and use the code by businesses. The library contains more than 2500 optimized algorithms. This includes a comprehensive set both of classic and modern computer vision and machine-learning algorithms. These algorithms can be used for recognizing faces, identifying objects, tracking camera movements, classifying human actions in videos and producing 3D point clouds from stereo-cameras. They can also be used to stitch images together to create a high resolution image of the entire scene, find similar images from a database, remove red eyes from images taken with flash, recognize scenery, and follow eye movements.

Advanced embedded machine learning applications can be built without a PhD. To create custom datasets, collect sensor, audio, and camera data directly from devices, files or cloud integrations. Automated labeling tools, from object detection to audio segmentation, are available. Our cloud infrastructure allows you to set up and execute reusable scripted tasks that transform large amounts of input data. Integrate custom data sources, CI/CD tool, and deployment pipelines using open APIs. With ready-to-use DSPs and ML algorithms, you can accelerate the development of custom ML pipelines. Every step of the process, hardware decisions are made based on flash/RAM and device performance. Keras APIs allow you to customize DSP feature extraction algorithms. You can also create custom machine learning models. Visualized insights on model performance, memory, and datasets can fine-tune your production model. Find the right balance between DSP configurations and model architecture. All this is budgeted against memory constraints and latency constraints.

Easily extract, transform, manage & process CAD data, FE data, CFD and test results. Create automatic data pipelines to support machine learning, deep learning, and ROM. Data science barriers can be removed without coding. Key Ward's platform, the first engineering no-code end-to-end solution, redefines how engineers work with their data. Our software allows engineers to handle multi-source data with ease, extract direct value using our built-in advanced analytical tools, and build custom machine and deep learning model with just a few clicks. Automatically centralize, update and extract your multi-source data, then sort, clean and prepare it for analysis, machine and/or deep learning. Use our advanced analytics tools to correlate, identify patterns, and find dependencies in your experimental & simulator data.

Amazon EC2 Inf1 instances were designed to deliver high-performance, cost-effective machine-learning inference. Amazon EC2 Inf1 instances offer up to 2.3x higher throughput, and up to 70% less cost per inference compared with other Amazon EC2 instance. Inf1 instances are powered by up to 16 AWS inference accelerators, designed by AWS. They also feature Intel Xeon Scalable 2nd generation processors, and up to 100 Gbps of networking bandwidth, to support large-scale ML apps. These instances are perfect for deploying applications like search engines, recommendation system, computer vision and speech recognition, natural-language processing, personalization and fraud detection. Developers can deploy ML models to Inf1 instances by using the AWS Neuron SDK. This SDK integrates with popular ML Frameworks such as TensorFlow PyTorch and Apache MXNet.

Machine learning uncovers hidden patterns in enterprise data and generates new value for businesses. Oracle Machine Learning makes it easier to create and deploy machine learning models for data scientists by using AutoML technology and reducing data movement. It also simplifies deployment. Apache Zeppelin notebook technology, which is open-source-based, can increase developer productivity and decrease their learning curve. Notebooks are compatible with SQL, PL/SQL and Python. Users can also use markdown interpreters for Oracle Autonomous Database to create models in their preferred language. No-code user interface that supports AutoML on Autonomous Database. This will increase data scientist productivity as well as non-expert users' access to powerful in-database algorithms to classify and regression. Data scientists can deploy integrated models using the Oracle Machine Learning AutoML User Interface.

TrueFoundry provides data scientists and ML engineers with the fastest framework to support the post-model pipeline. With the best DevOps practices, we enable instant monitored endpoints to models in just 15 minutes! You can save, version, and monitor ML models and artifacts. With one command, you can create an endpoint for your ML Model. WebApps can be created without any frontend knowledge or exposure to other users as per your choice. Social swag! Our mission is to make machine learning fast and scalable, which will bring positive value! TrueFoundry is enabling this transformation by automating parts of the ML pipeline that are automated and empowering ML Developers with the ability to test and launch models quickly and with as much autonomy possible. Our inspiration comes from the products that Platform teams have created in top tech companies such as Facebook, Google, Netflix, and others. These products allow all teams to move faster and deploy and iterate independently.

WEKA 4 delivers unprecedented performance and runs impossible workloads everywhere, without compromise. Artificial Intelligence is opening up new business opportunities. Operationalizing AI requires the ability of processing large amounts of data from multiple sources in a short amount time. WEKA is a complete solution that can be used to accelerate DataOps tasks across the entire data pipeline, whether it is on-prem or the public cloud. Modern methods are required to store and analyze large data sets in life science, whether they are next-generation sequencing, imaging, and microscopy. This will allow for better insights and economics. WEKA reduces the time it takes to get insights. It eliminates performance bottlenecks in the Life Sciences data pipeline and significantly reduces the cost and complexity of managing large amounts of data. WEKA is a modern storage architecture that can manage the most demanding I/O-intensive workloads as well as latency-sensitive applications at exabyte scale.

Oracle Analytics is a comprehensive platform designed for all analytics user roles, integrating AI and machine learning across the board to boost productivity and enable smarter business decisions. Whether you opt for Oracle Analytics Cloud, our cloud-native service, or Oracle Analytics Server, our on-premises solution, you can ensure robust security and governance without compromise.

Amazon DevOps Guru, powered by machine learning (ML), is a service that makes it easy to improve operational performance and availability of applications. DevOps Guru detects abnormal operating patterns and helps you to identify them before they impact your customers. To identify abnormal application behavior, such as increased latency, error rates or resource limitations, DevOps Guru employs ML models that are based on data collected over years by Amazon.com Operational Excellence and Amazon.com. It helps to detect critical errors that could cause service interruptions. The DevOps Guru automatically alerts you when it detects a critical issue. It provides context and details about the root cause and the possible consequences.

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

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.

Ametnes: A Streamlined Data App Deployment Management Ametnes is the future of data applications deployment. Our cutting-edge solution will revolutionize the way you manage data applications in your private environments. Manual deployment is a complex process that can be a security concern. Ametnes tackles these challenges by automating the whole process. This ensures a seamless, secure experience for valued customers. Our intuitive platform makes it easy to deploy and manage data applications. Ametnes unlocks the full potential of any private environment. Enjoy efficiency, security and simplicity in a way you've never experienced before. Elevate your data management game - choose Ametnes today!

PredictSense is an AI-powered machine learning platform that uses AutoML to power its end-to-end Machine Learning platform. Accelerating machine intelligence will fuel the technological revolution of tomorrow. AI is key to unlocking the value of enterprise data investments. PredictSense allows businesses to quickly create AI-driven advanced analytical solutions that can help them monetize their technology investments and critical data infrastructure. Data science and business teams can quickly develop and deploy robust technology solutions at scale. Integrate AI into your existing product ecosystem and quickly track GTM for new AI solution. AutoML's complex ML models allow you to save significant time, money and effort.

Arize's machine-learning observability platform automatically detects and diagnoses problems and improves models. Machine learning systems are essential for businesses and customers, but often fail to perform in real life. Arize is an end to-end platform for observing and solving issues in your AI models. Seamlessly enable observation for any model, on any platform, in any environment. SDKs that are lightweight for sending production, validation, or training data. You can link real-time ground truth with predictions, or delay. You can gain confidence in your models' performance once they are deployed. Identify and prevent any performance or prediction drift issues, as well as quality issues, before they become serious. Even the most complex models can be reduced in time to resolution (MTTR). Flexible, easy-to use tools for root cause analysis are available.

The Descartes Labs Platform was created to address some of the most pressing geospatial analysis questions in the world. The platform allows customers to quickly and efficiently build models and algorithms that transform their businesses. We help AI become a core competency by providing data scientists and their line of business colleagues with the best geospatial and modeling tools in one package. Our massive data archive and their own data can be used by data science teams to create models faster than ever before. Our cloud-based platform allows customers to rapidly and securely scale machine learning, statistical, or computer vision models to inform business decisions using powerful raster-based analytics. Our extensive API documentation, tutorials and guides, as well as demos, provide users with a rich knowledge base that allows them to quickly deploy high-value apps across a variety of industries.

One development environment for all data science workflows. Natively analyze your data without the need to switch between services. Data to training at scale Models can be built and trained 5X faster than traditional notebooks. Scale up model development using simple connectivity to Vertex AI Services. Access to data is simplified and machine learning is made easier with BigQuery Dataproc, Spark and Vertex AI integration. Vertex AI training allows you to experiment and prototype at scale. Vertex AI Workbench allows you to manage your training and deployment workflows for Vertex AI all from one location. Fully managed, scalable and enterprise-ready, Jupyter-based, fully managed, scalable, and managed compute infrastructure with security controls. Easy connections to Google Cloud's Big Data Solutions allow you to explore data and train ML models.

Businesses and developers can now create cutting-edge neural searches, generative AI and multimodal services using state of the art LMOps, LLOps, and cloud-native technology. Multimodal data is everywhere. From tweets to short videos on TikTok to audio snippets, Zoom meeting records, PDFs containing figures, 3D meshes and photos in games, there's no shortage of it. It is powerful and rich, but it often hides behind incompatible data formats and modalities. High-level AI applications require that one solve search first and create second. Neural Search uses AI for finding what you need. A description of a sunrise may match a photograph, or a photo showing a rose can match the lyrics to a song. Generative AI/Creative AI use AI to create what you need. It can create images from a description or write poems from a photograph.

You can uncover valuable insights by accurately recognising objects in images or videos. AI can be used to drive value in many areas, including spotting products in the right spot in shopping aisles and surveilling people at events in real-time. You can perform in-depth analysis by placing image objects in the right segments. Insurers can use AI-based algorithms to analyze auto and home damage to make more accurate claims. Get instant insights and take action when it is most important. AI algorithms allow for real-time processing, which can be used for many valuable purposes, such as face recognition. You can identify customer behavior from video and see it in real time. AI can reveal how customers interact and engage with brands and products to provide better experiences. AI-based analytics can be applied to satellite images to detect traffic in real time, analyze parking lots, segment buildings, and more.

Kraken is suitable for all data scientists and analysts. It is designed to be easy-to-use and no-code automated machine-learning platform. The Kraken no code automated machine learning platform (AutoML), simplifies and automates data science tasks such as data prep, data cleaning and algorithm selection. It also allows for model training and deployment. Kraken was designed with engineers and analysts in mind. If you've done data analysis before, you're ready! Kraken's intuitive interface and integrated SONAR(c), training make it easy for citizens to become data scientists. Data scientists can work more efficiently and faster with advanced features. You can use Excel or flat files for daily reporting, or just ad-hoc analysis. With Kraken's drag-and-drop CSV upload feature and the Amazon S3 connector, you can quickly start building models. Kraken's Data Connectors allow you to connect with your favorite data warehouse, business intelligence tool, or cloud storage.

SquareML is an advanced machine learning platform that does not require any coding. It was designed to make predictive modeling and advanced data analytics more accessible to everyone, especially in the healthcare industry. It allows users to harness machine-learning capabilities without extensive coding expertise, regardless of their technical expertise. The platform is specialized in data ingestion, including electronic health records and claims databases. It also includes medical devices and health information exchanges. The platform's key features include a data science lifecycle that requires no coding, generative AI for healthcare, unstructured conversion of data, diverse machine learning algorithms for predicting disease progression and patient outcomes, a library with pre-built models, and seamless integration to various healthcare data sources. SquareML's AI-powered insights are designed to streamline data processes, improve diagnostic accuracy, and improve the outcomes of patient care.

Datoin removes barriers to entry into Machine Learning by using Graphical Interface and No Code approach. It's designed to quickly turn your vision into reality. Re-using blocks over and over is the best way to reduce costs. The Datoin's Block Superstore has a wide range of blocks, including enterprise software connectors and ETL tools, machine-learning libraries, NLP libraries, cloud service integration, SaaS APIs, and machine learning libraries. The best thing about Datoin is that the blocks are constantly being added to the store as we cover more use cases. Pre-built machine learning models make it easy to get started quickly and eliminate the need for training. We have created and built blocks that solve common problems across all industries and functional areas. Edit existing apps to quickly test them if you are unsure of specific functionality or efficacy.

FinetuneFast allows you to fine-tune AI models, deploy them quickly and start making money online. Here are some of the features that make FinetuneFast unique: - Fine tune your ML models within days, not weeks - The ultimate ML boilerplate, including text-to-images, LLMs and more - Build your AI app to start earning online quickly - Pre-configured scripts for efficient training of models - Efficient data load pipelines for streamlined processing Hyperparameter optimization tools to improve model performance - Multi-GPU Support out of the Box for enhanced processing power - No-Code AI Model fine-tuning for simple customization - Model deployment with one-click for quick and hassle free deployment - Auto-scaling Infrastructure for seamless scaling of your models as they grow - API endpoint creation for easy integration with other system - Monitoring and logging for real-time performance monitoring

Your deep learning workload can be accelerated. AI model training and inference can speed up your time to value. Deep learning is becoming more popular as enterprises adopt it to gain and scale insight through speech recognition and natural language processing. Deep learning can read text, images and video at scale and generate patterns for recommendation engines. It can also model financial risk and detect anomalies. Due to the sheer number of layers and volumes of data required to train neural networks, it has been necessary to use high computational power. Businesses are finding it difficult to demonstrate results from deep learning experiments that were implemented in silos.

Paradise employs robust, unsupervised machine-learning and supervised deep learning technologies in order to increase interpretation and gain greater insight from the data. Generate attributes to extract valuable geological information and for input into machine learning analysis. Identify the attributes that have the greatest variance and contribution to a given set of attributes in a particular geologic setting. Display the neural classes (topology), and the associated colors resulting Stratigraphic analysis. These indicate the distribution of facies. Deep learning and machine learning can automatically detect faults. Compare machine learning classification results with other seismic attributes to traditional logs. In fraction of the time it takes to generate spectral and geometric decomposition attributes on a cluster compute nodes, you can do this in fraction of the time with a single machine.

Vaex.io aims to democratize the use of big data by making it available to everyone, on any device, at any scale. Your prototype is the solution to reducing development time by 80%. Create automatic pipelines for every model. Empower your data scientists. Turn any laptop into an enormous data processing powerhouse. No clusters or engineers required. We offer reliable and fast data-driven solutions. Our state-of-the art technology allows us to build and deploy machine-learning models faster than anyone else on the market. Transform your data scientists into big data engineers. We offer comprehensive training for your employees to enable you to fully utilize our technology. Memory mapping, a sophisticated Expression System, and fast Out-of-Core algorithms are combined. Visualize and explore large datasets and build machine-learning models on a single computer.

Machine learning (ML) allows you to detect machine problems before they happen and take immediate action. Easy installation and secure analysis via the Amazon Monitron end to end system allow you to quickly monitor your equipment. Amazon Monitron continuously improves system accuracy by analyzing technician feedback through the web and mobile apps. Amazon Monitron is an end to end system that uses machine-learning to detect abnormal conditions in industrial equipment. This allows for predictive maintenance. Easy-to-install hardware combined with the power of machine learning allows you to save money and prevent equipment from going down. Predictive maintenance and machine learning can reduce unplanned equipment downtime. Amazon Monitron uses machine-learning to analyze temperature and vibration data. Amazon Monitron helps you predict equipment failures before they happen. Compare the cost of getting started with the savings you could make.

Datuum is an AI-powered data integration tool that offers a unique solution for organizations looking to streamline their data integration process. With our pre-trained AI engine, Datuum simplifies customer data onboarding by allowing for automated integration from various sources without coding. This reduces data preparation time and helps establish resilient connectors, ultimately freeing up time for organizations to focus on generating insights and improving the customer experience. At Datuum, we have over 40 years of experience in data management and operations, and we've incorporated our expertise into the core of our product. Our platform is designed to address the critical challenges faced by data engineers and managers while being accessible and user-friendly for non-technical specialists. By reducing up to 80% of the time typically spent on data-related tasks, Datuum can help organizations optimize their data management processes and achieve more efficient outcomes.

Emly Labs, an AI framework, is designed to make AI accessible to users of all technical levels via a user-friendly interface. It offers AI project-management with tools that automate workflows for faster execution. The platform promotes team collaboration, innovation, and data preparation without code. It also integrates external data to create robust AI models. Emly AutoML automates model evaluation and data processing, reducing the need for human input. It prioritizes transparency with AI features that are easily explained and robust auditing to ensure compliance. Data isolation, role-based accessibility, and secure integrations are all security measures. Emly's cost effective infrastructure allows for on-demand resource provisioning, policy management and risk reduction.

A platform that offers a variety of machine learning algorithms to meet data mining and analysis needs. Machine Learning Platform for AI offers end-to-end machine-learning services, including data processing and feature engineering, model prediction, model training, model evaluation, and model prediction. Machine learning platform for AI integrates all these services to make AI easier than ever. Machine Learning Platform for AI offers a visual web interface that allows you to create experiments by dragging components onto the canvas. Machine learning modeling is a step-by-step process that improves efficiency and reduces costs when creating experiments. Machine Learning Platform for AI offers more than 100 algorithm components. These include text analysis, finance, classification, clustering and time series.

Pachyderm's Data Versioning provides teams with an automated and efficient way to track all data changes. File-based versioning allows for a complete audit trail of all data and artifacts across the pipeline stages, including intermediate results. Versioning can be automated and guaranteed because they are native objects, not metadata pointers. Without writing additional code, autoscale data processing by parallel. Incremental processing reduces computation by only processing the differences and automatically skipping duplicates. Pachyderm's Global IDs allow teams to track any result back to its raw input. This includes all analysis, parameters, codes, and intermediate results. The Pachyderm Console allows you to see your DAG (directed-acyclic graph) and helps with reproducibility using Global IDs.

ML Kit provides Google's machine-learning expertise to mobile developers in a simple-to-use package. Optimized for Android and iOS devices, these solutions will make your apps more engaging, personalized and helpful. The processing of ML Kit takes place on-device. This makes it extremely fast and allows for real-time applications such as processing camera input. It can also be used offline to process images and text that must remain on the device. Make use of the machine learning technology that powers Google's mobile experiences. Our machine learning models are complemented by advanced processing pipelines. We offer these APIs through simple-to-use APIs that allow you to create powerful use cases for your apps. Handwritten text and handdrawn forms can be recognized on a digital surface such as a touch screen. Recognizes more than 300 languages, emojis, and basic shapes.

Apache Mahout is an incredibly powerful, scalable and versatile machine-learning library that was designed for distributed data processing. It provides a set of algorithms that can be used for a variety of tasks, such as classification, clustering and recommendation. Mahout is built on top of Apache Hadoop and uses MapReduce and Spark for data processing. Apache Mahout(TM), a distributed linear-algebra framework, is a mathematically expressive Scala DSL that allows mathematicians to quickly implement their algorithms. Apache Spark is recommended as the default distributed back-end, but can be extended to work with other distributed backends. Matrix computations play a key role in many scientific and engineering applications such as machine learning, data analysis, and computer vision. Apache Mahout is designed for large-scale data processing, leveraging Hadoop and Spark.

Sagify is a complement to AWS Sagemaker. It hides all low-level details so you can focus 100% of Machine Learning. Sagemaker is the ML engine, and Sagify the data science-friendly interface. To train, tune, and deploy hundreds ML models, you only need to implement two functions, a train AND a predict. You can manage all your ML models from one location without having to deal with low-level engineering tasks. No more sloppy ML pipelines. Sagify offers 100% reliable AWS training and deployment. Only 2 functions are required to train, tune and deploy hundreds ML models.

Scikit-learn offers simple and efficient tools to analyze predictive data. Scikit-learn, an open source machine learning toolkit for Python, is designed to provide efficient and simple tools for data modeling and analysis. Scikit-learn is a robust, open source machine learning library for the Python programming language, built on popular scientific libraries such as NumPy SciPy and Matplotlib. It offers a range of supervised learning algorithms and unsupervised learning methods, making it a valuable toolkit for researchers, data scientists and machine learning engineers. The library is organized in a consistent, flexible framework where different components can be combined to meet specific needs. This modularity allows users to easily build complex pipelines, automate tedious tasks, and integrate Scikit-learn in larger machine-learning workflows. The library's focus on interoperability also ensures that it integrates seamlessly with other Python libraries to facilitate smooth data processing.

Simplified quality assurance for machine learning. Snitch eliminates all noise so you can find the most relevant information to improve your models. With powerful dashboards and analysis, you can track your model's performance beyond accuracy. Identify potential problems in your data pipeline or distribution shifts and fix them before they impact your predictions. Once you've deployed, stay in production and have visibility to your models and data throughout the entire cycle. You can keep your data safe, whether it's cloud, on-prem or private cloud. Use the tools you love to integrate Snitch into your MLops process! We make it easy to get up and running quickly. Sometimes accuracy can be misleading. Before you deploy your models, make sure to assess their robustness and importance. Get actionable insights that will help you improve your models. Compare your models against historical metrics.

Machine learning has led to business and research breakthroughs in everything from network security to medical diagnosis. To make similar breakthroughs possible, we created the Tensor Processing unit (TPU). Cloud TPU is a custom-designed machine learning ASIC which powers Google products such as Translate, Photos and Search, Assistant, Assistant, and Gmail. Here are some ways you can use the TPU and machine-learning to accelerate your company's success, especially when it comes to scale. Cloud TPU is designed for cutting-edge machine learning models and AI services on Google Cloud. Its custom high-speed network provides over 100 petaflops performance in a single pod. This is enough computational power to transform any business or create the next breakthrough in research. It is similar to compiling code to train machine learning models. You need to update frequently and you want to do it as efficiently as possible. As apps are built, deployed, and improved, ML models must be trained repeatedly.

Declarative machine-learning systems offer the best combination of flexibility and simplicity, allowing for the fastest way to implement state-of-the art models. The system works by asking users to specify the "what" and then the system will figure out the "how". Start with smart defaults and iterate down to the code level on parameters. With Ludwig at Uber, and Overton from Apple, our team pioneered declarative machine-learning systems in industry. You can choose from our pre-built data connectors to support your databases, data warehouses and lakehouses as well as object storage. You can train state-of the-art deep learning models without having to manage infrastructure. Automated Machine Learning achieves the right balance between flexibility and control in a declarative manner. You can train and deploy models quickly using a declarative approach.

A neuro-bio inspired generic deep-learning algorithm was developed that is as simple as an intelligence test. It automatically detects patterns and predicts them, allowing you to easily create intelligent apps. Our software is simple to use due to its REST API which allows you to create intelligent apps in any environment. This allows you to connect e.g. You can connect e.g., JavaScript applications, spreadsheets, or your online broker. Our service is available for testing and development purposes. Our community edition is open-source licensed under SSPL. Vanillatech ML Workstation can be used on any local computer. For a customized offer, please contact us if you need any modifications or support.

Build, test and optimize Generative AI apps that unlock the value in your data. Our industry-leading ML expertise, our state-of-the art test and evaluation platform and advanced retrieval augmented-generation (RAG) pipelines will help you optimize LLM performance to meet your domain-specific needs. We provide an end-toend solution that manages the entire ML Lifecycle. We combine cutting-edge technology with operational excellence to help teams develop high-quality datasets, because better data leads better AI.

Easy management, deployment and monitoring of machine learning models for supercharging MLOps. Available for all organizations on the best AI platform. You can use navio for various machine learning operations across your entire artificial intelligence landscape. Machine learning can be integrated into your business workflow to make a tangible, measurable impact on your business. navio offers various Machine Learning Operations (MLOps), which can be used to support you from the initial model development phase to the production run of your model. Automatically create REST endspoints and keep track the clients or machines that interact with your model. To get the best results, you should focus on exploring and training your models. You can also stop wasting time and resources setting up infrastructure. Let navio manage all aspects of product ionization so you can go live quickly with your machine-learning models.

SANCARE is a start up that specializes in Machine Learning applied to hospital data. We work with some of the most respected scientists in the field. SANCARE offers Medical Information Departments an intuitive and ergonomic interface that promotes rapid adoption. All documents that make up the computerized patient record are available to the user. Each step of the coding process can be traced to external checks. Machine learning allows you to create powerful predictive models using large amounts of data. It also allows you to consider the notion of context which is not possible with rule engines or semantic analysis engines. It is possible to automate complex decision making processes and to detect weak signals that are often ignored by humans. The SANCARE software machine-learning engine is based upon a probabilistic approach. It uses a large number of examples to predict the correct codes without any indication.

Machine Learning and Predictive Analytics Platform. Advanced artificial intelligence is a pre-built platform for machine learning and big-data analytics that can enhance data-driven decision making. OpenText Magellan makes predictive analytics easy to use and provides flexible data visualizations that maximize business intelligence. Artificial intelligence software reduces the need to manually process large amounts of data. It presents valuable business insights in a manner that is easily accessible and relevant to the organization's most important objectives. Organizations can enhance business processes by using a curated combination of capabilities such as predictive modeling, data discovery tools and data mining techniques. IoT data analytics is another way to use data to improve decision-making based on real business intelligence.