Alternatives to Shogun Machine Learning Toolbox

Fully managed service that provides product recommendations tailored to the needs of your customers. BytePlus recommend draws on our machine learning expertise to provide dynamic and targeted recommendations. Our industry-leading team has a track history of delivering recommendations on some of the most popular platforms in the world. To engage users better and make personalized suggestions based upon customer behavior, you can use the data from your users. BytePlus recommend is easy to use, leveraging your existing infrastructure and automating the machine-learning workflow. BytePlus recommend leverages our research on machine learning to deliver personalized recommendations that are tailored to your audience's preferences. Our algorithm team is highly skilled and can develop customized strategies to meet changing business goals and needs. Pricing is determined based on A/B testing results. Based on your business needs, optimization goals are set.

PrecisionOCR is an easy-to-use, secure and HIPAA-compliant cloud-based optical character recognition (OCR) platform that organizations and providers can user to extract medical meaning from unstructured health care documents. Our OCR tooling leverages machine learning (ML) and natural language processing (NLP) to power semi-automatic and automated transformations of source material, such as pdfs and images, into structured data records. These records integrate seamlessly with EMR data using the HL7s FHIR standards to make the data searchable and centralized alongside other patient health information. Our health OCR technology can be accessed directly in a simple web-UI or the tooling can be used via integrations with API and CLI support on our open healthcare platform. We partner directly with PrecisionOCR customers to build and maintain custom OCR report extractors, which intelligently look for the most critical health data points in your health documents to cut through the noise that comes with pages of health information. PrecisionOCR is also the only self-service capable health OCR tool, allowing teams to easily test the technology for their task workflows.

Machine learning can provide insightful text analysis that extracts, analyses, and stores text. AutoML allows you to create high-quality custom machine learning models without writing a single line. Natural Language API allows you to apply natural language understanding (NLU). To identify and label fields in a document, such as emails and chats, use entity analysis. Next, perform sentiment analysis to understand customer opinions and find UX and product insights. Natural Language with speech to text API extracts insights form audio. Vision API provides optical character recognition (OCR), which can be used to scan scanned documents. Translation API can understand sentiments in multiple languages. You can use custom entity extraction to identify domain-specific entities in documents. Many of these entities don't appear within standard language models. This allows you to save time and money by not having to do manual analysis. You can create your own machine learning custom models that can classify, extract and detect sentiment.

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.

Immuta's Data Access Platform is built to give data teams secure yet streamlined access to data. Every organization is grappling with complex data policies as rules and regulations around that data are ever-changing and increasing in number. Immuta empowers data teams by automating the discovery and classification of new and existing data to speed time to value; orchestrating the enforcement of data policies through Policy-as-code (PaC), data masking, and Privacy Enhancing Technologies (PETs) so that any technical or business owner can manage and keep it secure; and monitoring/auditing user and policy activity/history and how data is accessed through automation to ensure provable compliance. Immuta integrates with all of the leading cloud data platforms, including Snowflake, Databricks, Starburst, Trino, Amazon Redshift, Google BigQuery, and Azure Synapse. Our platform is able to transparently secure data access without impacting performance. With Immuta, data teams are able to speed up data access by 100x, decrease the number of policies required by 75x, and achieve provable compliance goals.

Even if you have the best developers, every test makes them slower. 80% of your software testing is pointless. The problem is that you don't know which 20%. We use your data to find the right 20% so you can ship faster. We offer shrink-wrapped predictive testing selection. This machine learning-based method is used by companies like Facebook and can be used by all companies. We support multiple languages, test runners and CI systems. Bring Git. Launchable uses machine-learning to analyze your source code and test failures. It doesn't rely solely on code syntax analysis. Launchable can easily add support for any file-based programming language. This allows us to scale across projects and teams with different languages and tools. We currently support Python, Ruby and Java, JavaScript and Go, as well as C++ and C++. We regularly add new languages to our support.

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.

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.

Torch is a scientific computing platform that supports machine learning algorithms and has wide support for them. It is simple to use and efficient thanks to a fast scripting language, LuaJIT and an underlying C/CUDA implementation. Torch's goal is to allow you maximum flexibility and speed when building your scientific algorithms, while keeping it simple. Torch includes a large number of community-driven packages for machine learning, signal processing and parallel processing. It also builds on the Lua community. The core of Torch is the popular optimization and neural network libraries. These libraries are easy to use while allowing for maximum flexibility when implementing complex neural networks topologies. You can create arbitrary graphs of neuro networks and parallelize them over CPUs or GPUs in an efficient way.

Creating ML applications should be easy and frictionless. UnionML is a Python framework that is built on Flyte™ and unifies the ecosystem of ML software into a single interface. Combine the tools you love with a simple, standard API. This allows you to stop writing boilerplate code and focus on the important things: the data and models that learn from it. Fit the rich ecosystems of tools and frameworks to a common protocol for Machine Learning. Implement endpoints using industry-standard machine-learning methods for fetching data and training models. Serve predictions (and more) in order to create a complete ML stack. UnionML apps can be used by data scientists, ML engineers, and MLOps professionals to define a single source for truth about the behavior of your ML system.

Machine Learning made simple for everyone The leading Machine Learning platform will take your business to the next level. Get data-driven decisions now! No more cumbersome or expensive solutions. Machine Learning that works. BigML offers a variety of Machine Learning algorithms that are robustly engineered and can be applied across your company to solve real-world problems. You can avoid dependencies on multiple libraries that will increase complexity, maintenance costs, or technical debt in your projects. BigML allows unlimited predictive applications in all industries, including aerospace, automotive and energy, entertainment, financial, financial services, food and healthcare, IoT pharmaceutical, transportation, telecommunications and many more. Supervised Learning: Classification and regression (trees and ensembles, logistic regressions and deepnets), as well as time series forecasting.

SHARK is an open-source C++ machine-learning library that is fast, modular, and feature-rich. It offers methods for linear and unlinear optimization, kernel-based algorithms, neural networks, as well as other machine learning techniques. It is a powerful toolbox that can be used in real-world applications and research. Shark relies on Boost, CMake. It is compatible with Windows and Solaris, MacOS X and Linux. Shark is licensed under the permissive GNU Lesser General Public License. Shark offers a great compromise between flexibility and ease of use and computational efficiency. Shark provides many algorithms from different domains of machine learning and computational intelligence that can be combined and extended easily. Shark contains many powerful algorithms that, to our best knowledge, are not available in any other library.

Machine learning models can be deployed at scale with greater accuracy. With more models in production, R&D can be turned into ROI. Seldon reduces time to value so models can get to work quicker. Scale with confidence and minimize risks through transparent model performance and interpretable results. Seldon Deploy cuts down on time to production by providing production-grade inference servers that are optimized for the popular ML framework and custom language wrappers to suit your use cases. Seldon Core Enterprise offers enterprise-level support and access to trusted, global-tested MLOps software. Seldon Core Enterprise is designed for organizations that require: - Coverage for any number of ML models, plus unlimited users Additional assurances for models involved in staging and production - You can be confident that their ML model deployments will be supported and protected.

IBM Watson Machine Learning, a full-service IBM Cloud offering, makes it easy for data scientists and developers to work together to integrate predictive capabilities into their applications. The Machine Learning service provides a set REST APIs that can be called from any programming language. This allows you to create applications that make better decisions, solve difficult problems, and improve user outcomes. Machine learning models management (continuous-learning system) and deployment (online batch, streaming, or online) are available. You can choose from any of the widely supported machine-learning frameworks: TensorFlow and Keras, Caffe or PyTorch. Spark MLlib, scikit Learn, xgboost, SPSS, Spark MLlib, Keras, Caffe and Keras. To manage your artifacts, you can use the Python client and command-line interface. The Watson Machine Learning REST API allows you to extend your application with artificial intelligence.

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.

Open-Source AI layer for databases. Machine Learning capabilities can be integrated directly into your data domain to increase efficiency and productivity. MindsDB makes it easy to create, train, and then test ML models. Then publish them as virtual AI tables into databases. Integrate seamlessly with all major databases. SQL queries can be used to manipulate ML models. You can increase model training speed using GPU without affecting the performance of your database. Learn how the ML model arrived at its conclusions and what factors affect prediction confidence. Visual tools that allow you to analyze model performance. SQL and Python queries that return explanation insights in a single code. You can use What-if analysis to determine confidence based upon different inputs. Automate the process for applying machine learning using the state-of the-art Lightwood AutoML library. Machine Learning can be used to create custom solutions in your preferred programming language.

Amazon SageMaker JumpStart can help you speed up your machine learning (ML). SageMaker JumpStart gives you access to pre-trained foundation models, pre-trained algorithms, and built-in algorithms to help you with tasks like article summarization or image generation. You can also access prebuilt solutions to common problems. You can also share ML artifacts within your organization, including notebooks and ML models, to speed up ML model building. SageMaker JumpStart offers hundreds of pre-trained models from model hubs such as TensorFlow Hub and PyTorch Hub. SageMaker Python SDK allows you to access the built-in algorithms. The built-in algorithms can be used to perform common ML tasks such as data classifications (images, text, tabular), and sentiment analysis.

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.

Unlock vast amounts of disparate data. Our machine learning algorithms combine the previously dissimilar worlds of commercial real estate to provide property insight. Without a common language to standardize information sharing and collection, commercial real estate data has remained fragmented and isolated. Our machine learning algorithms can take data from any source, and restructure it using our universal language, the Reonomy ID. You can now simultaneously resolve disparate records, and also augment your database using the same technology. The Reonomy ID, powered by Artificial Intelligence can unlock the true potential of your commercial realty database. It maps all records, even lost, to the correct source with a clear identifier. This allows you to uncover new depths in the data you already have.

ioModel allows existing analytics teams to access powerful machine learning models without writing code. This greatly reduces development and maintenance costs. Analysts can validate and understand the effectiveness of models created on the platform by using well-known and proven statistical validation methods. The ioModel Research Platform can do for machine learning what the spreadsheet could do for general computing. The ioModel Research Platform was developed entirely with open source technology. It is also available (without support and warranty) under the GPL License at GitHub. We invite the community to join us in developing the Platform's roadmap and governance. We are committed to working openly, transparently, and to driving forward analytics, modeling and innovation.

Machine learning can be applied to distributed data to provide faster insights and better results without the long lead times, high concentration risk, or privacy concerns associated with centralizing data. Access to diverse, high-quality data sources is often a limitation of machine learning algorithms' effectiveness. You can gain more insight by unlocking more data and making it transparent about the impact of each dataset model. It takes time to get approvals, centralize data, and build out infrastructure. You can train models faster by using data right where it is while parallelizing and federating the training process. Devron allows you to access data in situ without the need to mask or anonymize. This greatly reduces the overhead of data extraction, transformation, loading, and storage.

Neuton.AI, an automated solution, empowering users to build accurate predictive models and make smart predictions with: Zero code solution Zero need for technical skills Zero need for data science knowledge

PolyAnalyst, a data analysis tool, is used by large companies in many industries (Insurance Manufacturing, Finance, etc.). It uses a visual composer to simplify complex data analysis modeling instead of programming/coding. This is one of its most distinctive features. It can combine structured and poly-structured data for unified analysis (multiple-choice questions and open ended responses), and it can process text data from over 16+ languages. PolyAnalyst provides many features to meet comprehensive data analysis requirements, including the ability to load data, cleanse and prepare data for analysis, deploy machine learning and supervised analytics techniques, and create reports that non-analysts may use to uncover insights.

Big Data Platform that generates intelligence for your business and efficiency with features that simplify data journey. You can create algorithms, Artificial Intelligence and Machine Learning for your business. SDP allows you to unify your entire data-driven journey, centralized information and create data-driven intelligence. All aspects of data ingestion, engineering, science, and visualization are possible in one journey. A robust, operations-ready, and agnostic technology to facilitate data governance. Easy to use Marketplace interface with pre-made algorithms and extensibility via the APIs. Only Big Data platform that can centralize and unify all your business data journeys.

Building Machine Learning Pipelines internally can be costly and take longer than expected. Gartner's statistics show that more than 80% will fail in AI Projects. Cortex helps teams set up machine learning faster than other alternatives and puts data to work for business results. Every team can create their own AI Predictions. You no longer need to wait for a team to be hired and costly infrastructure to be built. Cortex allows you to make predictions using the data you already own, all via a simple web interface. Everyone can now be a Data Scientist! Cortex automates the process for turning raw data into Machine Learning Pipelines. This eliminates the most difficult and time-consuming aspects of AI. These predictions are accurate and always up-to-date because Cortex continuously ingests new data and updates the underlying model automatically, with no human intervention.

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.

Neptune.ai, a platform for machine learning operations, is designed to streamline tracking, organizing and sharing of experiments, and model-building. It provides a comprehensive platform for data scientists and machine-learning engineers to log, visualise, and compare model training run, datasets and hyperparameters in real-time. Neptune.ai integrates seamlessly with popular machine-learning libraries, allowing teams to efficiently manage research and production workflows. Neptune.ai's features, which include collaboration, versioning and reproducibility of experiments, enhance productivity and help ensure that machine-learning projects are transparent and well documented throughout their lifecycle.

Weka is a collection containing machine learning algorithms for data mining tasks. It includes tools for data preparation, classification and regression, clustering, clustering, association rule mining, visualization, and visualization. The Weka is a flightless bird that lives only on New Zealand's islands. It has an inquisitive nature and is not able to fly. The bird's name is pronounced as follows: Weka is open-source software that is licensed under the GNU General Public License. Weka is open source software. We have created several online courses that can be used to teach machine learning and data mining. You can find the videos on Youtube. The invention and application methods of machine learning (ML) is an exciting development in computer science. These allow a computer program analyze large amounts of data and determine the most relevant information. This information can then be used to automatically predict or help people make faster decisions.

Create & Share Delightful Apps for Machine Learning. Gradio allows you to quickly and easily demo your machine-learning model. It has a friendly interface that anyone can use, anywhere. Installing Gradio is easy with pip. It only takes a few lines of code to create a Gradio Interface. You can choose between a variety interface types to interface with your function. Gradio is available as a webpage or embedded into Python notebooks. Gradio can generate a link that you can share publicly with colleagues to allow them to interact with your model remotely using their own devices. Once you have created an interface, it can be permanently hosted on Hugging Face. Hugging Face Spaces hosts the interface on their servers and provides you with a shareable link.

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.

Baidu AI Cloud Machine Learning is an end-toend machine learning platform for enterprises and AI developers. It can perform data preprocessing, model evaluation and training, as well as service deployments. The Baidu AI Cloud AI Development Platform BML is a platform for AI development and deployment. BML allows users to perform data pre-processing and model training, evaluation, service deployment and other tasks. The platform offers a high-performance training environment for clusters, a massive algorithm framework and model cases as well as easy to use prediction service tools. It allows users to concentrate on the algorithm and model, and achieve excellent model and predictions results. The interactive programming environment is fully hosted and allows for data processing and code debugging. The CPU instance allows users to customize the environment and install third-party software libraries.

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.

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.

A fully-featured machine learning platform empowers enterprises to conduct real data science at scale and speed. You can spend less time managing infrastructure and tools so that you can concentrate on building machine learning applications to propel your business forward. Anaconda Enterprise removes the hassle from ML operations and puts open-source innovation at the fingertips. It provides the foundation for serious machine learning and data science production without locking you into any specific models, templates, workflows, or models. AE allows data scientists and software developers to work together to create, test, debug and deploy models using their preferred languages. AE gives developers and data scientists access to both notebooks as well as IDEs, allowing them to work more efficiently together. They can also choose between preconfigured projects and example projects. AE projects can be easily moved from one environment to the next by being automatically packaged.

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.

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.

It is clear that data and analytics are key drivers of transformative business initiatives. Enterprises are increasingly able to access data to answer difficult questions. There is a greater demand for machine learning and data transformation tools that are easy to use, low-code, but flexible. Multiple tools can lead to inefficient data analysis, higher costs, and slower decision making. As closed-source solutions become obsolete, aging solutions with redundant features can threaten current data science projects. Knowledge Works combines decades of experience in data preparation and machine learning with one unified interface. As data sizes increase, Knowledge Works develops new open-source features and functionalities, and user profiles become more complex. It is easy to use for data scientists and business analysts.

Amazon SageMaker Studio Lab provides a free environment for machine learning (ML), which includes storage up to 15GB and security. Anyone can use it to learn and experiment with ML. You only need a valid email address to get started. You don't have to set up infrastructure, manage access or even sign-up for an AWS account. SageMaker Studio Lab enables model building via GitHub integration. It comes preconfigured and includes the most popular ML tools and frameworks to get you started right away. SageMaker Studio Lab automatically saves all your work, so you don’t have to restart between sessions. It's as simple as closing your computer and returning later. Machine learning development environment free of charge that offers computing, storage, security, and the ability to learn and experiment using ML. Integration with GitHub and preconfigured to work immediately with the most popular ML frameworks, tools, and libraries.

You can build, run, and manage AI models and optimize decisions across any cloud. IBM Watson Studio allows you to deploy AI anywhere with IBM Cloud Pak®, the IBM data and AI platform. Open, flexible, multicloud architecture allows you to unite teams, simplify the AI lifecycle management, and accelerate time-to-value. ModelOps pipelines automate the AI lifecycle. AutoAI accelerates data science development. AutoAI allows you to create and programmatically build models. One-click integration allows you to deploy and run models. Promoting AI governance through fair and explicable AI. Optimizing decisions can improve business results. Open source frameworks such as PyTorch and TensorFlow can be used, as well as scikit-learn. You can combine the development tools, including popular IDEs and Jupyter notebooks. JupterLab and CLIs. This includes languages like Python, R, and Scala. IBM Watson Studio automates the management of the AI lifecycle to help you build and scale AI with trust.

Accelerate compute jobs such as machine learning and HPC. There are many GPUs available to suit different price points and performance levels. Flexible pricing and machine customizations are available to optimize your workload. High-performance GPUs available on Google Cloud for machine intelligence, scientific computing, 3D visualization, and machine learning. NVIDIA K80 and P100 GPUs, T4, V100 and A100 GPUs offer a variety of compute options to meet your workload's cost and performance requirements. You can optimize the processor, memory and high-performance disk for your specific workload by using up to 8 GPUs per instance. All this with per-second billing so that you only pay for what you use. You can run GPU workloads on Google Cloud Platform, which offers industry-leading storage, networking and data analytics technologies. Compute Engine offers GPUs that can be added to virtual machine instances. Learn more about GPUs and the types of hardware available.

ONNX defines a set of common operators - the building block of machine learning and deeper learning models – and a standard file format that allows AI developers to use their models with a wide range of frameworks, runtimes and compilers. You can use your preferred framework to develop without worrying about downstream implications. ONNX allows you to use the framework of your choice with your inference engine. ONNX simplifies the access to hardware optimizations. Use runtimes and libraries compatible with ONNX to optimize performance across hardware. Our community thrives in our open governance structure that provides transparency and inclusion. We encourage you to participate and contribute.

Amazon EC2 Trn2 instances powered by AWS Trainium2 are designed for high-performance deep-learning training of generative AI model, including large language models, diffusion models, and diffusion models. They can save up to 50% on the cost of training compared to comparable Amazon EC2 Instances. Trn2 instances can support up to 16 Trainium2 accelerations, delivering up to 3 petaflops FP16/BF16 computing power and 512GB of high bandwidth memory. Trn2 instances support up to 1600 Gbps second-generation Elastic Fabric Adapter network bandwidth. NeuronLink is a high-speed nonblocking interconnect that facilitates efficient data and models parallelism. They are deployed as EC2 UltraClusters and can scale up to 30,000 Trainium2 processors interconnected by a nonblocking, petabit-scale, network, delivering six exaflops in compute performance. The AWS neuron SDK integrates with popular machine-learning frameworks such as PyTorch or TensorFlow.

RapidMiner is redefining enterprise AI so anyone can positively shape the future. RapidMiner empowers data-loving people from all levels to quickly create and implement AI solutions that drive immediate business impact. Our platform unites data prep, machine-learning, and model operations. This provides a user experience that is both rich in data science and simplified for all others. Customers are guaranteed success with our Center of Excellence methodology, RapidMiner Academy and no matter what level of experience or resources they have.

Data is essential when it comes to constructing, streamlining and growing your company. Unfortunately, it can be difficult to get the most out of data. Many organizations face incompatibilities, slow results, poor access to data and spiraling costs. Leaders who can transform raw data into real results are the ones who will succeed in today's competitive landscape. This is possible by empowering everyone in your company to be able analyze, build, and collaborate on machine learning and AI solutions. Streamflux is a one stop shop for all your data analytics and AI needs. Our self-service platform gives you the freedom to create end-to-end data solutions. It uses models to answer complex questions, and evaluates user behavior. You can transform raw data into real business impact in days instead of months, whether you are generating recommendations or predicting customer turnover and future revenue.

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.

Amazon SageMaker Studio (IDE) is an integrated development environment that allows you to access purpose-built tools to execute all steps of machine learning (ML). This includes preparing data, building, training and deploying your models. It can improve data science team productivity up to 10x. Quickly upload data, create notebooks, tune models, adjust experiments, collaborate within your organization, and then deploy models to production without leaving SageMaker Studio. All ML development tasks can be performed in one web-based interface, including preparing raw data and monitoring ML models. You can quickly move between the various stages of the ML development lifecycle to fine-tune models. SageMaker Studio allows you to replay training experiments, tune model features, and other inputs, and then compare the results.

Amazon SageMaker Feature Store can be used to store, share and manage features for machine-learning (ML) models. Features are inputs to machine learning models that are used for training and inference. In an example, features might include song ratings, listening time, and listener demographics. Multiple teams may use the same features repeatedly, so it is important to ensure that the feature quality is high-quality. It can be difficult to keep the feature stores synchronized when features are used to train models offline in batches. SageMaker Feature Store is a secure and unified place for feature use throughout the ML lifecycle. To encourage feature reuse across ML applications, you can store, share, and manage ML-model features for training and inference. Any data source, streaming or batch, can be used to import features, such as application logs and service logs, clickstreams and sensors, etc.

Amazon EC2 UltraClusters allow you to scale up to thousands of GPUs and machine learning accelerators such as AWS trainium, providing access to supercomputing performance on demand. They enable supercomputing to be accessible for ML, generative AI and high-performance computing through a simple, pay-as you-go model, without any setup or maintenance fees. UltraClusters are made up of thousands of accelerated EC2 instance co-located within a specific AWS Availability Zone and interconnected with Elastic Fabric Adapter networking to create a petabit scale non-blocking network. This architecture provides high-performance networking, and access to Amazon FSx, a fully-managed shared storage built on a parallel high-performance file system. It allows rapid processing of large datasets at sub-millisecond latency. EC2 UltraClusters offer scale-out capabilities to reduce training times for distributed ML workloads and tightly coupled HPC workloads.

Amazon SageMaker Canvas provides business analysts with a visual interface to help them generate accurate ML predictions. They don't need any ML experience nor to write a single line code. A visual interface that allows users to connect, prepare, analyze and explore data in order to build ML models and generate accurate predictions. Automate the creation of ML models in just a few clicks. By sharing, reviewing, updating, and revising ML models across tools, you can increase collaboration between data scientists and business analysts. Import ML models anywhere and instantly generate predictions in Amazon SageMaker Canvas. Amazon SageMaker Canvas allows you to import data from different sources, select the values you wish to predict, prepare and explore data, then quickly and easily build ML models. The model can then be analyzed and used to make accurate predictions.

Keepsake, an open-source Python tool, is designed to provide versioning for machine learning models and experiments. It allows users to track code, hyperparameters and training data. It also tracks metrics and Python dependencies. Keepsake integrates seamlessly into existing workflows. It requires minimal code additions and allows users to continue training while Keepsake stores code and weights in Amazon S3 or Google Cloud Storage. This allows for the retrieval and deployment of code or weights at any checkpoint. Keepsake is compatible with a variety of machine learning frameworks including TensorFlow and PyTorch. It also supports scikit-learn and XGBoost. It also has features like experiment comparison that allow users to compare parameters, metrics and dependencies between experiments.