Find and compare the best Artificial Intelligence software for APERIO DataWise in 2024
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Use the comparison tool below to compare the top Artificial Intelligence software for APERIO DataWise on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.
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Integrate analytics into any workflow or application to make crucial decisions - confidently. Analytics can be integrated into your everyday workflows and applications to help you make better and faster decisions for your business and customers. To make analytics easy and intuitive, integrate customized analytics into your products and applications. The AI-driven predictive analytics platform is designed to increase product adoption, retention, and engagement. Sisense, a top-rated Business Intelligence (BI), reporting software, allows you to prepare, analyze, and examine data from multiple sources. Sisense is trusted by industry-leading companies like NASDAQ, Phillips and Airbus. It offers an end to end, agile BI platform that enables businesses to make better, faster data-driven business decisions. Sisense has an open, single-stack architecture that enables machine learning, best-in class analytics engines, and delivers insights beyond the dashboard.
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Dagster+
Dagster Labs
$0Dagster is the cloud-native open-source orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. It is the platform of choice data teams responsible for the development, production, and observation of data assets. With Dagster, you can focus on running tasks, or you can identify the key assets you need to create using a declarative approach. Embrace CI/CD best practices from the get-go: build reusable components, spot data quality issues, and flag bugs early. -
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Amazon SageMaker
Amazon
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. -
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Amazon QuickSight
Amazon
Amazon QuickSight allows everyone within your organization to access your data. This includes asking questions in natural language, exploring interactive dashboards, and automatically looking for patterns or outliers using machine learning. QuickSight powers millions upon millions of dashboard views per week for customers like the NFL, Expedia and Volvo. This allows their end-users make better data-driven decision making. To receive relevant visualizations, ask questions about your data using Q's ML-powered engine. This allows you to ask conversational questions without the need for data preparation by admins and authors. AWS' machine learning expertise allows you to uncover hidden insights in your data, forecast accurately and do what-if analysis. You can also add natural language narratives or easy-to-understand natural languages to your dashboards using AWS' machine learning expertise. You can embed interactive visualizations and dashboards in your applications, as well as sophisticated dashboard authoring and natural language query capabilities. -
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Databricks Data Intelligence Platform
Databricks
The Databricks Data Intelligence Platform enables your entire organization to utilize data and AI. It is built on a lakehouse that provides an open, unified platform for all data and governance. It's powered by a Data Intelligence Engine, which understands the uniqueness in your data. Data and AI companies will win in every industry. Databricks can help you achieve your data and AI goals faster and easier. Databricks combines the benefits of a lakehouse with generative AI to power a Data Intelligence Engine which understands the unique semantics in your data. The Databricks Platform can then optimize performance and manage infrastructure according to the unique needs of your business. The Data Intelligence Engine speaks your organization's native language, making it easy to search for and discover new data. It is just like asking a colleague a question. -
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Azure Machine Learning
Microsoft
Accelerate the entire machine learning lifecycle. Developers and data scientists can have more productive experiences building, training, and deploying machine-learning models faster by empowering them. Accelerate time-to-market and foster collaboration with industry-leading MLOps -DevOps machine learning. Innovate on a trusted platform that is secure and trustworthy, which is designed for responsible ML. Productivity for all levels, code-first and drag and drop designer, and automated machine-learning. Robust MLOps capabilities integrate with existing DevOps processes to help manage the entire ML lifecycle. Responsible ML capabilities – understand models with interpretability, fairness, and protect data with differential privacy, confidential computing, as well as control the ML cycle with datasheets and audit trials. Open-source languages and frameworks supported by the best in class, including MLflow and Kubeflow, ONNX and PyTorch. TensorFlow and Python are also supported. -
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Kubeflow
Kubeflow
Kubeflow is a project that makes machine learning (ML), workflows on Kubernetes portable, scalable, and easy to deploy. Our goal is not create new services, but to make it easy to deploy the best-of-breed open source systems for ML to different infrastructures. Kubeflow can be run anywhere Kubernetes is running. Kubeflow offers a custom TensorFlow job operator that can be used to train your ML model. Kubeflow's job manager can handle distributed TensorFlow training jobs. You can configure the training controller to use GPUs or CPUs, and to adapt to different cluster sizes. Kubeflow provides services to create and manage interactive Jupyter Notebooks. You can adjust your notebook deployment and compute resources to meet your data science requirements. You can experiment with your workflows locally and then move them to the cloud when you are ready.
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