Find and compare the best Artificial Intelligence software for Google Cloud Dataproc in 2024
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Use the comparison tool below to compare the top Artificial Intelligence software for Google Cloud Dataproc on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.
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Vertex AI
Google
620 RatingsFully 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. -
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Google Cloud Platform
Google
Free ($300 in free credits) 55,132 RatingsGoogle Cloud is an online service that lets you create everything from simple websites to complex apps for businesses of any size. Customers who are new to the system will receive $300 in credits for testing, deploying, and running workloads. Customers can use up to 25+ products free of charge. Use Google's core data analytics and machine learning. All enterprises can use it. It is secure and fully featured. Use big data to build better products and find answers faster. You can grow from prototypes to production and even to planet-scale without worrying about reliability, capacity or performance. Virtual machines with proven performance/price advantages, to a fully-managed app development platform. High performance, scalable, resilient object storage and databases. Google's private fibre network offers the latest software-defined networking solutions. Fully managed data warehousing and data exploration, Hadoop/Spark and messaging. -
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Google Cloud BigQuery
Google
$0.04 per slot hour 1,686 RatingsANSI SQL allows you to analyze petabytes worth of data at lightning-fast speeds with no operational overhead. Analytics at scale with 26%-34% less three-year TCO than cloud-based data warehouse alternatives. You can unleash your insights with a trusted platform that is more secure and scales with you. Multi-cloud analytics solutions that allow you to gain insights from all types of data. You can query streaming data in real-time and get the most current information about all your business processes. Machine learning is built-in and allows you to predict business outcomes quickly without having to move data. With just a few clicks, you can securely access and share the analytical insights within your organization. Easy creation of stunning dashboards and reports using popular business intelligence tools right out of the box. BigQuery's strong security, governance, and reliability controls ensure high availability and a 99.9% uptime SLA. Encrypt your data by default and with customer-managed encryption keys -
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Immuta
Immuta
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. -
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Google Cloud Vertex AI Workbench
Google
$10 per GBOne 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. -
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Google Cloud GPUs
Google
$0.160 per GPUAccelerate 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. -
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Privacera
Privacera
Multi-cloud data security with a single pane of glass Industry's first SaaS access governance solution. Cloud is fragmented and data is scattered across different systems. Sensitive data is difficult to access and control due to limited visibility. Complex data onboarding hinders data scientist productivity. Data governance across services can be manual and fragmented. It can be time-consuming to securely move data to the cloud. Maximize visibility and assess the risk of sensitive data distributed across multiple cloud service providers. One system that enables you to manage multiple cloud services' data policies in a single place. Support RTBF, GDPR and other compliance requests across multiple cloud service providers. Securely move data to the cloud and enable Apache Ranger compliance policies. It is easier and quicker to transform sensitive data across multiple cloud databases and analytical platforms using one integrated system.
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