Best Operations Management Software for Amazon SageMaker

Find and compare the best Operations Management software for Amazon SageMaker in 2024

Use the comparison tool below to compare the top Operations Management software for Amazon SageMaker on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    New Relic Reviews
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    Around 25 million engineers work across dozens of distinct functions. Engineers are using New Relic as every company is becoming a software company to gather real-time insight and trending data on the performance of their software. This allows them to be more resilient and provide exceptional customer experiences. New Relic is the only platform that offers an all-in one solution. New Relic offers customers a secure cloud for all metrics and events, powerful full-stack analytics tools, and simple, transparent pricing based on usage. New Relic also has curated the largest open source ecosystem in the industry, making it simple for engineers to get started using observability.
  • 2
    AWS Step Functions Reviews
    AWS Step Functions, a serverless function orchestrator, makes it easy to sequence AWS Lambda and multiple AWS services into business critical applications. It allows you to create and manage a series event-driven and checkpointed workflows that maintain the application's state. The output of each step acts as an input for the next. Your business logic dictates that each step of your application runs in the right order. It can be difficult to manage a series serverless applications, manage retries, or debugging errors. The complexity of managing distributed applications increases as they become more complex. Step Functions, which has built-in operational controls manages state, sequencing, error handling and retry logic. This removes a significant operational burden from your staff. AWS Step Functions allows you to create visual workflows that allow for fast translation of business requirements into technical specifications.
  • 3
    Privacera Reviews
    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.
  • 4
    Okera Reviews
    Complexity is the enemy of security. Simplify and scale fine-grained data access control. Dynamically authorize and audit every query to comply with data security and privacy regulations. Okera integrates seamlessly into your infrastructure – in the cloud, on premise, and with cloud-native and legacy tools. With Okera, data users can use data responsibly, while protecting them from inappropriately accessing data that is confidential, personally identifiable, or regulated. Okera’s robust audit capabilities and data usage intelligence deliver the real-time and historical information that data security, compliance, and data delivery teams need to respond quickly to incidents, optimize processes, and analyze the performance of enterprise data initiatives.
  • 5
    Robust Intelligence Reviews
    Robust Intelligence Platform seamlessly integrates into your ML lifecycle to eliminate any model failures. The platform detects weaknesses in your model, detects statistical data issues such as drift, and prevents data from being inserted into your AI system. A single test is the heart of our test-based approach. Each test measures the model's resistance to a particular type of production model failure. Stress Testing runs hundreds upon hundreds of these tests in order to assess model production readiness. These tests are used to automatically configure an AI Firewall to protect the model from the specific types of failures to which it is most vulnerable. Continuous Testing also runs these tests during production. Continuous Testing provides an automated root cause analysis that identifies the root cause of any test failure. ML Integrity can be ensured by using all three elements of Robust Intelligence.
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