Best Machine Learning Software for Google Cloud BigQuery - Page 2

Find and compare the best Machine Learning software for Google Cloud BigQuery in 2025

Use the comparison tool below to compare the top Machine Learning software for Google Cloud BigQuery on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    Zepl Reviews
    Coordinate, explore, and oversee all projects within your data science team efficiently. With Zepl's advanced search functionality, you can easily find and repurpose both models and code. The enterprise collaboration platform provided by Zepl allows you to query data from various sources like Snowflake, Athena, or Redshift while developing your models using Python. Enhance your data interaction with pivoting and dynamic forms that feature visualization tools such as heatmaps, radar, and Sankey charts. Each time you execute your notebook, Zepl generates a new container, ensuring a consistent environment for your model runs. Collaborate with teammates in a shared workspace in real time, or leave feedback on notebooks for asynchronous communication. Utilize precise access controls to manage how your work is shared, granting others read, edit, and execute permissions to facilitate teamwork and distribution. All notebooks benefit from automatic saving and version control, allowing you to easily name, oversee, and revert to previous versions through a user-friendly interface, along with smooth exporting capabilities to Github. Additionally, the platform supports integration with external tools, further streamlining your workflow and enhancing productivity.
  • 2
    Robust Intelligence Reviews
    The Robust Intelligence Platform is designed to integrate effortlessly into your machine learning lifecycle, thereby mitigating the risk of model failures. It identifies vulnerabilities within your model, blocks erroneous data from infiltrating your AI system, and uncovers statistical issues such as data drift. Central to our testing methodology is a singular test that assesses the resilience of your model against specific types of production failures. Stress Testing performs hundreds of these evaluations to gauge the readiness of the model for production deployment. The insights gained from these tests enable the automatic configuration of a tailored AI Firewall, which safeguards the model from particular failure risks that it may face. Additionally, Continuous Testing operates during production to execute these tests, offering automated root cause analysis that is driven by the underlying factors of any test failure. By utilizing all three components of the Robust Intelligence Platform in tandem, you can maintain the integrity of your machine learning processes, ensuring optimal performance and reliability. This holistic approach not only enhances model robustness but also fosters a proactive stance in managing potential issues before they escalate.
  • 3
    Layerup Reviews
    Effortlessly extract and transform data from various sources using Natural Language, whether it's your database, CRM, or billing system. Experience a remarkable boost in productivity, enhancing it by 5-10 times, and say goodbye to the frustrations of cumbersome BI tools. With the power of Natural Language, you can swiftly query intricate data within seconds, making it easy to transition from DIY solutions to advanced, AI-driven tools. In just a few lines of code, you can create sophisticated dashboards and reports without the need for SQL or complicated formulas, as Layerup AI handles all the hard work for you. Not only does Layerup provide immediate answers to questions that would typically take 5 to 40 hours a month to resolve through SQL queries, but it also functions as your personal data analyst around the clock, delivering intricate dashboards and charts that can be seamlessly embedded anywhere. With Layerup, you unlock the potential of your data in ways that were previously unimaginable.