Vertex AI
Fully 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.
Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.
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Bitrise
Streamline your development process while saving time, reducing costs, and alleviating developer stress with a mobile CI/CD solution that is not only swift and adaptable but also scalable. Whether your preference leans towards native development or cross-platform frameworks, we have a comprehensive solution that meets your needs. Supporting languages such as Swift, Objective-C, Java, and Kotlin, along with platforms like Xamarin, Cordova, Ionic, React Native, and Flutter, we ensure that your initial workflows are configured automatically so you can start building within minutes. Bitrise seamlessly integrates with any Git service, whether public, private, or ad hoc, including platforms like GitHub, GitHub Enterprise, GitLab, GitLab Enterprise, and Bitbucket, available both in the cloud and on-premises. You can easily trigger builds based on pull requests, schedule them for specific times, or set up custom webhooks to suit your workflow. Additionally, our workflows are designed to operate on your terms, enabling you to coordinate various tasks such as performing integration tests, deploying to device farms, and distributing apps to testers or app stores, ultimately enhancing your overall efficiency. With a flexible approach, you can adapt your CI/CD processes to meet the evolving demands of your development cycle.
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Apache Spark
Apache Spark™ serves as a comprehensive analytics platform designed for large-scale data processing. It delivers exceptional performance for both batch and streaming data by employing an advanced Directed Acyclic Graph (DAG) scheduler, a sophisticated query optimizer, and a robust execution engine. With over 80 high-level operators available, Spark simplifies the development of parallel applications. Additionally, it supports interactive use through various shells including Scala, Python, R, and SQL. Spark supports a rich ecosystem of libraries such as SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming, allowing for seamless integration within a single application. It is compatible with various environments, including Hadoop, Apache Mesos, Kubernetes, and standalone setups, as well as cloud deployments. Furthermore, Spark can connect to a multitude of data sources, enabling access to data stored in systems like HDFS, Alluxio, Apache Cassandra, Apache HBase, and Apache Hive, among many others. This versatility makes Spark an invaluable tool for organizations looking to harness the power of large-scale data analytics.
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MLlib
MLlib, the machine learning library of Apache Spark, is designed to be highly scalable and integrates effortlessly with Spark's various APIs, accommodating programming languages such as Java, Scala, Python, and R. It provides an extensive range of algorithms and utilities, which encompass classification, regression, clustering, collaborative filtering, and the capabilities to build machine learning pipelines. By harnessing Spark's iterative computation features, MLlib achieves performance improvements that can be as much as 100 times faster than conventional MapReduce methods. Furthermore, it is built to function in a variety of environments, whether on Hadoop, Apache Mesos, Kubernetes, standalone clusters, or within cloud infrastructures, while also being able to access multiple data sources, including HDFS, HBase, and local files. This versatility not only enhances its usability but also establishes MLlib as a powerful tool for executing scalable and efficient machine learning operations in the Apache Spark framework. The combination of speed, flexibility, and a rich set of features renders MLlib an essential resource for data scientists and engineers alike.
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