PySpark Integrations

CometLLM is a tool to log and visualize your LLM prompts and chains. Use CometLLM to identify effective prompt strategies, streamline your troubleshooting, and ensure reproducible workflows. Log your prompts and responses, including prompt template, variables, timestamps and duration, and any metadata that you need. Visualize your prompts and responses in the UI. Log your chain execution down to the level of granularity that you need. Visualize your chain execution in the UI. Automatically tracks your prompts when using the OpenAI chat models. Track and analyze user feedback. Diff your prompts and chain execution in the UI. Comet LLM Projects have been designed to support you in performing smart analysis of your logged prompt engineering workflows. Each column header corresponds to a metadata attribute logged in the LLM project, so the exact list of the displayed default headers can vary across projects.

Deploy machine learning applications to production in minutes, rather than months. Automate the transformation of raw data, generate training data sets, and serve features for online inference at scale. Save months of work by replacing bespoke data pipelines with robust pipelines that are created, orchestrated and maintained automatically. Increase your team’s efficiency by sharing features across the organization and standardize all of your machine learning data workflows in one platform. Serve features in production at extreme scale with the confidence that systems will always be up and running. Tecton meets strict security and compliance standards. Tecton is not a database or a processing engine. It plugs into and orchestrates on top of your existing storage and processing infrastructure.

Make your offline data available for real-time predictions without having to build custom pipelines. Ensure data consistency between offline training and online inference, eliminating train-serve skew. Standardize data engineering workflows under one consistent framework. Teams use Feast as the foundation of their internal ML platforms. Feast doesn’t require the deployment and management of dedicated infrastructure. Instead, it reuses existing infrastructure and spins up new resources when needed. You are not looking for a managed solution and are willing to manage and maintain your own implementation. You have engineers that are able to support the implementation and management of Feast. You want to run pipelines that transform raw data into features in a separate system and integrate with it. You have unique requirements and want to build on top of an open source solution.

Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources.

Everything you need to make better business decisions. The Fosfor Decision Cloud unifies the modern data ecosystem to deliver the long-sought promise of AI: enhanced business outcomes. The Fosfor Decision Cloud unifies the components of your data stack into a modern decision stack, built to amplify business outcomes. Fosfor works seamlessly with its partners to create the modern decision stack, which delivers unprecedented value from your data investments.

Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes. With SageMaker Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow (including data selection, cleansing, exploration, visualization, and processing at scale) from a single visual interface. You can use SQL to select the data you want from a wide variety of data sources and import it quickly. Next, you can use the Data Quality and Insights report to automatically verify data quality and detect anomalies, such as duplicate rows and target leakage. SageMaker Data Wrangler contains over 300 built-in data transformations so you can quickly transform data without writing any code. Once you have completed your data preparation workflow, you can scale it to your full datasets using SageMaker data processing jobs; train, tune, and deploy models.

Pandera provides a simple, flexible, and extensible data-testing framework for validating not only your data but also the functions that produce them. Overcome the initial hurdle of defining a schema by inferring one from clean data, then refine it over time. Identify the critical points in your data pipeline, and validate data going in and out of them. Validate the functions that produce your data by automatically generating test cases for them. Access a comprehensive suite of built-in tests, or easily create your own validation rules for your specific use cases.