Business Software for PySpark

CometLLM allows you to visualize and log your LLM chains and prompts. CometLLM can be used to identify effective prompting strategies, streamline troubleshooting and ensure reproducible workflows. Log your prompts, responses, variables, timestamps, duration, and metadata. Visualize your responses and prompts in the UI. Log your chain execution to the level you require. Visualize your chain in the UI. OpenAI chat models automatically track your prompts. Track and analyze feedback from users. Compare your prompts in the UI. Comet LLM Projects are designed to help you perform smart analysis of logged prompt engineering workflows. Each column header corresponds with a metadata attribute that was logged in the LLM Project, so the exact list can vary between projects.

Machine learning applications can be deployed to production in minutes instead of months. Automate the transformation of raw data and generate training data sets. Also, you can serve features for online inference at large scale. Replace bespoke data pipelines by robust pipelines that can be created, orchestrated, and maintained automatically. You can increase your team's efficiency and standardize your machine learning data workflows by sharing features throughout the organization. You can serve features in production at large scale with confidence that the systems will always be available. Tecton adheres to strict security and compliance standards. Tecton is neither a database nor a processing engine. It can be integrated into your existing storage and processing infrastructure and orchestrates it.

Your offline data can be used to make real-time predictions, without the need for custom pipelines. Data consistency is achieved between offline training and online prediction, eliminating train-serve bias. Standardize data engineering workflows within a consistent framework. Feast is used by teams to build their internal ML platforms. Feast doesn't require dedicated infrastructure to be deployed and managed. Feast reuses existing infrastructure and creates new resources as needed. You don't want a managed solution, and you are happy to manage your own implementation. Feast is supported by engineers who can help with its implementation and management. You are looking to build pipelines that convert raw data into features and integrate with another system. You have specific requirements and want to use an open-source solution.

Apache Spark™, a unified analytics engine that can handle large-scale data processing, is available. Apache Spark delivers high performance for streaming and batch data. It uses a state of the art DAG scheduler, query optimizer, as well as a physical execution engine. Spark has over 80 high-level operators, making it easy to create parallel apps. You can also use it interactively via the Scala, Python and R SQL shells. Spark powers a number of libraries, including SQL and DataFrames and MLlib for machine-learning, GraphX and Spark Streaming. These libraries can be combined seamlessly in one application. Spark can run on Hadoop, Apache Mesos and Kubernetes. It can also be used standalone or in the cloud. It can access a variety of data sources. Spark can be run in standalone cluster mode on EC2, Hadoop YARN and Mesos. Access data in HDFS and Alluxio.

You will find everything you need to improve your business decisions. The Fosfor Decision Cloud integrates the modern data ecosystem in order to deliver the long-sought promise that AI can bring: enhanced business outcomes. The Fosfor Decision Cloud combines the components of your data into a modern, decision stack that is designed to increase business outcomes. Fosfor collaborates seamlessly with partners to create a modern decision stack that delivers unprecedented value for your data investments.

Amazon SageMaker Data Wrangler cuts down the time it takes for data preparation and aggregation for machine learning (ML). This reduces the time taken from weeks to minutes. SageMaker Data Wrangler makes it easy to simplify the process of data preparation. It also allows you to complete every step of the data preparation workflow (including data exploration, cleansing, visualization, and scaling) using a single visual interface. SQL can be used to quickly select the data you need from a variety of data sources. The Data Quality and Insights Report can be used to automatically check data quality and detect anomalies such as duplicate rows or target leakage. SageMaker Data Wrangler has over 300 built-in data transforms that allow you to quickly transform data without having to write any code. After you've completed your data preparation workflow you can scale it up to your full datasets with SageMaker data processing jobs. You can also train, tune and deploy models using SageMaker data processing jobs.

Pandera is a flexible, simple and extensible framework for data testing that allows you to validate not only the data, but also the functions which produce it. You can overcome the initial challenge of defining a data schema by inferring it from clean data and then fine-tuning it over time. Identify critical points in your pipeline and validate the data that enters and leaves them. Validate functions that generate your data by automatically creating test cases. You can choose from a wide range of pre-built tests or create your own rules to validate your data.