Best Data Management Software for ZenML

Google Cloud is an online service that lets you create everything from simple websites to complex apps for businesses of any size. Customers who are new to the system will receive $300 in credits for testing, deploying, and running workloads. Customers can use up to 25+ products free of charge. Use Google's core data analytics and machine learning. All enterprises can use it. It is secure and fully featured. Use big data to build better products and find answers faster. You can grow from prototypes to production and even to planet-scale without worrying about reliability, capacity or performance. Virtual machines with proven performance/price advantages, to a fully-managed app development platform. High performance, scalable, resilient object storage and databases. Google's private fibre network offers the latest software-defined networking solutions. Fully managed data warehousing and data exploration, Hadoop/Spark and messaging.

MongoDB is a distributed database that supports document-based applications and is designed for modern application developers. No other database is more productive. Our flexible document data model allows you to ship and iterate faster and provides a unified query interface that can be used for any purpose. No matter if it's your first customer, or 20 million users worldwide, you can meet your performance SLAs in every environment. You can easily ensure high availability, data integrity, and meet compliance standards for mission-critical workloads. A comprehensive suite of cloud database services that allows you to address a wide range of use cases, including transactional, analytical, search, and data visualizations. Secure mobile apps can be launched with native, edge to-cloud sync and automatic conflicts resolution. MongoDB can be run anywhere, from your laptop to the data center.

Manage and optimize models throughout the entire ML lifecycle. This includes experiment tracking, monitoring production models, and more. The platform was designed to meet the demands of large enterprise teams that deploy ML at scale. It supports any deployment strategy, whether it is private cloud, hybrid, or on-premise servers. Add two lines of code into your notebook or script to start tracking your experiments. It works with any machine-learning library and for any task. To understand differences in model performance, you can easily compare code, hyperparameters and metrics. Monitor your models from training to production. You can get alerts when something is wrong and debug your model to fix it. You can increase productivity, collaboration, visibility, and visibility among data scientists, data science groups, and even business stakeholders.

The open-source ML observability Platform. From validation to production, evaluate, test, and track ML models. From tabular data up to NLP and LLM. Built for data scientists and ML Engineers. All you need to run ML systems reliably in production. Start with simple ad-hoc checks. Scale up to the full monitoring platform. All in one tool with consistent APIs and metrics. Useful, beautiful and shareable. Explore and debug a comprehensive view on data and ML models. Start in a matter of seconds. Test before shipping, validate in production, and run checks with every model update. By generating test conditions based on a reference dataset, you can skip the manual setup. Monitor all aspects of your data, models and test results. Proactively identify and resolve production model problems, ensure optimal performance and continually improve 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.

Amazon SageMaker lets you identify raw data, such as images, text files and videos. You can also add descriptive labels to generate synthetic data and create high-quality training data sets to support your machine learning (ML). SageMaker has two options: Amazon SageMaker Ground Truth Plus or Amazon SageMaker Ground Truth. These options allow you to either use an expert workforce or create and manage your data labeling workflows. data labeling. SageMaker GroundTruth allows you to manage and create your data labeling workflows. SageMaker Ground Truth, a data labeling tool, makes data labeling simple. It also allows you to use human annotators via Amazon Mechanical Turk or third-party providers.

PostgreSQL, a powerful open-source object-relational database system, has over 30 years of experience in active development. It has earned a strong reputation for reliability and feature robustness.

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.

Weights & Biases allows for experiment tracking, hyperparameter optimization and model and dataset versioning. With just 5 lines of code, you can track, compare, and visualise ML experiments. Add a few lines of code to your script and you'll be able to see live updates to your dashboard each time you train a different version of your model. Our hyperparameter search tool is scalable to a massive scale, allowing you to optimize models. Sweeps plug into your existing infrastructure and are lightweight. Save all the details of your machine learning pipeline, including data preparation, data versions, training and evaluation. It's easier than ever to share project updates. Add experiment logging to your script in a matter of minutes. Our lightweight integration is compatible with any Python script. W&B Weave helps developers build and iterate their AI applications with confidence.

Azure Databricks allows you to unlock insights from all your data, build artificial intelligence (AI), solutions, and autoscale your Apache Spark™. You can also collaborate on shared projects with other people in an interactive workspace. Azure Databricks supports Python and Scala, R and Java, as well data science frameworks such as TensorFlow, PyTorch and scikit-learn. Azure Databricks offers the latest version of Apache Spark and allows seamless integration with open-source libraries. You can quickly spin up clusters and build in an Apache Spark environment that is fully managed and available worldwide. Clusters can be set up, configured, fine-tuned, and monitored to ensure performance and reliability. To reduce total cost of ownership (TCO), take advantage of autoscaling or auto-termination.

Great Expectations is a standard for data quality that is shared and openly accessible. It assists data teams in eliminating pipeline debt through data testing, documentation and profiling. We recommend that you deploy within a virtual environment. You may want to read the Supporting section if you are not familiar with pip and virtual environments, notebooks or git. Many companies have high expectations and are doing amazing things these days. Take a look at some case studies of companies we have worked with to see how they use great expectations in their data stack. Great expectations cloud is a fully managed SaaS service. We are looking for private alpha members to join our great expectations cloud, a fully managed SaaS service. Alpha members have first access to new features, and can contribute to the roadmap.

This is the easiest way to perform batch and streaming data processing. For mission-critical production workloads, write once and run anywhere data processing. Beam can read your data from any supported source, whether it's on-prem and in the cloud. Beam executes your business logic in both batch and streaming scenarios. Beam converts the results of your data processing logic into the most popular data sinks. A single programming model that can be used for both streaming and batch use cases. This is a simplified version of the code for all members of your data and applications teams. Apache Beam is extensible. TensorFlow Extended, Apache Hop and other projects built on Apache Beam are examples of Apache Beam's extensibility. Execute pipelines in multiple execution environments (runners), allowing flexibility and avoiding lock-in. Open, community-based development and support are available to help you develop your application and meet your specific needs.

Polars, which is aware of the data-wrangling habits of its users, exposes a complete Python interface, including all of the features necessary to manipulate DataFrames. This includes an expression language, which will allow you to write readable, performant code. Polars was written in Rust to provide the Rust ecosystem with a feature-complete DataFrame interface. Use it as either a DataFrame Library or as a query backend for your Data Models.

Airflow is a community-created platform that allows programmatically to schedule, author, and monitor workflows. Airflow is modular in architecture and uses a message queue for managing a large number of workers. Airflow can scale to infinity. Airflow pipelines can be defined in Python to allow for dynamic pipeline generation. This allows you to write code that dynamically creates pipelines. You can easily define your own operators, and extend libraries to suit your environment. Airflow pipelines can be both explicit and lean. The Jinja templating engine is used to create parametrization in the core of Airflow pipelines. No more XML or command-line black-magic! You can use standard Python features to create your workflows. This includes date time formats for scheduling, loops to dynamically generate task tasks, and loops for scheduling. This allows you to be flexible when creating your workflows.