pandas Integrations

Monitor your servers, containers, and applications, in high-resolution and in real-time. Netdata collects metrics per second and presents them in beautiful low-latency dashboards. It is designed to run on all of your physical and virtual servers, cloud deployments, Kubernetes clusters, and edge/IoT devices, to monitor your systems, containers, and applications. It scales nicely from just a single server to thousands of servers, even in complex multi/mixed/hybrid cloud environments, and given enough disk space it can keep your metrics for years. KEY FEATURES: Collects metrics from 800+ integrations Real-Time, Low-Latency, High-Resolution Unsupervised Anomaly Detection Powerful Visualization Out of box Alerts systemd Journal Logs Explorer Low Maintenance Open and Extensible Troubleshoot slowdowns and anomalies in your infrastructure with thousands of per-second metrics, meaningful visualisations, and insightful health alarms with zero configuration. Netdata is different. Real-Time data collection and visualization. Infinite scalability baked into its design. Flexible and extremely modular. Immediately available for troubleshooting, requiring zero prior knowledge and preparation.

ProActive Parallel Suite, a member of the OW2 Open Source Community for acceleration and orchestration, seamlessly integrated with the management and operation of high-performance Clouds (Private, Public with bursting capabilities). ProActive Parallel Suite platforms offer high-performance workflows and application parallelization, enterprise Scheduling & Orchestration, and dynamic management of private Heterogeneous Grids & Clouds. Our users can now simultaneously manage their Enterprise Cloud and accelerate and orchestrate all of their enterprise applications with the ProActive platform.

Dagster is the cloud-native open-source orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. It is the platform of choice data teams responsible for the development, production, and observation of data assets. With Dagster, you can focus on running tasks, or you can identify the key assets you need to create using a declarative approach. Embrace CI/CD best practices from the get-go: build reusable components, spot data quality issues, and flag bugs early.

The workflow automation platform that automates complex, mission-critical data processing and ML processes at large scale. Flyte makes it simple to create machine learning and data processing workflows that are concurrent, scalable, and manageable. Flyte is used for production at Lyft and Spotify, as well as Freenome. Flyte is used at Lyft for production model training and data processing. It has become the de facto platform for pricing, locations, ETA and mapping, as well as autonomous teams. Flyte manages more than 10,000 workflows at Lyft. This includes over 1,000,000 executions per month, 20,000,000 tasks, and 40,000,000 containers. Flyte has been battle-tested by Lyft and Spotify, as well as Freenome. It is completely open-source and has an Apache 2.0 license under Linux Foundation. There is also a cross-industry oversight committee. YAML is a useful tool for configuring machine learning and data workflows. However, it can be complicated and potentially error-prone.

Giskard provides interfaces to AI & Business teams for evaluating and testing ML models using automated tests and collaborative feedback. Giskard accelerates teamwork to validate ML model validation and gives you peace-of-mind to eliminate biases, drift, or regression before deploying ML models into production.

ThinkData Works provides a robust catalog platform for discovering, managing, and sharing data from both internal and external sources. Enrichment solutions combine partner data with your existing datasets to produce uniquely valuable assets that can be shared across your entire organization. The ThinkData Works platform and enrichment solutions make data teams more efficient, improve project outcomes, replace multiple existing tech solutions, and provide you with a competitive advantage.

Coiled makes enterprise-grade Dask easy. Coiled manages Dask clusters within your AWS or GCP account. This makes it the easiest and most secure method to run Dask in production. Coiled manages your cloud infrastructure and can deploy to your AWS account or Google Cloud account in a matter of minutes. Coiled provides a solid deployment solution that requires little effort. You can customize the cluster node types to meet your analysis needs. Run Dask in Jupyter Notebooks to get real-time dashboards, cluster insights, and other useful information. You can easily create software environments with custom dependencies for your Dask analysis. Enjoy enterprise-grade security. SLAs, user level management, and auto-termination clusters reduce costs. Coiled makes it easy for you to deploy your cluster on AWS and GCP. It takes only minutes and requires no credit card. You can launch code from anywhere you like, including cloud services like AWS SageMaker and open source solutions like JupyterHub.

Dash gives your Mac instant offline accessibility to over 200+ API documentation sets. Dash is an API documentation browser, code snippet manager, and code snippet manager. Dash instantly searches offline documentation for 200+ APIs, 100+ cheatsheets, and many more. You can also request docsets to include or create your own. Dash includes 200+ offline documentation sets. Dash allows you to choose which documentation sets you want to download. Dash will keep them up-to-date. You can also request docsets, create your own docsets, or download docsets directly from third-party sources. All documentation sets were created and maintained with the greatest care. Dash integrates seamlessly with package managers to create documentation sets for any item you may need. It can also provide custom documentation sources. You can store snippets. You can reuse snippets easily. Expand snippets within any app. Organise snippets using tags, syntax highlighting, or variable placeholders.

Kedro provides the foundation for clean, data-driven code. It applies concepts from software engineering to machine-learning projects. Kedro projects provide scaffolding for complex machine-learning and data pipelines. Spend less time on "plumbing", and instead focus on solving new problems. Kedro standardizes the way data science code is written and ensures that teams can collaborate easily to solve problems. You can make a seamless transition between development and production by using exploratory code. This code can be converted into reproducible, maintainable and modular experiments. A series of lightweight connectors are used to save and upload data across a variety of file formats and file systems.

Boost your career and visibility with a standout presentation and analytics. Skip the tedious tasks associated with manual design and coding. Skills.ai allows you to quickly create detailed analytics using AI, ensuring that your team or yourself will be successful. Skills.ai's cutting-edge artificial-intelligence capabilities streamline the process of data analytics, allowing users to focus on data-driven decision-making and gaining insights without having to worry about complex coding. Skills' data chat makes data analysis as intuitive as talking to your favorite data analyst. With data chat, you can ask your data-related questions directly and on your terms.

Yandex Data Proc creates and configures Spark clusters, Hadoop clusters, and other components based on the size, node capacity and services you select. Zeppelin Notebooks and other web applications can be used to collaborate via a UI Proxy. You have full control over your cluster, with root permissions on each VM. Install your own libraries and applications on clusters running without having to restart. Yandex Data Proc automatically increases or decreases computing resources for compute subclusters according to CPU usage indicators. Data Proc enables you to create managed clusters of Hive, which can reduce failures and losses due to metadata not being available. Save time when building ETL pipelines, pipelines for developing and training models, and describing other iterative processes. Apache Airflow already includes the Data Proc operator.

Spyder's multilingual editor integrates many powerful tools straight out of the box to provide an efficient and easy-to-use editing experience. The editor's main features include syntax highlighting and style analysis (pyflakes, pycodestyle), on-demand completion, calltips, go-to-definition features like rope and jedi, a function/class browser and horizontal and vertical splitting. The IPython console lets you execute commands and interact directly with IPython interpreters. You can interactively browse and manage objects generated by your code using the variable explorer. It displays the namespace contents of the current IPython console session. You can add, remove and modify their values using a variety GUI-based editors.

RunCode offers online workspaces that allow you to work in a web browser on code projects. These workspaces offer a complete development environment that includes a code editor, a terminal and access to a variety of tools and libraries. These workspaces are easy to use and can be set up on your own computer.

We are a turnkey AI-automated IT workforce solution that is infinitely scalable, and payment-enabled. We are democratizing the way U.S. businesses go nearshore, without the high vendor cost and security risks. Our system can predict talent costs and aligned talent in the LATAM region to help bring new business goals to market. AccessInstantly gain access to a senior recruitment team that is dedicated and influential and understands your business technology and the talent market. Your dedicated engineering managers will validate and score the technical abilities of video-recorded tests to ensure best alignment. Automate the personalized onboarding for all roles in multiple LATAM countries. We prepare and procure dedicated devices, and ensure that all staff members have access to all tools and documentation to get started right away. Address top performers and those who want to expand their abilities.

Avanzai allows you to use natural language to produce Python code that is ready for production. This will help you speed up your financial data analysis. Avanzai makes financial data analysis easier for both beginners as well as experts. It uses plain English to provide simple English support. Natural prompts allow you to plot times series data, equity index members, or stock performance data. Use AI to generate code using the relevant Python packages. You can edit the code as needed. Once you are satisfied with the code, copy it into your local environment. Then you can get to work. Use Python packages such as Pandas, Numpy and others to perform quant analysis. You can quickly extract fundamental data and calculate the performance for nearly all US stocks. Accurate and current information will improve your investment decisions. Avanzai allows you to write the same Python code as quants to analyze complex financial data.

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.

Cleanlab Studio is a single framework that handles all analytics and machine-learning tasks. It includes the entire data quality pipeline and data-centric AI. The automated pipeline takes care of all your ML tasks: data preprocessing and foundation model tuning, hyperparameters tuning, model selection. ML models can be used to diagnose data problems, and then re-trained using your corrected dataset. Explore the heatmap of all suggested corrections in your dataset. Cleanlab Studio offers all of this and more free of charge as soon as your dataset is uploaded. Cleanlab Studio is pre-loaded with a number of demo datasets and project examples. You can view them in your account once you sign in.

You can make changes to your models quickly and easily by turning on the black box, pip installing 3LC. Iterate quickly and remove the guesswork in your model training. Visualize per-sample metrics in your browser. Analyze and fix issues in your dataset by analyzing your training. Interactive data debugging, guided by models. Find out which samples are important or inefficient. Understanding what samples work well and where your model struggles. Improve your model in different ways by weighting your data. Make sparse and non-destructive changes to individual samples or a batch. Keep track of all changes, and restore previous revisions. Data tracking and metrics per-sample, per-epoch will allow you to go deeper than standard experiment trackers. To uncover hidden trends, aggregate metrics by sample features rather than epoch. Each training run should be tied to a specific revision of the dataset for reproducibility.