Dask Integrations

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.

Saturn Cloud is an AI/ML platform available on every cloud. Data teams and engineers can build, scale, and deploy their AI/ML applications with any stack.

Anaconda Enterprise equips organizations to conduct robust data science rapidly and at scale through a comprehensive machine learning platform. By reducing the time spent on managing tools and infrastructure, you can concentrate on creating machine learning applications that propel your business forward. This platform alleviates the challenges associated with ML operations, grants you access to open-source innovations, and lays the groundwork for serious data science and machine learning production without confining you to specific models, templates, or workflows. Software developers and data scientists can collaborate seamlessly with Anaconda Enterprise to build, test, debug, and deploy models utilizing their favored programming languages and tools. The platform offers both notebooks and integrated development environments (IDEs), enhancing the efficiency of teamwork between developers and data scientists. They can also explore example projects and utilize preconfigured options. Moreover, Anaconda Enterprise ensures that projects are automatically containerized, facilitating effortless transitions between different environments. This flexibility allows teams to adapt and scale their machine learning solutions according to evolving business needs.

The Domino Enterprise MLOps Platform helps data science teams improve the speed, quality, and impact of data science at scale. Domino is open and flexible, empowering professional data scientists to use their preferred tools and infrastructure. Data science models get into production fast and are kept operating at peak performance with integrated workflows. Domino also delivers the security, governance and compliance that enterprises expect. The Self-Service Infrastructure Portal makes data science teams become more productive with easy access to their preferred tools, scalable compute, and diverse data sets. By automating time-consuming and tedious DevOps tasks, data scientists can focus on the tasks at hand. The Integrated Model Factory includes a workbench, model and app deployment, and integrated monitoring to rapidly experiment, deploy the best models in production, ensure optimal performance, and collaborate across the end-to-end data science lifecycle. The System of Record has a powerful reproducibility engine, search and knowledge management, and integrated project management. Teams can easily find, reuse, reproduce, and build on any data science work to amplify innovation.

You can develop on your laptop, then scale the same Python code elastically across hundreds or GPUs on any cloud. Ray converts existing Python concepts into the distributed setting, so any serial application can be easily parallelized with little code changes. With a strong ecosystem distributed libraries, scale compute-heavy machine learning workloads such as model serving, deep learning, and hyperparameter tuning. Scale existing workloads (e.g. Pytorch on Ray is easy to scale by using integrations. Ray Tune and Ray Serve native Ray libraries make it easier to scale the most complex machine learning workloads like hyperparameter tuning, deep learning models training, reinforcement learning, and training deep learning models. In just 10 lines of code, you can get started with distributed hyperparameter tune. Creating distributed apps is hard. Ray is an expert in distributed execution.

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.

Union.ai Benefits: - Accelerated Data Processing & ML: Union.ai significantly speeds up data processing and machine learning. - Built on Trusted Open-Source: Leverages the robust open-source project Flyte™, ensuring a reliable and tested foundation for your ML projects. - Kubernetes Efficiency: Harnesses the power and efficiency of Kubernetes along with enhanced observability and enterprise features. - Optimized Infrastructure: Facilitates easier collaboration among Data and ML teams on optimized infrastructures, boosting project velocity. - Breaks Down Silos: Tackles the challenges of distributed tooling and infrastructure by simplifying work-sharing across teams and environments with reusable tasks, versioned workflows, and an extensible plugin system. - Seamless Multi-Cloud Operations: Navigate the complexities of on-prem, hybrid, or multi-cloud setups with ease, ensuring consistent data handling, secure networking, and smooth service integrations. - Cost Optimization: Keeps a tight rein on your compute costs, tracks usage, and optimizes resource allocation even across distributed providers and instances, ensuring cost-effectiveness.

Flyte is a robust platform designed for automating intricate, mission-critical data and machine learning workflows at scale. It simplifies the creation of concurrent, scalable, and maintainable workflows, making it an essential tool for data processing and machine learning applications. Companies like Lyft, Spotify, and Freenome have adopted Flyte for their production needs. At Lyft, Flyte has been a cornerstone for model training and data processes for more than four years, establishing itself as the go-to platform for various teams including pricing, locations, ETA, mapping, and autonomous vehicles. Notably, Flyte oversees more than 10,000 unique workflows at Lyft alone, culminating in over 1,000,000 executions each month, along with 20 million tasks and 40 million container instances. Its reliability has been proven in high-demand environments such as those at Lyft and Spotify, among others. As an entirely open-source initiative licensed under Apache 2.0 and backed by the Linux Foundation, it is governed by a committee representing multiple industries. Although YAML configurations can introduce complexity and potential errors in machine learning and data workflows, Flyte aims to alleviate these challenges effectively. This makes Flyte not only a powerful tool but also a user-friendly option for teams looking to streamline their data operations.

Kedro serves as a robust framework for establishing clean data science practices. By integrating principles from software engineering, it enhances the efficiency of machine-learning initiatives. Within a Kedro project, you will find a structured approach to managing intricate data workflows and machine-learning pipelines. This allows you to minimize the time spent on cumbersome implementation tasks and concentrate on addressing innovative challenges. Kedro also standardizes the creation of data science code, fostering effective collaboration among team members in problem-solving endeavors. Transitioning smoothly from development to production becomes effortless with exploratory code that can evolve into reproducible, maintainable, and modular experiments. Additionally, Kedro features a set of lightweight data connectors designed to facilitate the saving and loading of data across various file formats and storage systems, making data management more versatile and user-friendly. Ultimately, this framework empowers data scientists to work more effectively and with greater confidence in their projects.

Prefect Cloud serves as a centralized hub for managing your workflows effectively. By deploying from Prefect core, you can immediately obtain comprehensive oversight and control over your operations. The platform features an aesthetically pleasing user interface that allows you to monitor the overall health of your infrastructure effortlessly. You can receive real-time updates and logs, initiate new runs, and access vital information just when you need it. With Prefect's Hybrid Model, your data and code stay on-premises while Prefect Cloud's managed orchestration ensures seamless operation. The Cloud scheduler operates asynchronously, guaranteeing that your tasks commence punctually without fail. Additionally, it offers sophisticated scheduling capabilities that enable you to modify parameter values and define the execution environment for each execution. You can also set up personalized notifications and actions that trigger whenever there are changes in your workflows. Keep track of the status of all agents linked to your cloud account and receive tailored alerts if any agent becomes unresponsive. This level of monitoring empowers teams to proactively tackle issues before they escalate into significant problems.

Coiled simplifies the process of using Dask at an enterprise level by managing Dask clusters within your AWS or GCP accounts, offering a secure and efficient method for deploying Dask in a production environment. With Coiled, you can set up cloud infrastructure in mere minutes, allowing for a seamless deployment experience with minimal effort on your part. You have the flexibility to tailor the types of cluster nodes to meet the specific requirements of your analysis. Utilize Dask in Jupyter Notebooks while gaining access to real-time dashboards and insights about your clusters. The platform also facilitates the easy creation of software environments with personalized dependencies tailored to your Dask workflows. Coiled prioritizes enterprise-level security and provides cost-effective solutions through service level agreements, user-level management, and automatic termination of clusters when they’re no longer needed. Deploying your cluster on AWS or GCP is straightforward and can be accomplished in just a few minutes, all without needing a credit card. You can initiate your code from a variety of sources, including cloud-based services like AWS SageMaker, open-source platforms like JupyterHub, or even directly from your personal laptop, ensuring that you have the freedom and flexibility to work from anywhere. This level of accessibility and customization makes Coiled an ideal choice for teams looking to leverage Dask efficiently.

The NVIDIA Deep Learning GPU Training System (DIGITS) empowers engineers and data scientists by making deep learning accessible and efficient. With DIGITS, users can swiftly train highly precise deep neural networks (DNNs) tailored for tasks like image classification, segmentation, and object detection. It streamlines essential deep learning processes, including data management, neural network design, multi-GPU training, real-time performance monitoring through advanced visualizations, and selecting optimal models for deployment from the results browser. The interactive nature of DIGITS allows data scientists to concentrate on model design and training instead of getting bogged down with programming and debugging. Users can train models interactively with TensorFlow while also visualizing the model architecture via TensorBoard. Furthermore, DIGITS supports the integration of custom plug-ins, facilitating the importation of specialized data formats such as DICOM, commonly utilized in medical imaging. This comprehensive approach ensures that engineers can maximize their productivity while leveraging advanced deep learning techniques.

Pandera offers a straightforward, adaptable, and expandable framework for data testing, enabling the validation of both datasets and the functions that generate them. Start by simplifying the task of schema definition through automatic inference from pristine data, and continuously enhance it as needed. Pinpoint essential stages in your data workflow to ensure that the data entering and exiting these points is accurate. Additionally, validate the functions responsible for your data by automatically crafting relevant test cases. Utilize a wide range of pre-existing tests, or effortlessly design custom validation rules tailored to your unique requirements, ensuring comprehensive data integrity throughout your processes. This approach not only streamlines your validation efforts but also enhances the overall reliability of your data management strategies.

AI is today blocked by a lack of labeled data. Not models. The first data-centric AI platform powered by a programmatic approach will unblock AI. With its unique programmatic approach, Snorkel AI is leading a shift from model-centric AI development to data-centric AI. By replacing manual labeling with programmatic labeling, you can save time and money. You can quickly adapt to changing data and business goals by changing code rather than manually re-labeling entire datasets. Rapid, guided iteration of the training data is required to develop and deploy AI models of high quality. Versioning and auditing data like code leads to faster and more ethical deployments. By collaborating on a common interface, which provides the data necessary to train models, subject matter experts can be integrated. Reduce risk and ensure compliance by labeling programmatically, and not sending data to external annotators.