Best Data Science Software for Google Cloud Platform

Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case. Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection.

Secure and manage the data lifecycle, from Edge to AI in any cloud or data centre. Operates on all major public clouds as well as the private cloud with a public experience everywhere. Integrates data management and analytics experiences across the entire data lifecycle. All environments are covered by security, compliance, migration, metadata management. Open source, extensible, and open to multiple data stores. Self-service analytics that is faster, safer, and easier to use. Self-service access to multi-function, integrated analytics on centrally managed business data. This allows for consistent experiences anywhere, whether it is in the cloud or hybrid. You can enjoy consistent data security, governance and lineage as well as deploying the cloud analytics services that business users need. This eliminates the need for shadow IT solutions.

Deepnote is building the best data science notebook for teams. Connect your data, explore and analyze it within the notebook with real-time collaboration and versioning. Share links to your projects with other analysts and data scientists on your team, or present your polished, published notebooks to end users and stakeholders. All of this is done through a powerful, browser-based UI that runs in the cloud.

Datalore is a platform for collaborative data science and analytics that aims to improve the entire analytics workflow and make working with data more enjoyable for both data scientists as well as data-savvy business teams. Datalore is a collaborative platform that focuses on data teams workflow. It offers technical-savvy business users the opportunity to work with data teams using no-code and low-code, as well as the power of Jupyter Notebooks. Datalore allows business users to perform analytic self-service. They can work with data using SQL or no-code cells, create reports, and dive deep into data. It allows core data teams to focus on simpler tasks. Datalore allows data scientists and analysts to share their results with ML Engineers. You can share your code with ML Engineers on powerful CPUs and GPUs, and you can collaborate with your colleagues in real time.

Coder offers self-hosted cloud development environments, provisioned as code and ready for developers from day one. Favored by enterprises, Coder is open source and can be deployed air-gapped on-premise or in your cloud, ensuring powerful infrastructure access without sacrificing governance. By shifting local development and source code to a centralized infrastructure, Coder allows developers to access their remote environments via their preferred desktop or web-based IDE. This approach enhances developer experience, productivity, and security. With Coder’s ephemeral development environments, provisioned as code from pre-defined templates, developers can instantly create new workspaces. This streamlines the process, eliminating the need to deal with local dependency versioning issues or lengthy security approvals. Coder enables developers to onboard or switch projects in a matter of minutes.

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.

TrueFoundry provides data scientists and ML engineers with the fastest framework to support the post-model pipeline. With the best DevOps practices, we enable instant monitored endpoints to models in just 15 minutes! You can save, version, and monitor ML models and artifacts. With one command, you can create an endpoint for your ML Model. WebApps can be created without any frontend knowledge or exposure to other users as per your choice. Social swag! Our mission is to make machine learning fast and scalable, which will bring positive value! TrueFoundry is enabling this transformation by automating parts of the ML pipeline that are automated and empowering ML Developers with the ability to test and launch models quickly and with as much autonomy possible. Our inspiration comes from the products that Platform teams have created in top tech companies such as Facebook, Google, Netflix, and others. These products allow all teams to move faster and deploy and iterate independently.

The Databricks Data Intelligence Platform enables your entire organization to utilize data and AI. It is built on a lakehouse that provides an open, unified platform for all data and governance. It's powered by a Data Intelligence Engine, which understands the uniqueness in your data. Data and AI companies will win in every industry. Databricks can help you achieve your data and AI goals faster and easier. Databricks combines the benefits of a lakehouse with generative AI to power a Data Intelligence Engine which understands the unique semantics in your data. The Databricks Platform can then optimize performance and manage infrastructure according to the unique needs of your business. The Data Intelligence Engine speaks your organization's native language, making it easy to search for and discover new data. It is just like asking a colleague a question.

An end-to-end solution gives you all the tools your data science team needs to scale your machine learning development, from research to production. cnvrg.io, the world's leading data science platform for MLOps (model management) is a leader in creating cutting-edge machine-learning development solutions that allow you to build high-impact models in half the time. In a collaborative and clear machine learning management environment, bridge science and engineering teams. Use interactive workspaces, dashboards and model repositories to communicate and reproduce results. You should be less concerned about technical complexity and more focused on creating high-impact ML models. The Cnvrg.io container based infrastructure simplifies engineering heavy tasks such as tracking, monitoring and configuration, compute resource management, server infrastructure, feature extraction, model deployment, and serving infrastructure.

SAS Visual Statistics allows multiple users to interactively explore data and then create and refine predictive models. Your statisticians and data scientists can use the most appropriate analytical modeling techniques to analyze your observations at a fine level. What will you get? The result? You can quickly build and refine models to target specific segments or groups, and run multiple scenarios simultaneously. To get better results, you can ask more "what-if" questions. You can also use an automatically generated score code to put your results into practice. Multiple users can interact with data visually. They can add, change, or remove outliers. You can instantly see how changes affect the predictive power of your model and make adjustments quickly. Data science teams have the freedom to work in the language they prefer, so they can make the most of their talents. SAS Visual Statistics combines all analytical assets.

SAS®, Viya®, data science offerings offer a comprehensive, scalable analytical environment that is quick and easy to use, allowing you to meet diverse business requirements. Automatically generated insights allow you to identify the most commonly used variables across all models, the most significant variables selected across models, and assess results for all models. Natural language generation capabilities allow you to create project summaries in plain language. This makes it easy to interpret reports. Analytics team members can add project notes and comments to the insights report to facilitate communication between team members. SAS allows you to embed open source code into an analysis and call open-source algorithms seamlessly within its environment. This allows for collaboration within your organization as users can program in the language they prefer. SAS Deep Learning with Python (DLPy) is also available on GitHub.

Dask is free and open-source. It was developed in collaboration with other community projects such as NumPy and pandas. Dask uses existing Python data structures and APIs to make it easy for users to switch between NumPy/pandas and scikit-learn-powered versions. Dask's schedulers can scale to thousands of node clusters, and its algorithms have been tested at some of the most powerful supercomputers around the world. You don't necessarily need a large cluster to get started. Dask ships schedulers that can be used on personal computers. Many people use Dask to scale computations on their laptops, using multiple cores and their disk for extra storage. Dask exposes lower level APIs that allow you to build custom systems for your own applications. This allows open-source leaders to parallelize their own packages, and business leaders to scale custom business logic.

Amadea technology uses the fastest real-time modeling and calculation engine on the market. You can create, deploy, and automate your analytics projects in one integrated environment. Data quality is key to successful analytical projects. Amadea, the fastest real-time calculation engine on the market, allows companies to prepare large and/or complex data and to use it in real time, regardless of its volume. ISoft was founded on the simple observation that successful analytical projects require participation of business users at all stages. Amadea is accessible to all users and was built on a simple interface that is easy to use. Amadea's fastest real-time calculation engine allows you to specify, prototype, and build your data applications simultaneously. Amadea has the fastest real-time data analytics engine on the market with 10 million lines per second and per core for standard calculations.

Data is essential when it comes to constructing, streamlining and growing your company. Unfortunately, it can be difficult to get the most out of data. Many organizations face incompatibilities, slow results, poor access to data and spiraling costs. Leaders who can transform raw data into real results are the ones who will succeed in today's competitive landscape. This is possible by empowering everyone in your company to be able analyze, build, and collaborate on machine learning and AI solutions. Streamflux is a one stop shop for all your data analytics and AI needs. Our self-service platform gives you the freedom to create end-to-end data solutions. It uses models to answer complex questions, and evaluates user behavior. You can transform raw data into real business impact in days instead of months, whether you are generating recommendations or predicting customer turnover and future revenue.

Snowflake®, a cloud data platform and enterprise-grade architecture, allows you to securely store and use your data in the cloud. Drag-and-drop visual workflow design and built-in ETL to connect, clean and transform data from 250+ sources. You can generate actionable insights and insights from your data using the latest Search and AI-driven technology. Our AI/ML environments can be used to build, tune, and deploy models for predictive analytics or forecasting. Our AI/ML environments are available to help you take your data to new heights. The PurpleCube Data Science module allows you to create, train, tune, and deploy AI models for forecasting and predictive analysis. PurpleCube Analytics allows you to create BI visualizations, search your data with natural language and use AI-driven insights and smart recommendations to provide answers to questions that you didn't know to ask.

Daft is an ETL, analytics, and ML/AI framework that can be used at scale. Its familiar Python Dataframe API is designed to outperform Spark both in terms of performance and ease-of-use. Daft integrates directly with your ML/AI platform through zero-copy integrations of essential Python libraries, such as Pytorch or Ray. It also allows GPUs to be requested as a resource when running models. Daft is a lightweight, multithreaded local backend. When your local machine becomes insufficient, it can scale seamlessly to run on a distributed cluster. Daft supports User-Defined Functions in columns. This allows you to apply complex operations and expressions to Python objects, with the flexibility required for ML/AI. Daft is a lightweight, multithreaded local backend that runs locally. When your local machine becomes insufficient, it can be scaled to run on a distributed cluster.

ZinkML is an open-source data science platform that does not require any coding. It was designed to help organizations leverage data more effectively. Its visual and intuitive interface eliminates the need for extensive programming expertise, making data sciences accessible to a wider range of users. ZinkML streamlines data science from data ingestion, model building, deployment and monitoring. Users can drag and drop components to create complex pipelines, explore the data visually, or build predictive models, all without writing a line of code. The platform offers automated model selection, feature engineering and hyperparameter optimization, which accelerates the model development process. ZinkML also offers robust collaboration features that allow teams to work seamlessly together on data science projects. By democratizing the data science, we empower businesses to get maximum value out of their data and make better decisions.

Real-time business decisions that are ubiquitous without the need for expensive and specialized resources. UBIX is at the forefront of generative AI, reinforcement learning and business-led automation. Our patented SaaS platform contextualizes data from within and outside the enterprise and presents it in minutes, rather than days, ensuring AI innovation transformation. Improve daily decisions that have an impact on productivity, waste, compliance and profitability. The right data is delivered to the right business leaders at the right time and in the correct format. In just a few days and 5 simple steps, you can AI-enable any enterprise. Data scientists can be freed up to innovate instead of generating reports and improve business intelligence initiatives. Artificial intelligence is not just for large organizations or technical teams. It's available to all organizations. UBIX makes advanced analytics and artificial intelligence available to everyone.