Alternatives to H2O.ai

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.

Qloo, the "Cultural AI", is capable of decoding and forecasting consumer tastes around the world. Privacy-first API that predicts global consumer preferences, catalogs hundreds of million of cultural entities, and is privacy-first. Our API provides contextualized personalization and insight based on deep understanding of consumer behavior. We have access to more than 575,000,000 people, places, and things. Our technology allows you to see beyond trends and discover the connections that underlie people's tastes in their world. Our vast library includes entities such as brands, music, film and fashion. We also have information about notable people. Results are delivered in milliseconds. They can be weighted with factors like regionalization and real time popularity. Companies who want to use best-in-class data to enhance their customer experiences. Our flagship recommendation API provides results based on demographics and preferences, cultural entities, metadata, geolocational factors, and metadata.

Composable is an enterprise-grade DataOps platform designed for business users who want to build data-driven products and create data intelligence solutions. It can be used to design data-driven products that leverage disparate data sources, live streams, and event data, regardless of their format or structure. Composable offers a user-friendly, intuitive dataflow visual editor, built-in services that facilitate data engineering, as well as a composable architecture which allows abstraction and integration of any analytical or software approach. It is the best integrated development environment for discovering, managing, transforming, and analysing enterprise data.

Amazon SageMaker, a fully managed service, provides data scientists and developers with the ability to quickly build, train, deploy, and deploy machine-learning (ML) models. SageMaker takes the hard work out of each step in the machine learning process, making it easier to create high-quality models. Traditional ML development can be complex, costly, and iterative. This is made worse by the lack of integrated tools to support the entire machine learning workflow. It is tedious and error-prone to combine tools and workflows. SageMaker solves the problem by combining all components needed for machine learning into a single toolset. This allows models to be produced faster and with less effort. Amazon SageMaker Studio is a web-based visual interface that allows you to perform all ML development tasks. SageMaker Studio allows you to have complete control over each step and gives you visibility.

Neuton.AI, an automated solution, empowering users to build accurate predictive models and make smart predictions with: Zero code solution Zero need for technical skills Zero need for data science knowledge

JADBio is an automated machine learning platform that uses JADBio's state-of-the art technology without any programming. It solves many open problems in machine-learning with its innovative algorithms. It is easy to use and can perform sophisticated and accurate machine learning analyses, even if you don't know any math, statistics or coding. It was specifically designed for life science data, particularly molecular data. It can handle the unique molecular data issues such as low sample sizes and high numbers of measured quantities, which could reach into the millions. It is essential for life scientists to identify the biomarkers and features that are predictive and important. They also need to know their roles and how they can help them understand the molecular mechanisms. Knowledge discovery is often more important that a predictive model. JADBio focuses on feature selection, and its interpretation.

RZT aiOS provides all the benefits of a unified AI platform, and more. It's not just a platform, it's an Operating System that connects, manages, and unifies all your AI initiatives. AI developers can now do what used to take months in days thanks to aiOS process management which dramatically increases their productivity. This Operating System provides an intuitive environment for AI development. It allows you to visually build models, explore data and create processing pipelines. You can also run experiments and view analytics. It's easy to do all of this without any advanced software engineering skills.

Model building software for today's machine learning age incorporates credit risk modelling expertise spanning over thirty years. Modeller is a flexible, transparent, interactive, and feature-rich tool that helps organizations get more out of their analytical teams. It allows for a variety of techniques, rapid development of powerful models, full explanation, and advancement of less experienced members of the team. You can choose from a variety of modeling techniques, including machine-learning, to achieve optimal predictive accuracy, especially when working with complex interrelationships and multicollinearity. At the touch of a button, you can create industry-standard binary and continuous target models. You can use decision tree modeling with CHAID trees and CART. You can choose from logistic regression, elastic network models, survival analysis (Cox PH), random forest, XGBoost and stochastic gradient descend. SAS, SQL and PMML are all available export options for use in other scoring and decisioning programs.

AI Cloud is a new approach that addresses the challenges and opportunities presented by AI today. A single system of records that accelerates the delivery of AI to production in every organization. All users can collaborate in a single environment that optimizes the entire AI lifecycle. The AI Catalog facilitates seamlessly finding, sharing and tagging data. This helps to increase collaboration and speed up time to production. The catalog makes it easy to find the data you need to solve a business problem. It also ensures security, compliance, consistency, and consistency. Contact Support if your database is protected by a network rule that allows connections only from certain IP addresses. An administrator will need to add addresses to your whitelist.

Auger.AI offers the best solution to ensure accuracy of machine learning models. Our MLRAM tool (Machine Learning Review & Monitoring) ensures that your models are always accurate. It even calculates the ROI for your predictive model! MLRAM can be used with any machine-learning technology stack. Inaccurate predictions can cost you money if your ML system's lifecycle doesn't include consistent measurement. Frequent retraining models can be costly and may not solve the problem if they are experiencing concept drift. MLRAM is a valuable tool for both data scientists and business users. It includes features such as accuracy visualization graphs and performance alerts. It also allows for anomaly detection and automated optimized retraining. It takes only one line of code to connect your predictive model with MLRAM. Qualified users can get a one-month free trial of MLRAM. Auger.AI is the most accurate AutoML platform.

Neural Designer is a data-science and machine learning platform that allows you to build, train, deploy, and maintain neural network models. This tool was created to allow innovative companies and research centres to focus on their applications, not on programming algorithms or programming techniques. Neural Designer does not require you to code or create block diagrams. Instead, the interface guides users through a series of clearly defined steps. Machine Learning can be applied in different industries. These are some examples of machine learning solutions: - In engineering: Performance optimization, quality improvement and fault detection - In banking, insurance: churn prevention and customer targeting. - In healthcare: medical diagnosis, prognosis and activity recognition, microarray analysis and drug design. Neural Designer's strength is its ability to intuitively build predictive models and perform complex operations.

Data analysts, engineers, scientists, and other scientists can be brought together. Automate self-service analytics and machine learning operations. Get results today, build for tomorrow. Dataiku DSS is a collaborative data science platform that allows data scientists, engineers, and data analysts to create, prototype, build, then deliver their data products more efficiently. Use notebooks (Python, R, Spark, Scala, Hive, etc.) You can also use a drag-and-drop visual interface or Python, R, Spark, Scala, Hive notebooks at every step of the predictive dataflow prototyping procedure - from wrangling to analysis and modeling. Visually profile the data at each stage of the analysis. Interactively explore your data and chart it using 25+ built in charts. Use 80+ built-in functions to prepare, enrich, blend, clean, and clean your data. Make use of Machine Learning technologies such as Scikit-Learn (MLlib), TensorFlow and Keras. In a visual UI. You can build and optimize models in Python or R, and integrate any external library of ML through code APIs.

RapidMiner is redefining enterprise AI so anyone can positively shape the future. RapidMiner empowers data-loving people from all levels to quickly create and implement AI solutions that drive immediate business impact. Our platform unites data prep, machine-learning, and model operations. This provides a user experience that is both rich in data science and simplified for all others. Customers are guaranteed success with our Center of Excellence methodology, RapidMiner Academy and no matter what level of experience or resources they have.

Combine Data Science, Business Intelligence and Data Management Capabilities into One Integrated, Self-Serve Platform. Analance is an end-to-end platform with robust and salable features that combines Data Science and Advanced Analytics, Business Intelligence and Data Management into a single integrated platform. It provides core analytical processing power to ensure that data insights are easily accessible to all, performance remains consistent over time, and business objectives can be met within a single platform. Analance focuses on making quality data into accurate predictions. It provides both citizen data scientists and data scientists with pre-built algorithms as well as an environment for custom programming. Company - Overview Ducen IT provides advanced analytics, business intelligence, and data management to Fortune 1000 companies through its unique data science platform Analance.

Darwin is an automated machine-learning product that allows your data science and business analysis teams to quickly move from data to meaningful results. Darwin assists organizations in scaling the adoption of data science across their teams and the implementation machine learning applications across operations to become data-driven enterprises.

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.

ClearML is an open-source MLOps platform that enables data scientists, ML engineers, and DevOps to easily create, orchestrate and automate ML processes at scale. Our frictionless and unified end-to-end MLOps Suite allows users and customers to concentrate on developing ML code and automating their workflows. ClearML is used to develop a highly reproducible process for end-to-end AI models lifecycles by more than 1,300 enterprises, from product feature discovery to model deployment and production monitoring. You can use all of our modules to create a complete ecosystem, or you can plug in your existing tools and start using them. ClearML is trusted worldwide by more than 150,000 Data Scientists, Data Engineers and ML Engineers at Fortune 500 companies, enterprises and innovative start-ups.

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.

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.

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.

A fully-featured machine learning platform empowers enterprises to conduct real data science at scale and speed. You can spend less time managing infrastructure and tools so that you can concentrate on building machine learning applications to propel your business forward. Anaconda Enterprise removes the hassle from ML operations and puts open-source innovation at the fingertips. It provides the foundation for serious machine learning and data science production without locking you into any specific models, templates, workflows, or models. AE allows data scientists and software developers to work together to create, test, debug and deploy models using their preferred languages. AE gives developers and data scientists access to both notebooks as well as IDEs, allowing them to work more efficiently together. They can also choose between preconfigured projects and example projects. AE projects can be easily moved from one environment to the next by being automatically packaged.

Machine learning uncovers hidden patterns in enterprise data and generates new value for businesses. Oracle Machine Learning makes it easier to create and deploy machine learning models for data scientists by using AutoML technology and reducing data movement. It also simplifies deployment. Apache Zeppelin notebook technology, which is open-source-based, can increase developer productivity and decrease their learning curve. Notebooks are compatible with SQL, PL/SQL and Python. Users can also use markdown interpreters for Oracle Autonomous Database to create models in their preferred language. No-code user interface that supports AutoML on Autonomous Database. This will increase data scientist productivity as well as non-expert users' access to powerful in-database algorithms to classify and regression. Data scientists can deploy integrated models using the Oracle Machine Learning AutoML User Interface.

DATAGYM allows data scientists and machine-learning experts to label images up 10x faster than before. AI-assisted annotators reduce manual labeling, give you more time for fine tuning ML models, and speed up your product launch. Reduce data preparation time by up to half and accelerate your computer vision projects

Alteryx is the launchpad to automation breakthroughs. The results are unrivalled, whether you're looking for personal growth, rapid innovation, or transformative digital outcomes. This unique innovation combines analytics, data science, and process automation into a single platform that empowers every person and organization to make business-changing breakthroughs.

All the steps involved in building machine learning algorithms and predicting results, all in one click. Data Dialog allows you to easily shape your data without having to wrangle your files. Your prediction reports can be shared with your team members or made public. Let anyone make predictions on your model. Our low-code API allows you to integrate dynamic ML predictions directly into your app. Real-time prediction of willingness to pay, score leads, and many other things. AI gives you access to the most advanced algorithms in the world, without compromising on performance. Forecast revenue, optimize supply chain, personalize your marketing. Now you can see what the next steps are. In minutes, you can add a CSV file or integrate with your favorite data sources. Select your prediction column from the dropdown and we'll automatically build the AI. Visualize the top drivers, predicted results, and simulate "what-if?" scenarios.

Metacoder makes data processing faster and more efficient. Metacoder provides data analysts with the flexibility and tools they need to make data analysis easier. Metacoder automates data preparation steps like cleaning, reducing the time it takes to inspect your data before you can get up and running. It is a good company when compared to other companies. Metacoder is cheaper than similar companies and our management is actively developing based upon our valued customers' feedback. Metacoder is primarily used to support predictive analytics professionals in their work. We offer interfaces for database integrations, data cleaning, preprocessing, modeling, and display/interpretation of results. We make it easy to manage the machine learning pipeline and help organizations share their work. Soon, we will offer code-free solutions for image, audio and video as well as biomedical data.

FortressIQ is the industry's most advanced process-intelligence platform. It allows enterprises to decode work and transform experiences. FortressIQ combines innovative computer vision with artificial intelligence to provide unprecedented process insights. It is extremely fast and delivers detail and accuracy that are unattainable using traditional methods. The platform automatically acquires process data across multiple systems. This empowers enterprises to understand, monitor and improve their operations, employee and customer experience, and every business process. FortressIQ was established in 2017 and is supported by Lightspeed Venture Partners and Boldstart Ventures as well as Comcast Ventures and Eniac Ventures. Continuously and automatically identify inefficiencies and process variations to determine optimal process paths and reduce time to automate.

Cauliflower can process feedback and comments for any type of service or product. Cauliflower uses Artificial Intelligence (AI) to identify the most important topics, evaluate them, and establish relationships. Machine learning models in-house developed for extracting content and evaluating sentiment. Intuitive dashboards that offer filter options and drill-downs. You can use included variables to indicate language, weight, ID and time. In the dropdown, you can define your own filter variables. Cauliflower can translate the results into a common language if necessary. Instead of reading customer feedback sporadically and quoting individual opinions, define a company-wide language.

Machine learning can be shared across your organization by collaborating, democratizing, and operationalizing it. Data science is a team sport. Data scientists, citizen data scientists and data engineers, as well as business users and developers, need flexible tools that facilitate collaboration, automation and reuse of analytic workflows. Algorithms are just one part of advanced analytic technology. Companies must increase their focus on the management, deployment, and monitoring analytic models in order to deliver predictive insights. Smart businesses depend on platforms that can support the entire lifecycle of analytics and provide enterprise security and governance. TIBCO®, Data Science software allows organizations to innovate and solve complex problems more quickly, ensuring that predictive findings are quickly turned into optimal outcomes. Flexible authoring and deployment capabilities allow organizations to expand their data science deployments throughout the organization with TIBCO Data Science.

Produvia is a serverless machine-learning development service. Partner with Produvia for machine model development and deployment using serverless cloud infrastructure. Produvia partners with Fortune 500 companies and Global 500 businesses to develop and deploy machine-learning models using modern cloud infrastructure. Produvia uses state-of-the art methods in machine learning and deep-learning technologies to solve business problems. Overspending on infrastructure costs can lead to organizations. Modern organizations employ serverless architectures to lower server costs. Complex servers and legacy code can hold back organizations. Machine learning technologies are used by modern organizations to rewrite technology stacks. Software developers are hired by companies to write code. Machine learning is used to create software that codes in modern companies.

Unutilized data is the biggest obstacle to scaling AI-powered decision making. IBM Cloud Pak®, for Data is a unified platform that provides a data fabric to connect, access and move siloed data across multiple clouds or on premises. Automate policy enforcement and discovery to simplify access to data. A modern cloud data warehouse integrates to accelerate insights. All data can be protected with privacy and usage policy enforcement. To gain faster insights, use a modern, high-performance cloud storage data warehouse. Data scientists, analysts, and developers can use a single platform to create, deploy, and manage trusted AI models in any cloud.

With open-source Metaflow, you can design and develop data-intensive projects. You can scale them up and deploy them on the fully managed Outerbounds platform. All your data science and ML projects can be managed from one platform. Access data securely from existing data warehouses. A cluster that is optimized for cost and scale can be used to compute. 24/7 managed orchestration of production workflows. Results can be used to power any application. Your engineers will give your data scientists superpowers. Outerbounds Platform enables data scientists to quickly develop, experiment at scale, then deploy to production with confidence. All within the boundaries of your engineers' policies and processes, all running on your cloud account, fully supported by us. Security is part of our DNA, not at its perimeter. Through multiple layers of security, the platform adapts to your policies. Centralized authentication, a strict permission limit, and granular task execution role.

Data science platform that increases productivity and has unparalleled capabilities. Create and evaluate machine learning (ML), models of higher quality. Easy deployment of ML models can help increase business flexibility and enable enterprise-trusted data work faster. Cloud-based platforms can be used to uncover new business insights. Iterative processes are necessary to build a machine-learning model. This ebook will explain how machine learning models are constructed and break down the process. Use notebooks to build and test machine learning algorithms. AutoML will show you the results of data science. It is easier and faster to create high-quality models. Automated machine-learning capabilities quickly analyze the data and recommend the best data features and algorithms. Automated machine learning also tunes the model and explains its results.

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.

Saturn Cloud is a data science and machine learning platform flexible enough for any team supporting Python, R, and more. Scale, collaborate, and utilize built-in management capabilities to aid you when you run your code.

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.

The only platform that can handle all aspects of data pipeline. Gathr was built from the ground up to support a cloud-first world. It is the only platform that can handle all your data integration needs - ingestion and ETL, ELT and CDC, streaming analytics and data preparation, machine-learning, advanced analytics, and more. Gathr makes it easy for anyone to build and deploy pipelines, regardless of their skill level. Ingestion pipelines can be created in minutes and not weeks. You can access data from any source and deliver it to any destination. A wizard-based approach allows you to quickly build applications. A templatized CDC app allows you to replicate data in real time. Native integration for all sources. All the capabilities you need to succeed today or tomorrow. You can choose between pay-per-use, free, or customized according to your needs.

Machine Learning and Predictive Analytics Platform. Advanced artificial intelligence is a pre-built platform for machine learning and big-data analytics that can enhance data-driven decision making. OpenText Magellan makes predictive analytics easy to use and provides flexible data visualizations that maximize business intelligence. Artificial intelligence software reduces the need to manually process large amounts of data. It presents valuable business insights in a manner that is easily accessible and relevant to the organization's most important objectives. Organizations can enhance business processes by using a curated combination of capabilities such as predictive modeling, data discovery tools and data mining techniques. IoT data analytics is another way to use data to improve decision-making based on real business intelligence.

The definitive system for modern technical computing. Mathematica is the global standard for technical computing. It has been the main computing environment for millions of students, educators, and innovators around the globe for over three decades. Mathematica is widely admired for its technical prowess as well as its elegant ease-of-use. It seamlessly integrates all aspects of technical computing and is available in the cloud via any web browser as well as natively on any modern desktop system. Mathematica is a pioneer in technical computing support and workflows, thanks to its energetic development and consistent vision over three decades.

With a fully automated cloud infrastructure, experts can explore data and write stable codes at the same time. Zerve’s data science environment gives data scientists and ML teams a unified workspace to explore, collaborate and build data science & AI project like never before. Zerve provides true language interoperability. Users can use Python, R SQL or Markdown in the same canvas and connect these code blocks. Zerve offers unlimited parallelization, allowing for code blocks and containers to run in parallel at any stage of development. Analysis artifacts can be automatically serialized, stored and preserved. This allows you to change a step without having to rerun previous steps. Selecting compute resources and memory in a fine-grained manner for complex data transformation.

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.

FARO Sphere XG, a cloud-based digital platform for digital reality, provides users with a centralized collaborative experience across all of the company's 3D modeling and reality capture applications. Sphere XG, when paired with Stream, enables faster 3D data collection, processing, and project management anywhere in the world. Sphere XG is a systematized tool that allows users to organize 3D scans, 360-degree photos and 3D models. It also allows them to manage data from different teams around the globe. Sphere XG allows you to view and share 3D point clouds, 360-degree photo documentation, and floorplans all in one place. You can also track the progress of your project over time. Ideal for 4D progress management, where the ability of comparing elements over time is crucial, project managers and VDC manager can better democratize the data and eliminate the necessity to use two platforms.

The GPUs are fast at transferring data, but they have very limited locality of reference due to their small caches. They are designed to apply a lot compute to little data, and not a lot compute to a lot data. They are designed to run full layers of computation in order to fully fill their computational pipeline. (See Figure 1 below). Because large models have small memory sizes (tens to gigabytes), GPUs are placed together and models are distributed across them. This creates a complicated and painful software stack. It also requires synchronization and communication between multiple machines. The CPUs on the other side have much larger caches than GPUs and a lot of memory (terabytes). A typical CPU server may have memory equivalent to hundreds or even tens of GPUs. The CPU is ideal for a brain-like ML environment in which pieces of a large network are executed as needed.

The most complex AI, ML, Deep Learning models can be trained. With just a few clicks, you can scale from a single machine up to a whole fleet of VMs. Lambda Cloud makes it easy to scale up or start your Deep Learning project. You can get started quickly, save compute costs, and scale up to hundreds of GPUs. Every VM is pre-installed with the most recent version of Lambda Stack. This includes major deep learning frameworks as well as CUDA®. drivers. You can access the cloud dashboard to instantly access a Jupyter Notebook development environment on each machine. You can connect directly via the Web Terminal or use SSH directly using one of your SSH keys. Lambda can make significant savings by building scaled compute infrastructure to meet the needs of deep learning researchers. Cloud computing allows you to be flexible and save money, even when your workloads increase rapidly.

Accelerate scientific discovery, empower your R&D team and use harmonized data in cloud to accelerate your R&D. The Tetra R&D Data Cloud is the only cloud-native data platform for global pharmaceutical companies. It combines the power of the largest Life Sciences integrations network and deep domain knowledge to provide a future-proof solution to harness the power of your most important asset, R&D data. The cloud covers the entire life-cycle of your R&D data: from acquisition, harmonization, engineering, downstream analysis, and native support for state–of-the–art data science tools. Pre-built integrations allow for easy connection to instruments, informatics and analytics applications, ELN/LIMSs, CRO/CDMOs, and other vendors. Data acquisition, management, harmonization, integration/engineering and data science enablement in one single platform.

AI is changing everything. AI is becoming ubiquitous. More intelligence is moving to the end devices today, and mobile is quickly becoming the dominant AI platform. Building on the smartphone foundation and the scale of mobile, Qualcomm envisions making AI ubiquitous--expanding beyond mobile and powering other end devices, machines, vehicles, and things. To make this a reality, we are developing, commercializing, and marketing power-efficient on-device AI and edge cloud AI. AI allows devices and things to perceive, reason and act intuitively. AI, which draws inspiration from the human brain will enhance our human abilities by being a natural extension to our senses. Through seamless interactions in everyday life, AI will personalize our lives and enhance our experience. Gartner predicts that AI augmentation will bring $3.3 trillion in business value by 2021. These benefits can be achieved across industries by combining cloud inference and on-device intelligence.

Predictive modeling with Machine Learning and Explainable Ai. FICO®, Analytics Workbench™, is a comprehensive suite of state-of the-art analytic authoring software that empowers companies to make better business decisions throughout the customer lifecycle. Data scientists can use it to build superior decisioning abilities using a variety of predictive data modeling tools, including the most recent machine learning (ML), and explainable AI (xAI) methods. FICO's innovative intellectual property enables us to combine the best of open-source data science and machine learning to provide world-class analytical capabilities to find, combine, and operationalize data predictive signals. Analytics Workbench is built upon the FICO®, leading platform that allows for new predictive models and strategies to easily be put into production.

NVIDIA Merlin enables data scientists, machine-learning engineers, and researchers, to build high-performance recommenders at scale. Merlin includes libraries, methods and tools to streamline the building and deployment of recommenders. These include addressing common challenges in preprocessing, feature engineering and training. Merlin components and capabilities have been optimized to support retrieval, scoring, filtering and ordering of hundreds terabytes data. All of this is accessible via easy-to-use interfaces. Merlin can help you make better predictions, increase click-through rates and deploy faster to production. NVIDIA Merlin is part of NVIDIA AI and advances our commitment to support innovative practitioners doing their best. NVIDIA Merlin is designed as an end-toend solution that can be integrated into existing recommender workflows utilizing data science and machine learning.

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.

IBM SPSS Modeler, a leading visual data-science and machine-learning (ML) solution, is designed to help enterprises accelerate their time to value through the automation of operational tasks by data scientists. It is used by organizations around the world for data preparation, discovery, predictive analytics and model management and deployment. ML is also used to monetize data assets. IBM SPSS Modeler transforms data in the best possible format for accurate predictive modeling. You can now analyze data in just a few clicks, identify fixes, screen fields out and derive new characteristics. IBM SPSS Modeler uses its powerful graphics engine to help you bring your insights to life. The smart chart recommender will select the best chart from dozens of options to share your insights.