FloydHub is a Deep Learning platform that provides all the tools to help data science teams efficiently create a solution to scientific or business problems. Groups both large and small can utilize the given resources to increase their Artificial intelligence Velocity. The Workspace can be launched with a single click and contains all the features to start developing with cloud-based deep learning. It also includes other essential components like Github integration, Jupyter Notebooks & scripts, and an alerts system.
You can use the command line console to train models and easily co-ordinate with team members while also tracking the progress of experiments. The CLI makes it easy to initiate parallel training projects and can be combined to operate with your development environment resulting in maximum reproducibility. Models that are fully trained can be deployed via the CLI within seconds, after which they will be made available in API format. The scaling level can be controlled based on requirements, and the module also produces an auto-generated web page that you can share with colleagues.
Amazon provides comprehensive Machine Learning solutions in the form of Amazon SageMaker that shortens the time taken to create and train machine learning models. It is a full-fledged service that empowers professionals such as Application developers, data analysts to produce and train ML models at a fast rate. It simplifies the ML workflow, enabling you to concentrate on creating ML use cases such as forecasting customer behavior. Amazon SageMaker uses a methodology for building efficient ML models. Data goes through multiple phases: Prepare, Build, Train & Tune, and Deploy & Manage.
In the Prepare Stage, data is tagged for machine learning by the Ground Truth Module and prepared via the Data Wrangler Component. The Build Phase revolves around the optimization of data using the pre-built or custom algorithms. It features Local Mode that allows you to develop a prototype and run it on any device. You can also turn on SageMaker Autopilot, which will develop ML models with complete clarity.
The Sagemaker provides rich features to train ML models. Training can be initiated with a single press, and you can record & analyze each step. You can train large datasets and use the SageMaker Debugger to find and fix any errors that occur in training.
Google Cloud offers a fully-featured AI Platform to help businesses develop and maintain machine learning models. It is easy to use thanks to no-code tools and has an extensive range of features that can be used by every professional to create, train and deploy ML models.
The platform divides the machine learning life cycle into various steps. Datasets can be made and saved using BigQuery & Cloud Storage. You can take assistance from the pre-loaded Data Labeling Service to name each piece of data for entity extraction, classification, and other tasks for image, video information.
Once data has been prepared, it can be trained using state-of-the-art Build functionalities. You can develop high-profile ML models without any programming through AutoML’s User Interface or import code from Notebooks. Deep Learning projects can be instantly generated by choosing from one of the two open-source frameworks named Deep Learning Containers and Deep Learning VM Image.
Azure Machine Learning is an end-to-end platform that allows businesses to prepare, build and test Machine Learning models. It is equipped with an extensive toolkit that caters to the needs of every professional. Datasets can be instantly imported using drag & drop functionality, and you can link to several cloud-based Data sources such as Azure Data Lake, blob, or Azure SQL.
Machine Learning models can be prepared and trained without any coding through top-notch ML and Deep Learning algorithms composed of computer vision and text analysis. Models can be assessed by initiating ML pipelines and by crosschecking them with given datasets for correctness. For a deeper analysis, you can use graphs, check logs and perform diagnostics by running troubleshooting.
Azure Machine Learning supports multiple frameworks and tools. You can use it with popular frameworks such as PyTorch, scikit-learn, or Tensorflow. It is also compatible with frequently used IDE’s and programming languages used for data science like Python and R.
Dataiku offers a DSS (Data Science Studio) to help businesses create, manage and deploy their AI applications safely & securely for an affordable price. It provides several modules that assist throughout the software life cycle. These components include Data Preparation, Visualization, Machine learning, DataOps, MLOps. And Analytic Apps.
Data Preparation helps establish connections with cloud-based data sources, purifying it from redundancy and readying it to be pushed in machine learning applications. It provides a Visual flow to help project managers create data pipelines, a formula to combine and change datasets, and the power to develop predictive models. You can link data to leading Cloud-based technologies such as Amazon S3, HDFS, SQL & NoSQL, Google Cloud Storage, Microsoft Azure, and more.
The visualization module helps you conduct a thorough analysis of data. It offers several tools that consume less time to examine columns/rows, and you can dig deeper to analyze the placement of data, outliers, statistics. You can draw Charts & Graphs, which can be shared across your organization to enhance collaboration and build a better understanding. There are multiple options for producing charts, including pie charts, lift charts, bar charts, 2D-distribution, curves, and more.
TensorFlow is an end-to-end and open-source Library for dataflow and differentiable programming across a range of tasks that pave the way for the practical machine learning for everyone. The software is using a neural network for the machine learning process, and the excellent documentation set for productive environments. TensorFlow is surfacing all the agile tools that making way for secure deployments. The open-source library enables you to develop and train ML models, and you can get started via the quickly running off the Colab notebook right in your browser.
The software is dynamic in terms of providing a comprehensive and flexible ecosystem of tools, Libraries, and community resources that lets developers build and deploy robustly. The multiple features offered By TensorFlow are easy to model building via using intuitive high-level APIs, nimble ML production anywhere with the deployment of models in the cloud, flexible architecture for robust experimentation, and solution to multiple ML problems with step-by-step workflow.
RapidMiner is a data science and machine learning platform that allows users to unite their data and understand the changing trends through it. The software is a fully transparent, end-to-end data science platform that allows users to seamlessly integrate and optimize their data preparation for building ML models. It comes with a machine learning technology that enables users to design models using a visual workflow.
The platform allows users to deploy and manage models and turn them into perspective actions with complete end-to-end collaboration. It has a lightning-fast business impact that provides products to users through visual and automated analysis.
RapidMiner comes with jumpstart features which help users to accelerate business care success, and it helps in augmenting the whole research process. It offers model deployment and optimization, along with algorithm selection and validation. Lastly, it automatically builds visuals and helps in collaborating with business stakeholders for a model explanation.
BigML is a leading Machine Learning platform that helps thousands of businesses to make highly automated, data-driven decisions. The platform has the Machine Learning-as-a Service wave of innovation through its consumable, programmable and scalable software solution streamlining the creation and deployment of the smart application powered by state-of-the-art predictive models. The platform features a wide variety of basic Machine Learning resources that can be composed together to solve complex Machine Learning tasks.
With the help of this platform, customers can access those resources via the dashboard that has an intuitive interface or programmatically via its REST API multitude of libraries and tools. In addition to commercial activities, the platform is also playing an active role in promoting Machine Learning in education worldwide through its education program, making real BigML’s motto that makes it beautifully simple for everyone. It also has lots of key features that make it stronger than others.