Data Science and Machine Learning Platforms reviews by real, verified users. Find unbiased ratings on user satisfaction, features, and price based on the most reviews available anywhere.
Data Science and Machine Learning Platforms provide users with tools to build, deploy, and monitor machine learning algorithms. These platforms combine intelligent, decision-making algorithms with data, thereby enabling developers to create a business solution. Some platforms offer prebuilt algorithms and simplistic workflows with such features as drag-and-drop modeling and visual interfaces that easily connect necessary data to the end solution, while others require a greater knowledge of development and coding. These algorithms can include functionality for image recognition, natural language processing, voice recognition, and recommendation systems, in addition to other machine learning capabilities.
The nature of some Data Science and Machine Learning Platforms enables users without intensive data science skills to benefit from the platforms’ features. AI platforms are very similar to platforms as a service (PaaS), which allow for basic application development, but these products differ by offering machine learning options.
To qualify for inclusion in the Data Science and Machine Learning Platforms category, a product must:
RapidMiner brings artificial intelligence to the enterprise through an open and extensible data science platform. Built for analytics teams, RapidMiner unifies the entire data science lifecycle from data prep to machine learning to predictive model deployment.
Together with IBM Watson Machine Learning, IBM Watson Studio is a leading data science and machine learning platform built from the ground up for an AI-powered business. It helps enterprises scale data science operations across the lifecycle--simplifying the process of experimentation to deployment, speeding up data exploration and preparation, as well as model development and training. IBM Watson Studio is code-optional, allowing both data scientists and business analysts to work on the same
Alteryx, Inc. is a leader in self-service data analytics. Alteryx Analytics provides analysts with the unique ability to easily prep, blend, and analyze all of their data using a repeatable workflow, then deploy and share analytics at scale for deeper insights in hours, not weeks. Analysts love the Alteryx Analytics platform because they can connect to and cleanse data from data warehouses, cloud applications, spreadsheets, and other sources, easily join this data together, then perform analytic
Anaconda Enterprise helps organizations harness data science, machine learning, and AI at the pace demanded by today's digital interactions. Anaconda Enterprise combines core AI technologies, governance, and cloud-native architecture. Each piece‚Äîcore AI, governance, and cloud nativere critical components to enabling organizations to automate AI at speed and scale.
Use your own data to create, train, and deploy machine learning and deep learning models. Leverage an automated, collaborative workflow to grow intelligent business applications easily and with more confidence.
Explorium offers a first of its kind data science platform powered by automatic data discovery and feature engineering. By automatically connecting to thousands of external data sources (premium, partner and public) and leveraging machine learning to distill the most impactful signals, the Explorium platform empowers data scientists and business leaders to drive decision-making by eliminating the barrier to acquiring the right data and fueling superior predictive power.
The IBM SPSS Modeler is a leading, visual data science and machine learning solution. It helps enterprises accelerate time to value and desired outcome by speeding the operational tasks for data scientists. Leading organizations worldwide rely on IBM for data discovery, predictive analytics, model management and deployment, and machine learning to monetize data assets. The IBM SPSS Modeler empowers organizations to tap data assets and modern applications with complete, out-of-box algorithms and
The primary mission of RStudio is to build a sustainable open-source business that creates software for data science and statistical computing. You may have already heard of some of our work, such as the RStudio IDE, Rmarkdown, shiny, and many packages in the tidyverse. Our open source projects are supported by our commercial products that help teams of R users work together effectively, share computing resources, and publish their results to decision makers within the organization. We also bui
Kraken by Big Squid is an AutoML platform built to enable data analysts with deeper insights and to scale data scientists across an organization. Machine Learning is helping companies become more data-driven than ever before. Although historical and predictive reporting is extremely valuable, machine learning insights through Kraken provide an even deeper understanding of the value and quality of your data. With direct connections to your existing BI platform or data warehouse, Kraken empowers
IBM Decision Optimization (CPLEX) is a family of prescriptive analytics products that combines mathematical and AI techniques to help address business decision-making such as operational, tactical /strategic planning and scheduling processes. The solutions enable business decision-makers to choose the optimal course of action from millions of alternatives when faced with decisions that involve multiple variables, trade-off possibilities and complex constraints. It includes optimization modeling
Amazon SageMaker is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.
Since 2007, we are creating the most powerful framework to push the barriers of analytics, predictive analytics, AI and Big Data, while offering a helpful, fast and friendly environment. The TIMi Suite consists of four tools: 1. Anatella (Analytical ETL & Big Data), 2. Modeler (Auto-ML / Automated Predictive Modelling / Automated-AI), 3. StarDust (3D Segmentation) 4. Kibella (BI Dashboarding solution).
H2O.ai is empowering companies to be AI companies. Market leading organizations are using H2O.ai platforms to solve a myriad of AI transformation use cases across industries, including determining credit, decrease fraud and money laundering risks; improve product design, marketing and business innovation; improve early disease detection, drug discovery, personalized medicine; increase customer experiences and loyalty, and improve brand safety. H2O.ai offers enterprise customers with multiple p
Dataiku is the centralized data platform that moves businesses along their data journey from analytics at scale to enterprise AI. By providing a common ground for data experts and explorers, a repository of best practices, shortcuts to machine learning and AI deployment/management, and a centralized, controlled environment, Dataiku is the catalyst for data-powered companies. Customers across retail, e-commerce, health care, finance, transportation, the public sector, manufacturing, pharmaceuti
Qubole is the open data lake company that provides a simple and secure data lake platform for machine learning, streaming, and ad-hoc analytics. No other platform provides the openness and data workload flexibility of Qubole while radically accelerating data lake adoption, reducing time to value, and lowering cloud data lake costs by 50 percent. Qubole’s Platform provides end-to-end data lake services such as cloud infrastructure management, data management, continuous data engineering, analytic
Cloud AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs, by leveraging Google's state-of-the-art transfer learning, and Neural Architecture Search technology
DataRobot is the premier platform for automated machine learning. With a library of hundreds of the most powerful open source machine learning algorithms, DataRobot automates feature engineering, model creation, and hyperparameter tuning to expedite the deployment of advanced AI applications. The platform encapsulates every best practice and safeguard to help organizations accelerate and scale their data science capabilities while maximizing transparency, accuracy and collaboration between train
SAS Enterprise Miner is a software provide insights that drive better decision making, it streamline the data mining process to develop models quickly, understand key relationships and find the patterns that matter most.
Oracle Data Science Cloud Service enables data science teams to easily organize their work, access data and computing resources, and build, train, deploy, and manage models on the Oracle Cloud. The platform makes data science teams more productive, and enables them to deploy more work faster to power their organizations with machine learning.
Beijing ZetYun Technology Co., Ltd. (DataCanvas) was founded in 2013, focusing on the continuous development and construction of automatic data science platform, focusing on providing a complete set of development platform for data scientists and AI practitioners, and providing comprehensive supporting services for intelligent upgrading and transformation of government and enterprises. DataCanvas is a Chinese company independently researched and developed. Relying on the domestic and overseas l
The amount of data being produced within companies is increasing at a rapid rate. Businesses are realizing its importance and are leveraging this accumulated data to gain a competitive advantage. Data is considered to be the oil that keeps an organization running and companies are hence turning their data into insights to drive business decisions and improve product offerings. With data science, of which artificial intelligence (AI) is a part, users are enabled to mine vast amounts of data. Whether it be structured or unstructured, it uncovers patterns and makes data-driven predictions.
One crucial aspect of data science is the development of machine learning models. Users leverage data science and machine learning platforms that facilitate the entire process from data integration to model management. With this single platform, data scientists, data engineers, developers, and other business stakeholders collaborate and ensure that the data is properly managed and mined for meaning.
A specific type of machine learning is referred to as deep learning, which differs from other machine learning algorithms as they use artificial neural networks to make their predictions and decisions. It does not require expert human knowledge of the data, although humans are still needed to initiate the training. Deep learning is powerful because data scientists do not have to extract features from the data as opposed to the procedure performed for training a classical machine learning model; in this case, the networks learn the features themselves. With artificial neural networks, elaborate algorithms make decisions in a similar way as the human brain. However, the decisions are made on a smaller scale because replicating the number of neural connections in the human brain is currently impossible. Uses for deep learning include image recognition (computer vision), natural language processing (NLP), and voice recognition. Image recognition algorithms allow applications to learn specific images pixel by pixel; the most common usage of an image recognition algorithm may be Facebook’s ability to recognize the faces of people when tagging them in a photo. NLP has the ability to consume human language in its natural form, which allows a machine to easily understand simple commands and speech by the user. NLP is widely used in applications like Apple’s Siri or Microsoft’s Cortana. Each of these subcategories utilizes artificial neural networks and rely on the networks’ deep layers of neural connections for an increased level of learning.
Key Benefits of Data Science and Machine Learning Platforms
Through the use of data science and machine learning platforms, data scientists are able to gain visibility into the entire data journey, from ingestion to inference. This helps them better understand what is and isn’t working, and are provided with the tools necessary to fix problems if and when they arise. With these tools, experts prepare and enrich their data, leverage machine learning libraries such as TensorFlow and PyTorch, and deploy their algorithms into production.
Share data insights — Users are enabled to share data, models, dashboards, or other related information with collaboration-based tools to foster and facilitate teamwork.
Simplify and scale data science — With easy-to-use features and drag-and-drop capabilities, many platforms are opening up these tools to a broader audience. In addition, pre-trained models and out-of-the-box pipelines tailored to specific tasks help streamline the process. These platforms easily help scale up experiments across many nodes to perform distributed training on large datasets.
Experimentation — Before a model is pushed to production, data scientists spend a significant amount of time working with the data and experimenting to find an optimal solution. Data science and machine learning platforms facilitate this experimentation through data visualization, data augmentation, and data preparation tools. Different types of layers and optimizers for deep learning is also used in experimentation, which are algorithms or methods used to change the attributes of neural networks such as weights and learning rate to reduce the losses.
Data scientists are in high demand but there is a shortage in the number of skilled professionals available. The skillset is varied and vast (for example, there is a need to understand a vast array of algorithms, advanced mathematics, programming skills, and more) and therefore such professionals are difficult to come by and command high compensation. To tackle this issue, platforms are increasingly including features which make it easier to develop AI solutions, such as drag-and-drop capabilities and prebuilt algorithms.
In addition, for data science projects to initiate, it is key that the broader business buys into these projects. As such, the more robust platforms provide resources which give nontechnical users the ability to understand the models, the data involved, and the aspects of the business which have been impacted.
Citizen data scientists — Especially with the rise of more user-friendly features, citizen data scientists who are not professionally trained but have developed data skills, are increasingly turning to data science and machine learning platforms to bring AI into their organization.
Professional data scientists — Expert data scientists take advantage of these platforms to scale data science operations across the lifecycle, simplifying the process of experimentation to deployment, speeding up data exploration and preparation as well as model development and training.
Data engineers — With robust data integration capabilities, data engineers tasked with the design, integration, and management of data use these platforms to collaborate with data scientists and other stakeholders within the organization.
Business stakeholders — Business stakeholders use these tools to gain clarity into the machine learning models and better understand how they tie in with the broader business and its operations.
While data science and machine learning platforms share a host of capabilities, one key differentiator is the manner in which they are hosted, either cloud or on-premises.
Cloud — The majority of data science and machine learning platforms offer their services in the cloud. This allows for flexible resource usage and eliminates the need to focus on managing infrastructure.
On-premises — Businesses that have major security concerns or require very low latency opt for platforms that allow for on-premises machine learning.
As mentioned, data science and machine learning platforms facilitate data science in an end-to-end fashion. As opposed to providing a point solution for AI, these platforms give users the ability to manage the entire lifecycle, from data ingestion to inference.
Data ingestion — Data ingestion features provide users with the ability to integrate and ingest data from a variety of internal or external sources. This may include enterprise applications, databases, or internet of things (IoT) devices.
Data preparation — Incomplete, dirty data is a non starter for building machine learning models. Bad training begets bad models, which in turn begets bad predictions which may be useful at best and detrimental at worst. Therefore, data preparation capabilities allow for data cleansing and data augmentation (in which related datasets are brought to bear on company data) to ensure that the data journey gets off to a good start.
Feature engineering — Feature engineering is the process of transforming raw data into features that better represent the underlying problem to the predictive models. It is a key step in building a model and results in improved model accuracy on unseen data.
Model training — Building a model requires training it by feeding it data. Training a model is the process whereby the proper values are determined for all the weights and the bias from the inputted data. Two key methods are used: supervised learning and unsupervised learning. The former is a method in which the input is labeled, whereas the latter deals with unlabeled data.
Model deployment — The deployment of machine learning models is the process for making the models available in production environments, where they provide predictions to other software systems. Methods of deployments take the form of REST APIs, GUI for on-demand analysis, and more.
Model management — The process does not end once the model is released. It is critical for businesses to monitor and manage their models in an effort to ensure that they remain accurate and updated.
Model comparison — Model comparison gives users the ability to quickly compare models to a baseline or to a previous result to determine the quality of the model built. Many of these platforms also have tools for tracking metrics, such as accuracy and loss.
Data requirements — For most AI algorithms, a great deal of data is required to make it learn the needful. Users need to train machine learning algorithms using techniques such as reinforcement learning, supervised learning, and unsupervised learning to build a truly intelligent application.
Skills shortage — There is also a shortage of people who understand how to build these algorithms and train them to perform the actions they need. The common user cannot simply fire up AI software and have it solve all their problems.
Algorithmic bias — Although the technology is efficient, it is not always effective and is marred with various types of biases in the training data such as race or gender. For example, since many facial recognition algorithms are trained on datasets with primarily white males faces, minorities are more likely to be falsely identified by the systems.