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.
Products classified in the overall Data Science and Machine Learning Platforms category are similar in many regards and help companies of all sizes solve their business problems. However, enterprise business features, pricing, setup, and installation differ from businesses of other sizes, which is why we match buyers to the right Enterprise Business Data Science and Machine Learning Platforms to fit their needs. Compare product ratings based on reviews from enterprise users or connect with one of G2's buying advisors to find the right solutions within the Enterprise Business Data Science and Machine Learning Platforms category.
In addition to qualifying for inclusion in the Data Science and Machine Learning Platforms category, to qualify for inclusion in the Enterprise Business Data Science and Machine Learning Platforms category, a product must have at least 10 reviews left by a reviewer from an enterprise business.
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 is the launchpad for automation breakthroughs. Be it your personal growth, achieving transformative digital outcomes, or rapid innovation, the results are unparalleled. The unique innovation that converges analytics, data science and process automation into one easy-to-use platform, empowers everyone and every organization to make business-altering breakthroughs the new status quo. Visit Alteryx.com for more information, and to start your free trial.
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.
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
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.
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
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.
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
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. Companies are 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.
Not all data science and machine learning platforms are designed equal. These tools all allow developers and data scientists to build, train, and deploy machine learning models. However, they differ in terms of the data types supported, as well as the method and manner of deployment.
Cloud data science and machine learning platforms
With the ability to store data in remote servers and easily access them, businesses can focus less on building infrastructure and more on their data, both in terms of how to derive insight from it, as well as to ensure its quality. These platforms afford them the ability to both train and deploy the models in the cloud. This also helps when these models are being built into various applications, as it provides easier access to change and tweak the models which have been deployed.
On-premises data science and machine learning platforms
Cloud is not always the answer, as it is not always a viable solution. Not all data experts have the luxury of working in the cloud for a number of reasons, including data security and issues related to latency. In cases such as health care, strict regulations such as HIPAA, require that data be secure. Therefore, on-premises solutions can be vital for some professionals, such as those in the healthcare industry and government sector, where privacy compliance is particularly strict and sometimes vital.
Some platforms allow for spinning up algorithms on the edge, which consists of a mesh network of data centers that process and store data locally prior to being sent to a centralized storage center or cloud. Edge computing optimizes cloud computing systems to avoid disruptions or slowing in the sending and receiving of data.
The following are some core features within data science and machine learning platforms that can help users in preparing data, as well as training, managing, and deploying models.
Data preparation: 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.
Dirty data (i.e., data that is incomplete, inaccurate, or incoherent) is a nonstarter for building machine learning models. Bad AI training begets bad models, which in turn begets bad predictions that 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.
Model training: 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.
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 used for this purpose are supervised learning and unsupervised learning. The former is a method in which the input is labeled, whereas the latter deals with unlabeled data.
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 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.
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.
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, 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 are 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 that 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. The more robust platforms provide resources that give nontechnical users the ability to understand the models, the data involved, and the aspects of the business which have been impacted.
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.
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.
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.
Alternatives to data science and machine learning platforms can replace this type of software, either partially or completely:
AI & machine learning operationalization software: Depending on the use case, businesses might consider AI & machine learning operationalization software. This software does not provide a platform for the full end-to-end development of machine learning models but can provide more robust features around operationalizing these algorithms. This includes monitoring the health, performance, and accuracy of models.
Machine learning software: Data science and machine learning platforms are great for the full-scale development of models, whether that be for computer vision, natural language processing (NLP), and more. However, in some cases, businesses may want a solution that is more readily available off the shelf, which they can use in a plug-and-play fashion. In such a case, they can consider machine learning software, which will involve less set up time, as well as development costs.
There are many different types of machine learning algorithms that perform a variety of tasks and functions. These algorithms may consist of more specific machine learning algorithms, such as association rule learning, Bayesian networks, clustering, decision tree learning, genetic algorithms, learning classifier systems, and support vector machines, among others. This helps organizations looking for point solutions.
Related solutions that can be used together with data science and machine learning platforms include:
Data preparation software: Data preparation software helps companies with their data management. These solutions allow users to discover, combine, clean, and enrich data for simple analysis. Although data science and machine learning platforms offer data preparation features, businesses might opt for a dedicated preparation tool.
Data warehouse software: Most companies have a large number of disparate data sources and to best integrate all their data, they implement a data warehouse. Data warehouses house data from multiple databases and business applications which allows business intelligence and analytics tools to pull all company data from a single repository. This organization is critical to the quality of the data that is ingested by data science and machine learning platforms.
Data labeling software: To achieve supervised learning off the ground, it is key to have labeled data. Putting in place a systematic, sustained labeling effort can be aided by data labeling software, which provides a toolset for businesses to turn unlabeled data into labeled data and build corresponding AI algorithms.
Natural language processing (NLP) software: NLP allows applications to interact with human language using a deep learning algorithm. NLP algorithms input language and give a variety of outputs based on the learned task. NLP algorithms provide voice recognition and natural language generation (NLG), which converts data into understandable human language. Some examples of NLP uses include chatbots, translation applications, and social media monitoring tools that scan social media networks for mentions.
Software solutions can come with their own set of challenges.
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.
Skill 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 biases. For example, since many facial recognition algorithms are trained on datasets with primarily white males faces, others are more likely to be falsely identified by the systems.
The implementation of AI can have a positive impact on businesses across a host of different industries. Here are a handful of examples:
Financial services: The use of AI in financial services is prolific, with banks using it for everything from developing credit score algorithms to analyzing earnings documents in order to spot trends. With data science and machine learning platforms, data science teams can build models with company data and deploy them to both internal and external applications.
Healthcare: Within healthcare, businesses can use these platforms to better understand patient populations, such as predicting in-patient visits and developing systems that can match people with relevant clinical trials. In addition, as the process of drug discovery is particularly costly and takes a significant amount of time, healthcare organizations are using data science to speed up the process, using data from past trials, research papers, and more.
Retail: In retail, especially e-commerce, personalization rules supreme. The top retailers are leveraging these platforms to provide customers with highly personalized experiences, based on factors such as previous behavior and location. With machine learning in place, these businesses can display highly relevant material and catch the attention of potential customers.
If a company is just starting out and looking to purchase their first data science and machine learning platform, or wherever a business is in its buying process, g2.com can help select the best option.
The first step in the buying process must involve a careful look at one’s company data. As a fundamental part of the data science journey involves data engineering (i.e., data collection and analysis), businesses must ensure that their data quality is high and the platform in question can adequately handle their data, both in terms of format, as well as volume. If the company has amassed a lot of data, the need is to look for a solution that can grow with the organization. Users should think about the pain points and jot them down; these should be used to help create a checklist of criteria. Additionally, the buyer must determine the number of employees who will need to use this software, as this drives the number of licenses they are likely to buy.
Taking a holistic overview of the business and identifying pain points can help the team springboard into creating a checklist of criteria. The checklist serves as a detailed guide that includes both necessary and nice-to-have features including budget, features, number of users, integrations, security requirements, cloud or on-premises solutions, and more.
Depending on the scope of the deployment, it might be helpful to produce an RFI, a one-page list with a few bullet points describing what is needed from a data science platform.
Create a long list
From meeting the business functionality needs to implementation, vendor evaluations are an essential part of the software buying process. For ease of comparison after all demos are complete, it helps to prepare a consistent list of questions regarding specific needs and concerns to ask each vendor.
Create a short list
From the long list of vendors, it is helpful to narrow down the list of vendors and come up with a shorter list of contenders, preferably no more than three to five. With this list in hand, businesses can produce a matrix to compare the features and pricing of the various solutions.
To ensure the comparison is thoroughgoing, the user should demo each solution on the short list with the same use case and datasets. This will allow the business to evaluate like for like and see how each vendor stacks up against the competition.
Choose a selection team
Before getting started, it's crucial to create a winning team that will work together throughout the entire process, from identifying pain points to implementation. The software selection team should consist of members of the organization who have the right interest, skills, and time to participate in this process. A good starting point is to aim for three to five people who fill roles such as the main decision maker, project manager, process owner, system owner, or staffing subject matter expert, as well as a technical lead, IT administrator, or security administrator. In smaller companies, the vendor selection team may be smaller, with fewer participants multitasking and taking on more responsibilities.
Just because something is written on a company’s pricing page, does not mean it is fixed (although some companies will not budge). It is imperative to open up a conversation regarding pricing and licensing. For example, the vendor may be willing to give a discount for multi-year contracts or for recommending the product to others.
After this stage, and before going all in, it is recommended to roll out a test run or pilot program to test adoption with a small sample size of users. If the tool is well used and well received, the buyer can be confident that the selection was correct. If not, it might be time to go back to the drawing board.
As mentioned above, data science and machine learning platforms come as both on-premises and cloud solutions. Pricing between the two might differ, with the former often coming with more upfront costs related to setting up the infrastructure.
As with any software, these platforms are frequently available in different tiers, with the more entry-level solutions costing less than the enterprise-scale ones. The former will frequently not have as many features and may have caps on usage. Vendors may have tiered pricing, in which the price is tailored to the users’ company size, the number of users, or both. This pricing strategy may come with some degree of support, which might be unlimited or capped at a certain number of hours per billing cycle.
Once set up, they do not often require significant maintenance costs, especially if deployed in the cloud. As these platforms often come with many additional features, businesses looking to maximize the value of their software can contract third-party consultants to help them derive insights from their data and get the most out of the software.
Businesses decide to deploy data science and machine learning platforms with the goal of deriving some degree of ROI. As they are looking to recoup their losses that they spent on the software, it is critical to understand the costs associated with it. As mentioned above, these platforms typically are billed per user, which is sometimes tiered depending on the company size. More users will typically translate into more licenses, which means more money.
Users must consider how much is spent and compare that to what is gained, both in terms of efficiency as well as revenue. Therefore, businesses can compare processes between pre- and post-deployment of the software to better understand how processes have been improved and how much time has been saved. They can even produce a case study (either for internal or external purposes) to demonstrate the gains they have seen from their use of the platform.
How are Data Science and Machine Learning Platforms Implemented?
Implementation differs drastically depending on the complexity and scale of the data. In organizations with vast amounts of data in disparate sources (e.g., applications, databases, etc.), it is often wise to utilize an external party, whether that be an implementation specialist from the vendor or a third-party consultancy. With vast experience under their belts, they can help businesses understand how to connect and consolidate their data sources and how to use the software efficiently and effectively.
Who is Responsible for Data Science and Machine Learning Platforms Implementation?
It may require a lot of people, or many teams, to properly deploy a data science platform, including data engineers, data scientists, and software engineers. This is because, as mentioned, data can cut across teams and functions. As a result, it is rare that one person or even one team has a full understanding of all of a company’s data assets. With a cross-functional team in place, a business can begin to piece together their data and begin the journey of data science, starting with proper data preparation and management.
What Does the Implementation Process Look Like for Data Science and Machine Learning Platforms?
In terms of implementation, it is typical for the deployment of the platform to begin in a limited fashion and subsequently rolled out in a broader fashion. For example, a retail brand might decide to A/B test their use of a personalization algorithm for a limited number of visitors to their site, to better understand how it is performing. If the deployment is successful, the data science team can present their findings to their leadership team (which might be the CTO, depending on the structure of the business).
If the deployment was not successful, the team can go back to the drawing board, attempting to figure out what went wrong. This will involve examining the training data, as well as the algorithms used. If they try again, yet nothing seems to be successful (i.e., the outcome is faulty or there is no improvement in regards to predictions), the business might need to go back to basics and review their data as a whole.
When Should You Implement Data Science and Machine Learning Platforms?
As previously mentioned, data engineering, which involves preparing and gathering data, is a fundamental feature of data science projects. Therefore, businesses must set as their top priority getting their data in order, ensuring that there are no duplicate records or misaligned fields. Although this sounds basic, it is anything but. Faulty data as an input will result in faulty data as an output.
AutoML helps automate many tasks needed to develop AI and machine learning applications. Uses include automatic data preparation, automated feature engineering, providing explainability for models, and more.
Machine and deep learning functionality is getting increasingly embedded in nearly all types of software, irrespective of whether the user is aware of it or not. The use of embedded AI inside software like CRM, marketing automation, and analytics solutions is allowing to streamline processes, automate certain tasks, and gain a competitive edge with predictive capabilities. Embedded AI may gradually pick up in the coming years and it may do so in the way cloud deployment and mobile capabilities have over the past decade or so. Eventually, vendors may not need to highlight their product benefits from machine learning as it may just be assumed and expected.
Machine learning as a service (MLaaS)
The software environment has moved to a more granular, microservices structure, particularly for development operations needs. Additionally, the boom of public cloud infrastructure services has allowed large companies to offer development and infrastructure services to other businesses with a pay-as-you-use model. AI software is no different, as the same companies are offering MLaaS to other businesses.
Developers easily take advantage of these prebuilt algorithms and solutions by feeding them their own data to gain insights from. Using systems built by enterprise companies helps small businesses save time, resources, and money by eliminating the need to hire skilled machine learning developers. MLaaS will grow further as businesses continue to rely on these microservices and as the need for AI increases.
When it comes to machine learning algorithms, especially deep learning, it may be particularly difficult to explain how they arrived at certain conclusions. Explainable AI, also known as XAI, is the process whereby the decision-making process of algorithms is made transparent and understandable to humans. Transparency is the most prevalent principle in the current AI ethics literature, and hence explainability, a subset of transparency becomes crucial. Data science and machine learning platforms are increasingly including tools for explainability, which helps users build explainability into their models and help them meet data explainability requirements in legislation such as the European Union's privacy law, the GDPR.