Machine learning is a computing technology that enables computers to explore, learn, and modify their analytical functionalities when exposed to new data sets, without being explicitly programmed. It is also used to capture data and consequently run discrete modelers to create patterns for subsequent processing, analysis, and interpretations required for real-time decision making.

The global machine learning softwares market is expected to grow from USD 1.41 Billion in 2017 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1%. The main driving factors for machine learning softwares market are proliferation in data generation and technological advancement.

In the services segment, the managed service segment is expected to grow at a higher CAGR, whereas professional service segment is expected to be a larger contributor during the forecast period. The managed service is said to be growing faster, as it helps organizations to increase efficiency and save costs for managing on-demand best machine learning softwares services. The growth of the professional services segment is mainly governed by the complexity of operations and increasing deployment of best machine learning softwares.

In the deployment mode segment, the cloud deployment mode is expected to hold the largest market share and grow at the highest CAGR during the forecast period. Flexibility, automated software updates, disaster recovery through cloud-based backup systems, increased collaboration, monitoring document version control, and data loss prevention with robust cloud storage facilities are some of the crucial benefits that have resulted in the adoption of cloud-based delivery models for best machine learning softwares and services.

In the organization size segment, the large enterprises segment is expected to have the largest market share, whereas the SMEs segment is expected to grow at the highest CAGR during the forecast period. The rapidly emerging and highly active SMEs have increased the adoption of best machine learning softwares and services globally, as a result of the growing digitization and increased cyber risks to critical business information and data. Large enterprises have been heavily adopting best machine learning softwares to extract the required information from a large amount of data and forecast the outcome of various problems.

In the verticals segment, the Banking, Financial Services, and Insurance (BFSI) vertical is expected to be the highest contributor, whereas the healthcare and life sciences vertical is projected to grow at highest CAGR during the forecast period. Both the verticals generate data in a huge amount every second, and there is accelerated demand for data management technologies such as machine learning and predictive analytics in order to extract business critical insights from this ever-increasing data. The other industry verticals, such as manufacturing, telecommunication, energy and utilities, retail, government and defense are contributing significantly to the best machine learning softwares market. These verticals are also expected to witness significant growth rates during the forecast period due to the increased concerns for managing the complex business processes with improved efficiency and lowering the overall costs.

The global machine learning softwares market has been segmented on the basis of regions into North America, Europe, Asia Pacific (APAC), Middle East and Africa (MEA), and Latin America. North America is estimated to be the largest revenue-generating region. This is mainly because, in the developed economies of the US and Canada, there is a high focus on innovations obtained from R&D. These regions have the most competitive and rapidly changing best machine learning softwares market in the world. The APAC region is expected to be the fastest-growing region in the best machine learning softwares market. The increased awareness for business productivity, supplemented with competently designed best machine learning softwares offered by vendors present in the APAC region, has led APAC to become a highly potential market.

The major issue faced by most of the organizations while incorporating best machine learning softwares in their business processes is the lack of skilled employees including analytical talent, and the demand for those who can monitor analytical content is even greater.

The major vendors that offer the best machine learning softwares across the globe are Microsoft Corporation (Washington, US), IBM Corporation (New York, US), SAP SE (Walldorf, Germany), SAS Institute Inc. (North Carolina, US), Google, Inc. (California, US), Amazon Web Services Inc. (Washington, US), Baidu, Inc. (Beijing, China), BigML, Inc. (Oregon, US), Fair Isaac Corporation (FICO) (California, US), Hewlett Packard Enterprise Development LP (HPE) (California, US), Intel Corporation (California, US), KNIME.com AG (Zurich, Switzerland), RapidMiner, Inc. (Massachusetts, US), Angoss Software Corporation (Toronto, Canada), H2O.ai (California, US), Alpine Data (California, US), Domino Data Lab, Inc. (California, US), Dataiku (Paris, France), Luminoso Technologies, Inc. (Massachusetts, US), TrademarkVision (Pennsylvania, US), Fractal Analytics Inc. (New Jersey, US), TIBCO Software Inc. (California, US), Teradata (Ohio, US), Dell Inc. (Texas, US), and Oracle Corporation (California, US).

VISIONARY LEADERS

Vendors who fall into this category in the best machine learning softwares market receive high scores for most of the evaluation criteria. They have a strong and established product portfolio and a very strong market presence. They provide mature and reputable machine learning softwares. They also have strong business strategies. Microsoft, IBM Corporation, SAP SE, SAS Institute Inc., Google Inc., and Amazon Web Services, Inc. are the vendors who fall into the visionary leaders’ category.

DYNAMIC DIFFERENTIATORS

They are established vendors in the best machine learning softwares market with very strong business strategies. However, they are low in their product portfolios. They focus on specific type of technology related to the product. Dell and Oracle Corporation are the vendors who fall into the dynamic differentiators’ category.

INNOVATORS

Innovators in the MicroQuadrant are vendors that have demonstrated substantial product innovations in the best machine learning softwares market as compared to their competitors. They have a much-focused product portfolio. However, they do not have a very strong growth strategy for their overall business. Baidu, BigML, FICO, HPE, Intel Corporation, KNIME.com AG, RapidMiner., Dataiku and Angoss Software Corporation are the vendors who fall into the innovator's category.

EMERGING COMPANIES

They are vendors with niche product offerings in the best machine learning softwares market and are starting to gain their position in the market. They do not have much strong business strategies, as compared to other established vendors. They might be new entrants in the market and require some more time to get significant traction in the market. H2O.ai, Alpine Data, Domino Data Lab, Luminoso Technologies, Skytree, Fractal Analytics, TIBCO Software, and Teradata are the vendors who fall into the emerging companies’ category.

BEST MACHINE LEARNING SOFTWARES : APPLICATIONS IN BFSI

The consistently evolving BFSI industry is data-driven, which stores huge volumes of unstructured information, most of which is not utilized properly. With the introduction of AI, best machine learning softwares, and other advanced analytics, banking institutes are now able to tap into the unstructured data that provides them with key insights about the consumers. Progress in big data and analytics has led to the emergence of new products, services, and solutions. Moreover, after the integration of best machine learning softwares with these services and solutions, the banking and financial services have become agiler and smarter. Additionally, fraud detection helps in identifying patterns in clustered data and has the ability to differentiate fraudulent activity from normal activity. The best machine learning softwares are also used to provide personalized product offering based on the patterns recognized from the user activities, which eventually leads to customer retention. The major applications of BFSI using best machine learning softwares include fraud and risk management, customer segmentation, sales and marketing campaign management, investment prediction, digital assistance, others (compliance management and credit underwriting).

BEST MACHINE LEARNING SOFTWARES : APPLICATIONS IN HEALTHCARE AND LIFE SCIENCES

Machine learning is a rapidly growing trend in the healthcare and life sciences industry, as it makes processes more efficient and forecasts future outcomes for patients. These services are used to understand customer behavior and derive in-depth insights into clinical performance. They also assist medical experts to analyze data in order to recognize trends that may lead to better diagnosis and treatment of patients. Many companies are using various machine learning softwares to cater the requirements and solve problems of the medical experts. As best machine learning softwares are capable of delivering real-time data, companies are applying machine learning with medical wearable devices, which allow the medical experts to be more accurate and predict diagnosis, owing to which they can intervene sooner. Moreover, as the received data sets are dynamic, the experts are able to detect changes more quickly. Apart from this, machine learning SMEs are using image analysis to provide faster medical diagnostic. The major applications of healthcare and life sciences sector using machine learning softwares include disease identification and diagnosis, image analytics, personalized treatment, drug discovery/manufacturing, and others (clinical trial research and epidemic outbreak prediction).

BEST MACHINE LEARNING SOFTWARES : APPLICATIONS IN RETAIL

Machine learning is being used on a large scale in the retail industry. Retail organizations have huge amounts of data, such as everyday transactions, customer information, preferences, product information, and sales. This huge data has to be processed and analyzed for key insights and to enhance and fasten the decision-making process, thus helping organizations to outline strategies for new product development and revenue optimization. Marketing campaigns and promotions are planned according to the customers and industry trends. With the help of machine learning algorithms, the retailer can predict the data to renew pricing decisions to match the interest of customers. Retailers are capable of adjusting their value chain adequately, which eventually permits them to deliver the right margins. The retail sector uses machine learning, majorly for inventory planning, recommendation engines, upsells and cross channel marketing, segmentation and targeting, and others (customer ROI and lifetime value and customization management).

BEST MACHINE LEARNING SOFTWARES : APPLICATIONS IN TELECOMMUNICATIONS

Telecommunication is one of the fastest-growing verticals and has undergone various transformations over time. The ever-changing preferences of customers and increased usage of the internet, laptops, tablets, and mobile phones mandate the service providers to understand customer preferences, behaviors, and trends in the industry. Telecom operators have to manage huge amounts of data generated from user networks and traffic data. With the help of real-time data collection and predictive analytics, the large amounts of data generated from the telecommunications vertical can be leveraged. Best machine learning softwares are used in this vertical for important purposes, such as customer churn reduction, where companies develop machine learning algorithms that predict the chance of customer churn by considering thousands of unique features and factors, including customer profiles and patterns of information. The telecommunications sector uses machine learning, mainly for customer analytics, network security, predictive maintenance, network optimization, and others (digital assistance/contact center analytics and marketing campaign analytics).

BEST MACHINE LEARNING SOFTWARES : APPLICATIONS IN GOVERNMENT AND DEFENSE

Government agencies are investing heavily into advancements in the field of AI. For instance, the U.S. Defense Department’s R&D arm called Defense Advanced Research Projects Agency (DARPA) is offering to fund projects, which will ease up the extremely complex task of building models for best machine learning softwares. With respect to cybersecurity, machine learning has an important significance. Cybersecurity has become a huge challenge in the modern world as many major corporations, individuals, and government agencies have become targets of cyber fraud. Monitoring critical networks, such as government clouds and websites for different types of intrusion detection systems and analysis of social network traffic and malware are some of the best machine learning softwares applications. Machine learning is carried out in various segments of this vertical, including threat intelligence, autonomous defense systems, and others (sustainability and operational analytics).

BEST MACHINE LEARNING SOFTWARES : APPLICATIONS IN MANUFACTURING

The manufacturing sector has started to incorporate machine learning throughout the production process. Machine learning and predictive algorithms are being used to plan machine maintenance adaptively rather than on a fixed schedule. Furthermore, quality control processes are becoming automated, with adaptive algorithms that learn to recognize correctly manufactured products and reject the defected ones. The best machine learning softwares being developed are iterative, designed to learn continually, and find optimized outcomes. The algorithms iterate in milliseconds, enabling manufacturers to seek optimized outcomes in minutes versus months. The manufacturing sector uses machine learning, majorly for predictive maintenance, revenue estimation, demand forecasting, supply chain management, and others (root cause analysis and telematics).

BEST MACHINE LEARNING SOFTWARES : APPLICATIONS IN ENERGY AND UTILITIES

The energy and utilities vertical includes energy, water, and oil and gas. It is one of the largest industries serving a huge customer base. The adoption of best machine learning softwares is rapidly changing the operational and performance model of the energy and utilities industry. The need for new data sources, new programs, and efficient management of resources as well as increasing competition from alternative energy providers are the growth drivers for this market. The smart grid backed with best machine learning capabilities has been trending in the energy and utilities sector lately. It gives access to the customer usage data that can be analyzed with the help of best machine learning softwares to secure market competitiveness, increase energy efficiency, manage risks, and lower operational costs. Analytics solutions also enable oil and gas exploration companies to analyze the data about oil and gas reserves, which helps them to increase efficiency and lower the operational risks and costs.

Frequently Asked Questions

  • How does the Machine Learning Software market looks in coming years?
    The machine learning market expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. Machine learning enabled solutions are being significantly adopted by organizations worldwide to enhance customer experience, ROI, and to gain a competitive edge in business operations. Moreover, in the coming years, applications of machine learning in various industry verticals is expected to rise exponentially. Technological advancement and proliferation in data generation are some of the major driving factors for the market.
  • Who are the target audience of the machine learning software?
    The key target audience includes: Machine learning/Artificial Intelligence (AI) solution and service providers, System integrators, Enterprise data center professionals, End-users/consumers/enterprise users, Telecommunication providers, Mobile network operators, Cloud service providers, Data center software vendors, IoT device/wearable device manufacturers, Cognitive and Artificial Intelligence (AI) technology experts/providers, Analytics service providers, Managed service providers, Consultants and Training and education service providers
  • Which are the major applications in different Industry Verticals?
    Applications in BFSI: Fraud and Risk Management, Investment Prediction, Sales and Marketing Campaign Management, Customer Segmentation, Digital Assistance, Others (compliance management and credit underwriting) Applications in Healthcare and Life Sciences: Disease Identification and Diagnosis, Image Analytics, Drug Discovery/Manufacturing, Personalized Treatment, Others (clinical trial research and epidemic outbreak prediction) Applications in Retail: Inventory Planning, Upsell and Cross Channel Marketing, Segmentation and Targeting, Recommendation engines, Others (customer ROI and lifetime value, and customization management) Applications in Telecommunication: Customer Analytics, Network Optimization, Network Security, Others (digital assistance/contact centers analytics and marketing campaign analytics) Applications in Government and Defense: Threat Intelligence, Autonomous Defense system, Others (sustainability and operational analytics) Applications in Manufacturing: Predictive Maintenance, Demand Forecasting, Revenue Estimation, Supply Chain Management, Others (root cause analysis and telematics) Applications in Energy and Utilities: Power/Energy Usage Analytics, Seismic Data Processing, Smart Grid Management, Carbon Emission, Others (customer specific pricing and renewable energy management)
  • Which is the major challenge faced by the machine learning vendors?
    The major issue faced by most of the organizations while incorporating machine learning in their business process is the lack of skilled employees including analytical talent, and the demand for those who can monitor analytical content is even greater.
  • How is the adoption trend of Machine Learning across major economies?
    The global machine learning market has been segmented on the basis of regions into North America, Europe, Asia Pacific (APAC), Middle East and Africa (MEA), and Latin America. North America is estimated to be the largest revenue-generating region. This is mainly because, in the developed economies of the US and Canada, there is a high focus on innovations obtained from R&D. These regions have the most competitive and rapidly changing global market in the world. The APAC region is expected to be the fastest-growing region in the market. The increased awareness for business productivity, supplemented with competently designed machine learning solutions offered by vendors present in the APAC region, has led APAC to become a highly potential market.
  • Which are the major factors who have boosted the growth of cloud based deployment?
    Flexibility, automated software updates, disaster recovery through cloud-based backup systems, increased collaboration, monitoring document version control, and data loss prevention with robust cloud storage facilities are some of the crucial benefits that have resulted in the adoption of cloud-based delivery models for machine learning software solutions and services.

Best Machine Learning Softwares

Comparing 38 vendors in Machine Learning Software across 114 criteria.
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IBM Watson machine learning software helps enterprises to use their data to create, train, and deploy self-learning models. It also helps users in building analytical models and neural networks. IBM data science experience is a cloud-based, social workspace that helps data professionals to consolidate, create, and collaborate across multiple open sources tools, such as R and Python.
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SAS best machine learning software offers major features like automated model tuning, powerful data manipulation and management, flexible web-based programming environment, integrated text analytics, model assessment and scoring, and modern statistical, data mining, and machine-learning techniques. It facilitates the end-to-end data mining and machine learning process with a visual and programming interface. It also boosts analytics teams of all skill levels with a simple yet powerful and automated way to tackle all tasks in the analytics life cycle.
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Microsoft Azure Machine Learning is a fully managed machine learning software service for advanced analytics in the cloud. It enables enterprises to build advanced analytic web services quickly and eradicate much of the heavy lifting associated with deploying machine learning in modern data-driven applications.
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Google Cloud AutoML Machine Learning Software includes portable models, notebook developer experience, scalable service, managed service, HyperTune, and the ability to discover and share samples. The company's Advanced Solutions Lab (ASL) supports businesses to partner with Google Cloud and apply machine learning software to tackle high-impact business challenges. The solution offers a unique opportunity for technical teams to understand from Google’s machine learning software experts.
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SAP Intelligent Robotic Process Automation controls robotic process automation, machine learning, and conversational AI in an integrated way to automate business processes with SAP Intelligent Robotic Process Automation services. The services offered help to reduce manual activities, respond to customer needs proactively, and make smarter decisions. It is capable to build intelligent bots with machine learning and conversational AI for hands-free execution and stability.

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Amazon Machine Learning Software Amazon Forecast eases the job of obtaining predictions for user’s applications using simple APIs without the need to implement custom prediction generation code or handle any infrastructure. It is considered to be highly scalable, and has the ability to generate billions of predictions on a daily basis and can serve those predictions in real-time and at a high throughput as well.
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Baidu’s open-source deep learning platform, PaddlePaddle (Parallel Distributed Deep Learning) supports neural network architectures, including convolutional neural networks and recurrent neural networks. The platform is fully scalable and is designed to enhance mathematical operations using BLAS libraries, including Intel MKL, ATLAS, OpenBLAS and cuBLAS. The company has made most of its software and systems open source and provided access to it on an “as-a-service” basis. 
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FICO has introduced new explainable artificial intelligence toolkit (xAI Toolkit) which meets customers’ increasing demand for industry-leading artificial intelligence. This solution enhances decision performance by integrating predictive and prescriptive models directly into real-time business operations to create faster, more impactful business outcomes. It also offers intuitive end-user experience and delivers direct and immediate access to data and insights dynamically.
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Oracle Machine Learning allows data scientists, citizen data scientists, and data analysts to work together to discover their data visually and develop analytical methodologies in the Autonomous Data Warehouse Cloud. Oracle Machine Learning consists of complementary components supporting scalable machine learning algorithms for in-database and big data environments, notebook technology, SQL and R APIs, and Hadoop/Spark environments.

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Dell ML comprises an enhanced solution stack along with data science and framework optimization, enabling swift setup. The solution also leverages DataRobot - an advanced enterprise automated machine learning solution that encapsulates the knowledge, experience and best practices of the world’s leading data scientists, enabling you to quickly build accurate predictive models without previous coding and ML skills.

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BigML ML software offers a selection of robustly-engineered ML algorithms proven to solve real-world problems by applying a single, standardized framework across the company. It helps avoid dependencies in disparate libraries that enhance complexity, maintenance costs, and technical debt. BigML enables unlimited predictive applications across industries including aerospace, automotive, energy, entertainment, financial services, food, healthcare, IoT, pharmaceutical, transportation, and telecommunications.

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HPE ML Ops solution extends the capabilities of the BlueData EPIC container software platform, providing data science teams with on-demand access to containerized environments for distributed AI / ML and analytics. HPE acquired BlueData in November 2018 to bolster its AI, analytics, and container offerings, and complements HPE’s Hybrid IT solutions and HPE Pointnext Services for enterprise AI deployments.
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H2O Sparkling Water permits users to combine the quick, scalable machine learning algorithms of H2O with the capabilities of Spark. Spark is an elegant and powerful general-purpose, open-source, an in-memory platform with tremendous momentum. H2O is an in-memory platform for machine learning that is reshaping how people apply math and predictive analytics to business problems. Integrating these two open-source environments provides a seamless experience for users who want to make a query using Spark SQL, feed the results into H2O to build a model and make predictions, and then use the results again in Spark.

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Intel offers a machine learning software platform based on Intel architecture, that helps organizations to transform data into actionable BI through accelerated model training, fast scoring, and highly scalable infrastructure. Intel data analytics acceleration High-Performance Computing reference architectures are optimized for machine learning software.
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KNIME Analytics Platform is the open source software for creating data science. Intuitive, open, and continuously integrating new developments, KNIME makes understanding data and designing data science workflows and reusable components accessible to everyone.

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The platform is designed to create potent machine learning models easy. It enables one to click through the interface for most use cases, whether one is an expert Data Scientist or a beginner. Dataiku makes it easy to leverage machine learning technologies and get instant visual and statistical feedback on model performance.

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RapidMiner Auto Model provides a complete solution on a unified platform that supports the entire Machine Learning workflow from data preparation through model deployment to ongoing model management. The quick-to-learn and easy-to-use workflow designer accelerates end-to-end data science for improved productivity. With the cutting-edge tools and innovative solutions that RapidMiner provides, insights can be delivered swiftly and at scale.

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Altair Knowledge Studio is an advanced data mining, machine learning software, and predictive analytics workbench, well known for industry-leading, patented Decision Trees, Strategy Trees, and its workflow and wizard-driven GUI. Altair Knowledge Studio distinguishes itself by offering a solution that can quickly and easily be adopted into an organization, providing an enhanced user experience that delivers insightful analytics swiftly.

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With organizations understanding the importance of Machine Learning technologies, it is moving out of the realm of untouchable subjects. Alpine Data Labs provides the data scientists with big data machine learning capabilities along with a high level of governance required by each and every organization today. The company is based on the philosophy of tracking movement or lack of movement of data. Alpine is known for its ability to access data sources directly.
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Fractal Analytics enables to reveal valuable insights by accurately recognizing objects in images and videos. From surveilling people in real-time at events to detecting if products are in the right place in shopping aisles, AI can drive value in many ways. This helps in creating in-depth analyses by placing image objects into relevant segments. Fractal Analytics AI-based algorithms help insurers analyze home and auto damage to create more accurate claims for customers.

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TIBCO Software is AI-powered, search-driven experience with built-in data wrangling and advanced analytics. It connects the creativity of the entire team, citizens to experts.  It is capable to combine AutoML, intuitive drag-and-drop workflows, and embedded Jupyter Notebooks that make creating and sharing reusable modules easy.

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Domino is a data science platform that allows data science teams to quickly develop and deploy models that drive ground-breaking innovation and competitive advantage. The platform automates DevOps for data science so that one can spend more time doing research and test more ideas faster. Enables automatic tracking of work for easy reproducibility, reusability, and collaboration.

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The analytics platform that boasts a built-in machine learning engine provides a wide variety of descriptive, predictive and prescriptive analytics; autonomous decision making and visualization tools. The platform is compatible with SQL, R, and Python, and can interface with visualization and BI tools like RStudio, SAS and Jupyter.

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Luminoso Score Drivers, a machine learning-powered solution helps companies intelligently automate the process of finding drivers in qualitative and quantitative feedback from their customers and employees. Score Drivers analyzes unstructured reviews and survey feedback and reveals how this unstructured data correlates with quantitative ratings.

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6Sense is a predictive software that helps companies and organizations in making predictions related to sales and marketing. It is a fast and reliable software that uses demographic, statistic, and firmographic analysis to make predictions. 6Sense is an organization’s best friend for making B2B connections because it combines time-sensitive data and uses it to make web-based predictions.

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The Alteryx machine learning has been specially built to amplify the capabilities of data scientists and analysts. The Alteryx platform provides a unique ability to prep, blend, and analyze all the available data by using a repeated workflow. The analytics can also be shared and deployed for deeper insights in just hours.
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Good Data is changing the way end-users make decisions driven by data. Useful Data offers company-wide expertise for adopting machine learning across the entire business network. From data insertion to insight delivery, the platform provides ample of flexible and modular features. The software has a lot of competitive advantages and improves efficiency across the entire network. Advanced analytics are out-of-the-box; interactive visualizations and predictive functions make the software very powerful.
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Building and deploying technology services for two decades, Kognitio is a pioneer in high performance, scalable Big Data. The machine learning software easily integrates business applications via industry-standard APIs along with any standard programming language and can interact with data from conventional as well as non-conventional data systems in real-time. Kognitio software gives the user the power of massively parallel processing and ample speed.
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With business in more than 100 countries in the world, Qlik Technologies is well known for solving the most complicated problems using data. Every user considers the machine learning platform offered by Qlik as valuable because their new machine learning capabilities include cognitive skills.

The cognitive engine gets smarter every time merging ML capabilities with human intuition. It increases the user’s power over data analysis. The platform generates and suggests the

best analytics and insights to the user for exploring the data.
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Radius Intelligence is dedicated to providing better data intelligence to its users across all B2B channels and applications. With Radius Intelligence, companies can make better use of their insights and data to ensure efficiency in their work. Radius Intelligence was built on the most comprehensive and accurate data ecosystem, the Network of Record. They have a pioneering AI which allows them to help businesses with accurate insights.
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