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).

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.

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 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.

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.