Machine learning as a Service (MLaaS) in any setting helps extract meaningful insight from large volumes of raw data to identify trends and patterns that otherwise are not apparent. It is most commonly used to solve complex business problems that rely heavily on data. Machine learning applications in business use algorithms to learn from the data with every iteration. i.e., every time there is any data input, the system will automatically compare with previous data entries. This comparison will allow the system to draw valuable insight and find hidden links between seemingly random information. The algorithm will do all of this without being explicitly programmed to do so, and therein lies the advantage.
Why You Need Machine Learning Application in Businesses?
Applications of machine learning in business primarily include improving business scalability and developing business operations for companies all over the world and in any industry. Artificial intelligence tools and various machine learning algorithms have gained considerable fame in the business analytics community. Factors such as increasing volumes, easy availability of data, more affordable and quicker processing, and practical data storage techniques have led to a significant increase in the use of machine learning. Therefore, businesses can now profit by understanding how companies can make use of machine learning and implement the same in their processes.
Machine learning in business applications helps in extracting meaningful information from massive raw data sets. If implemented correctly, Machine Learning can serve as a solution to a variety of business complexities problems and predict complex customer behaviors. Applications of machine learning in business are practically limitless but can (and do) include the following:
Customer Lifetime Value Prediction
Machine Learning and data mining can help companies to predict customer behavior and purchase patterns. The algorithms assist in sending the best possible offers to individual customers, based on their browsing and purchase histories.
Increasing Customer Satisfaction
Machine Learning can help in improving customer loyalty and also ensure the superior customer experience. Previous call records are analyzed to determine customer behavior and predict requirements, thereby assigning the most suitable customer service executive.
The machine learning algorithms use customer’s purchase history and match it with the extensive product inventory to identify hidden patterns and group together similar products. Product recommendations are then made using this information.
Eliminates Manual Data Entry
Machine learning programs discover data rather than requiring manual entry.
Spam filters in e-mail applications can now create new rules by using neural networks to identify spam and phishing messages.
Predictive maintenance helps in reducing the risks associated with unexpected failures and eliminates unnecessary expenses using historical data, workflow visualization tools, flexible analysis environment, and the feedback loop.
Machine Learning in finance helps in portfolio management, algorithmic trading, loan underwriting, and fraud detection. However, future applications of Machine Learning in finance will include Chatbots and other conversational interfaces for security, customer service, and sentiment analysis.
Image recognition involves machine learning through the use of data mining, computer vision, pattern recognition, and database knowledge discovery to produce numeric and symbolic information from images and other multi-dimensional data.
Machine learning in healthcare can potentially make near perfect diagnosis because of the objectivity in data analysis. The systems can also predict readmissions, recommend the best medicines, and identify patients at high risk, mostly based on patient records.
Improving Cyber Security
Machine learning can be used to enhance cybersecurity, as well. The algorithms can quickly analyze the most likely vulnerabilities and potential malware and spyware applications based on user data.
7 Best Machine Learning Applications in Businesses
Hiring data scientists or ML experts is never easy nor cheap, but the rise of MLaaS suggests that once the service becomes the mainstream, efficiency in delivery will improve. As data gathering becomes cheaper and processing power grows more powerful, miracles of data science become possible for everyone.
MLaaS is a good halfway point for companies that want to experience the benefits of machine learning without diving in themselves. Having an established provider handle it for you in the initial stages of implementation is an excellent way to minimize any transitional issues. Not to mention you have grater surety the process is going according to plan.
Here we will discuss some of the best machine learning service providers and application availability, and then you can see which one works for you.
AWS Machine Learning
Amazon Web Services have widely revolutionized the SaaS field and become a dominant player in the MLaaS market. Amazon Machine Learning offers a service that is incredibly popular for its useful guidance in creating an ML model without needing to delve into the complex world of the algorithms themselves. Some of the more prominent applications offered by AWS include Amazon EC2, Amazon S3, Amazon Aurora, and Amazon DynamoDB.
Amazon Machine Learning is highly automated and therefore is the best choice for beginners. It can automatically receive data from multiple input types such as Amazon RDS, Amazon Redshift and CSV files, etc. Categorical and numerical does not need to identify manually as the algorithms will do that for you and then determine the appropriate method of data preprocessing.
Amazon ML pricing is on a pay-as-you-go model. You first have a flat $0.42/hour fee for data analysis and model building, with separate charges for every addon. i.e., batch predictions ($0.10 per 1,000 predictions, to the nearest 1,000) and real-time predictions ($0.0001 per prediction, to the nearest penny). If you want Data storage as well, billing is separate.
Microsoft Azure Machine Learning Studio
Microsoft Azure has a lot of services to offer, but their machine learning options are particularly useful. Not only is their machine learning scalable, but it is also suitable for both beginners as well as experts in AI. It hosts a wide range of tools that lean towards flexibility for out-of-the-box algorithms.
That said, ML Studio is still a little challenging to get used to since the operations have to be completed manually. This includes everything from data exploration, preprocessing, choosing appropriate methods, and validating modeling results. Although the browser-based environment is a simple, visual drag-and-drop mechanism, so no coding needed there.
ML Studio’s more popular option is the free workspace. It only requires a Microsoft account and includes unrestricted access to over 10GB of storage, R and Python support as well as predictive web services. The standard enterprise-grade workspace is a little pricier, at $9.90/month plus an Azure subscription. At the same time, though, it offers much more in terms of support and services.
IBM Watson Machine Learning (WML)
WML is a public service provider that runs on IBM’s Bluemix. It helps data scientists and developers work together to fast-track the process of moving to deployment and integrate AI into their applications. By streamlining, accelerating, and leading AI deployments, Watson Machine Learning helps enable organizations to harness machine learning, deep learning, and decision optimization to deliver business value.
Similar to the Google Prediction API, Watson Studio offers fully automated data processing and model building. It is an interface that needs very little training to start processing data and therefore reducing the time for preparing the models and eventual deployment. It can be said that WML’s USP is its cloud computing expertise and the distribution of resources. It offers a public cloud, private cloud as well as a dedicated accelerated environment.
Pricing for IBM WML ranges from the “basic” free version to the “professional” service charged at USD 1,000 per instance and USD 0.4 per 1,000 predictions and capacity unit hour.
Folio3 machine learning applications for business bring continuous, uninterrupted intelligence to your enterprise processes. By radically improving productivity and decreasing costs, you can boost your ROI.
The company offers dynamic deployment strategies that analyze your ML or DL needs, skilled resources to determine the ML/DL models, integrate and then design solutions based around offsite ML. The Folio3 AI automates your processes and prioritizes the more routine aspects of data management through advanced algorithms.
Folio3 is an expert in adapting machine learning application in business intelligence as well as understanding customer and therefore, encourages regular touchpoints with customers and relevant stakeholders. Furthermore, tailored AI and ML algorithms. These algorithms can be integrated with both image and video analytics and coupled with emerging technologies such as augmented virtual reality. As such, the company has extensive experience working in multiple industries with featured applications such as Converse Smartly (Natural Language Processing), Breast Cancer HER2 Subtype Identification (Computer Vision), Road Traffic Analysis (Deep Learning), ATM Cash Forecasting (Deep Learning), Automated Authentication for Drive Throughs (Deep Learning), Cognitive services and Facial Recognition System (Histogram of Oriented Gradients or “HOG”).
As far as pricing is concerned, given the high-level os customisability, the cost of service depends on your exact requirements. It is best to contact the company to see how best Folio3 can help you in the deployment of advanced machine learning systems.
Iflexion is a software development company that distributes AI and machine learning applications to more than 30 countries around the globe. For almost 20 years, Iflexion has cooperated with a high number of clients, including big companies such as Philips, Toyota, and Adidas.
Iflexion develops AI solutions-focused on natural language processing, marketing personalization, and predictive analytics (to name a few) built around some of the most efficient machine learning and deep learning algorithms available.
Google Cloud Machine Learning Engine
Google offers a top-class MLaaS platform as well. Across all their Cloud AI services, they offer a Machine Learning Engine, as well as services for natural language processing and APIs for video, and image recognition, speech, etc.
However, it is the Google Cloud Machine Learning Engine that is most important. It offers a simple alternative for building ML models for any data type or size. The Google ML Engine is highly flexible and based on the ever-popular TensorFlow project. Of course, this integrated platform works with any Google service, but it is aimed primarily at tasks requiring a deep neural network.
As for the cost, if you’re interested in trying out Cloud ML Engine, you can sign up for a free trial with no initial charge, and it comes with a $300 credit. However, a subscription to the Google Cloud Platform isn’t cheap.
BigML is a little set apart from the rest. It is the only MLaaS provider on this list that isn’t backed by a major tech giant. Nonetheless, it is a worthy candidate for this list mostly because BigML is compatible with almost any platform and allows data imports from all significant sources such as AWS, MS Azure, Google Storage, Google Drive, Dropbox, etc.
Although BigML’s focus on machine learning means that it has far more web UI integrated features available than some of its counterparts. The platform is relatively intuitive and therefore easy to use, with flexible deployment for any enterprise option. BigML also boasts an extensive gallery of free datasets and models for experimentation.
As far as pricing is concerned, BigML as many options. If you’re smart with your dataset sizes, you can perform unlimited tasks for datasets up to 16MB for free. Students and educators have discounts, as well. There’s even a pay-as-you-go option. For more private deployments, Big ML offers opportunities for companies with more stringent data security or privacy requirements.
Difference Between Supervised And Unsupervised Machine Learning Applications for Business
Supervised Machine Learning
Supervised learning is the act of Data mining by inferring meaning from labeled training data. The data consists of a set of examples intended to train the algorithms used by the AI. In supervised learning, each case consists of a data pair where one element is an input object (typically a vector) and the second element is the desired output value (also known as the supervisory signal). In this way, a supervised learning algorithm analyzes the data and produces an inferred function (in other words, a connection). The learned relationship between two elements of data can then be used for mapping new examples. In the best-case scenario, the training data will allow for the algorithm to accurately determine the class labels for data where the relationship is undefined. Machine learning application in business intelligence is most efficiently achieved through supervised machine learning.
Unsupervised Machine Learning
In the case of unsupervised learning, there is so established precedent for the AI algorithms to develop relationships between unlabelled data. Meaning that there is significant room for error since there is no error or reward signal for the machine to evaluate a potential data set correctly.
There are many MlaaS providers out there that efficiently implement machine learning applications in business some of the more notable ones we have mentioned here, but the fact of the matter is that it all depends on what you need and how you need it. If the solutions offered solves the problem, then that is the provider for you. The best course of action is to explore your options first thoroughly before making a decision. AI deployment, as mentioned at the beginning of this article, is not always cheap and easy. That is why it pays off to make the right decision the first time.
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