Artificial intelligence is no longer something extraordinary in the world of business. If you keep an eye on IT news, you’re probably pretty aware of all the latest advancement in AI and their effect on the global market. From self-driving cars to personalized recommendation systems, machine learning innovations continue to disrupt industries with smart tech solutions.
No matter what industry you’re operating in, you can leverage the power of ML at an insanely affordable price and gain that competitive edge many businesses are striving to find. If you think machine learning software can’t be accessible, read our article and we will change your mind. Also, you’ll get an idea of the price for a simple ML solution.
In this short piece, we expanded on what machine learning is used for and provided an overview of the top machine learning applications to benefit your business.
Machine Learning Market Analysis
Machine learning continues to gather momentum among large enterprises and startups alike. In 2020, the total ML market value was estimated at $22.59 billion, and this figure is projected to increase up to $126 billion in 2025 with the compound annual growth rate of 42.08%.
Risk management, trading, automation and performance analysis remain the top ML applications businesses use to boost their results, drive sales and increase market share. With more than $28 billion of total funding allocated to AI, this technology has the potential to substitute a great deal of manual work and drive greater automation. Thus, companies can significantly increase performance and gain the competitive edge.
What do all these figures mean for your business? It is already clear that machine learning is not a flop as some experts projected. ML has become an inseparable part of industries and brought such meaningful advancements as telemedicine, self-driving cars, hyper-targeted ads and tons of other useful solutions that already benefit entrepreneurs, making their businesses more profitable and customer-centric.
What is more, machine learning is insanely accessible and affordable today. You don’t have to invest lots of funds in research or building an innovative solution. Instead, you validate your idea with PoC within 3-7 weeks and build an MVP in 6 months. With a reliable vendor, chances are you’ll get the first version of your product quickly and at an affordable price!
Find below an infographic on the major machine learning figures and trends:
Machine Learning Applications in Healthcare
Machine learning provides healthcare professionals with a variety of tools and techniques that significantly affect the health outcomes. Equipped with smart predictive algorithms, ML software can provide immediate benefits to disciplines that possess big datasets. In this perspective, machine learning models can be trained to analyze medical records to spot abnormalities or areas that require attention, thus providing new insights into patients’ well-being.
If you think machine learning is a breakthrough in healthcare, you’re probably wrong. Medical organizations have already been using ML models for a long time now to identify risks and predict disease progression.
In 2017, Google trained the software to find cell patterns in tissue to detect breast cancer at the early stage. The results are impressive — the findings have revealed 89% accuracy compared to the doctors’ 79%. Numerous healthcare organizations have been using this solution to build their own products.
Machine Learning Use Cases in Healthcare:
- improved medical diagnosis;
- accurate prediction algorithms;
- drug discovery and manufacturing;
- NLP for administrative tasks;
- visual data processing;
- patient risk identification;
- higher level of process automation.
Where to Start?
First and foremost, you should identify the gap on the market and make sure your product will meet the needs of your target audience or organization. For this purpose, it is necessary to identify the problem being solved and develop software that would help address it.
When we agree on a product, it is the time to gather more data and develop an ML model. But even if you don’t have enough data, the missing data can be crawled or sourced from other resources. But, as a rule, healthcare providers possess enough information to train the model and implement it into the product.
When you have an excellent idea for your product, you can reach out to your vendor for quality machine learning services and verify it with the help of PoC or build an MVP at a surprisingly affordable price. An MVP can be further tested and used to develop a full-fledged ML solution.
Machine Learning Applications in Retail
AI technologies, along with machine learning and deep learning, have already penetrated the world of retail, providing companies with more opportunities to market their products and achieve greater results.
First-movers, such as eBay, Amazon, and Alibaba, have long integrated AI products into their sales cycle from logistics to customer service, which now are excellent practical applications of machine learning. And their solutions are being used by millions of retailers throughout the world, which use machine learning to streamline their own operations, automate processes and improve decision-making.
The possibilities of ML in the field of retail is full of promise. The companies leverage the power of AI to build personalized recommendation systems, define their pricing strategy, source analytics and encourage visual search. Below we listed the top machine learning use cases you can apply to boost your retail business.
What is ML Capable of in Retail?
- predictive customer behavior;
- providing personalized recommendations to users;
- brand monitoring in social media;
- improving customer service with virtual assistants and chatbots;
- providing intelligent search.
Where to Start?
The first step is identifying opportunities on the market. Recommendation engines, pricing strategy tools, and visual search are among popular machine learning applications used by companies in the retail industry. To build such software, ML algorithms must be trained with information obtained from products, customers’ portfolios, or clients’ reviews.
If you do not have relevant data, you can still build a machine learning application. Much information can be sourced by crawling the web or by using special services.
Also, you might encourage your clients to share their data by providing them with a personalized experience. For instance, you might offer them valuable content if they fill out the form with information about their background, preferences, etc.
Machine Learning in Fintech
Only a few industries can boast of having as much structured data as financial and banking have, which makes it a perfect field for the application of AI and machine learning. Investment banks were the first to use AI innovation in the late 1980s. Since then, the industry has progressed to an unprecedented level, improving the experience of practitioners and their clients.
Today machine learning advancements are used to develop accurate prediction, effective asset management, and in-depth market analysis. Enterprises also apply AI to build smart chatbots that help them manage responses timely and enhance customer service. According to the studies published by Mordo Intelligence, artificial intelligence is expected to reach $7 billion and become one of the most promising techs in the financial sector.
Core Applications of ML in Fintech:
- asset management systems;
- fraud detection;
- trading algorithms;
- security software;
- stock market predictions;
Where to Start?
FinTech and machine learning is an unbeatable duo. As a rule, companies operating in this industry have enough data to build a full-fledged solution that will work properly and benefit their business. So, if you’re wondering what you need to start, we’d say it highly depends on the machine learning use case you’re going to implement.
For instance, for predictive models to forecast your customers’ behavior, you will require data from clients, such as the amount of purchases, demographic, navigation data, etc. You should also remember that retailer’s experience helps to fine tune your ML models. Thus, it would be beneficial if you could engage in the process and contribute to building your solution.
Machine Learning Applications in Marketing
Marketing and machine learning is a perfect mix when it comes to boosting sales and accelerating your business growth. When used together, these two have the potential to provide you with meaningful insights, which can be used to define your positioning or develop a strategic vision.
Being able to anticipate the customer behavior is the core factor affecting the company’s success today and artificial intelligence has become a key to optimizing marketing campaigns and making them more efficient. Here we listed core ML applications that can be integrated today to strengthen your marketing efforts.
Machine Learning Use Cases in Marketing:
- improved segmentation & targeting;
- recommendation engines;
- hyper-targeted ads;
- content generation;
- marketing attribution.
Where to Start?
Machine learning might become a perfect match for companies operating in the field of marketing. As a rule, they are pretty aware of their competitors, and in case they have a strong marketing team in place, they possess some knowledge about clients and their buying habits. The more data about your target audience you provide, the better you’ll be able to train a model.
There are already many ready-made and open-source solutions on the market, which you can freely use to boost your marketing. When integrated into your product, they can provide real value to your target audience and equip you with insights into the customers’ buying habits and future trends.
How Big Companies Contribute to the Development of ML
While you’re only searching for the ways to apply ML in business, startups and big behemoths have made their names by leveraging the power of AI to create smart solutions that disrupted their industries at some point of their development.
The good news is that most of these solutions are accessible for a broad audience. You can pay an affordable fee and use them to build your own models that could be further integrated into your product. This approach reduces the cost of ML development by several times and enables anyone from any industry to leverage the power of AI. Thus, you can develop a simple ML model starting at $4k! But keep in mind that the final cost of development is highly dependent on your needs, requirements and type of implementation.
Also, it is worth mentioning that there is an ever-growing community of AI enthusiasts, who constantly contribute with their software, products and other developments to the AI marketplace. Thus, if you need an open-source ML solution of your product, chances are you’ll find it here.
Below we listed some of the services released by large corporations that are frequently used to build and train ML models:
IBM Watson Machine Learning is the product that enables developers and data scientists to launch their data science and ML projects smoothly. Its scalable and open-source model operation software provides companies with an opportunity to harness the power of AI for their purposes.
TensorFlow is an open-source machine learning platform that helps software engineers build and train ML models. It equips practitioners with a flexible and comprehensive ecosystem of libraries, tools, and other community resources that significantly contribute to AI development.
This IT giant could not but benefit from the growing potential of ML and launched a series of AI-based products. Azure Machine Learning is one of the services catered by Microsoft that helps companies to build and deploy ML software faster.
Apart from Azure, Microsoft has put much effort into developing the AI direction. The company has recently launched a deepfake tool. Video Authenticator provides the user with the “confidence score” that the picture, video or any other media has been manipulated, helping the audience to differentiate between authentic and synthetic content.
Intel is one of the leading adopters of machine learning on the global market. The company applies ML software to decide on its pricing strategy, optimize expenses and apply large-scale data analysis. It also implemented machine learning to analyze IoT data streams.
The company also offers a set of tools and platforms to accelerate ML deployment. For instance, Intel’s Deep Learning SDK is widely used for ML model training and deployment, while AI Analytics Toolkit is applied to accelerate machine learning and data science pipelines.
Leverage the Power of ML with LITSLINK!
Meet the future today with top-notch machine learning services by LITSLINK. Regardless of your industry and type of business, we will show how you can benefit from ML solutions today. Contact us and get your project off the ground!