Machine learning can make an enterprise an industry leader by streamlining the processes and increasing returns. ML is right for every organization, and here’s why.
Have you heard of the terms artificial intelligence and machine learning? Or, the correct question would be how many of you have not heard of them. Isn’t it? The interest in AI and ML has been steadily growing over the years. Many organizations have already started investing in ML technologies to improve their business operations.
However, it is vital to understand what ML is, why your business needs machine learning, and what kind of technologies you should adopt. To make things easy for you, we’ll explain the various facets of ML in this blog.
In today’s world, there is no shortage of data. The world has generated around 2.5 quintillion data bytes every day in 2020. Every user created 1.7 MB of data per second. Data is available in real-time for processing and analysis. This helps organizations around the world to derive accurate insights and use them to make decisions.
But how is this data processed? Manually collecting, cleaning, and analyzing big data is not really possible and takes too much time and effort. Artificial intelligence tools and machine learning algorithms help replace the manual processing of data.
Machine learning models can process data in real-time, identify patterns, and share valuable insights that help enterprises streamline their day-to-day business operations. In recent years, machine learning adoption rates have improved in all parts of the world. The machine learning global adoption rate is 45%, with another 21% exploring ML methods to get started soon.
There are several practical applications and benefits of using machine learning in the business. It has become necessary to invest in artificial intelligence and machine learning tools to revamp the business process and compete in the market. Both SMEs and large-scale enterprises need to understand what machine learning is and how it can change their business for the better.
But first, let’s start with the basics. In the first section, we’ll read more about machine learning, the definition, model, purpose, and more.
Machine learning is an AI application that helps systems read and understand vast amounts of data. Machine learning is used to develop programs that can process and analyze data to deliver predictions and insights.
The main aim of using machine learning is to empower a computer to learn and understand data in various forms. In short, machine learning helps computers process data the way a human mind does.
Enterprises develop machine learning models customized to work with the type of data collected to make the business successful.
Is machine learning AI or is machine learning the same as Artificial Intelligence?
Machine learning is a subset of AI. The concept of machine learning is to empower machines to learn and adapt through self-training. It deals with the statistical methods and algorithms that teach a computer to understand and process data. This means that while machine learning can be called AI, the reverse is not true. It is an application of artificial intelligence where a machine is trained to think and make decisions like a human.
AI has more components than machine learning. It is a broader subject. Artificial intelligence aims to make computers smart enough to solve complex problems without human intervention. In other words, Artificial intelligence creates intelligent machines, while machine learning trains these machines to become efficient and accurate.
The machine learning algorithms are widely classified into supervised and unsupervised learning, semi-supervised learning, and reinforcement machine learning.
This model uses the learning of the past and implements it on new data through labeled examples. It is used to predict future outcomes and trends. The input is mapped to output based on a given example of input-output pair.
A regression model is where the output is continuous and is further subcategorized into the following-
This is where you find a line that best fits that data when you draw a graph with the X and Y-axis. Multiple Linear Regression (where a plane of best fit is determined) and Polynomial Regression (where a curve of best fit is found) are an extension of this model.
We hear about neural networks more often than other models. It is a series of algorithms to recognize the several relationships and underlying connections between datasets. Neural networks are vital for deep learning. Each node is connected to the other to send data across the network. The network is trained using huge data sets and becomes accurate over time. Google’s search algorithm is the best example of a neural network. This model has been developed to resemble the operations and thought processes of the human mind.
This is a popular model where every question/ assumption is a node. The probable answers to each question will lead to more questions (nodes), and you finally have the answers (called leaves). This method considers all possible conditions before coming up with the final answer. The more nodes you have, the better will be your decision. However, this method is not always accurate due to the various factors involved.
This is a collection of decision trees. However, random subsets of variables are selected at each step of the decision tree. Then the mean of predictions for each tree is selected. The final decision is selected depending on how many predictions are similar (same). This is a majority-based model that tries to eliminate the risk of individual error.
A classification model is where the output is not continuous but discrete. It uses logistic regression the probability of outcomes in two. The output value should either be 0 or 1. Decision trees, random forests, and neural networks are a part of the classification model as well. The only difference is the change in output.
The information used to train the algorithm is neither labeled nor classified into neat categories. This helps in identifying the hidden patterns in datasets and finding a correlation between events. The model is not used to derive a concrete output. Rather, it is used to understand data and get inferences from it. Unsupervised learning is sub-categorized as-
It is a model where data points are grouped based on different parameters. There are various clustering methods (k-means, mean shift, density-based, hierarchical), and each model uses a different technique. However, the end result is the same. Clustering is used to segment customers.
This is a model where the number of random variables is reduced using a set of defined principles. This allows creating a simpler machine learning model as using fewer input dimensions will directly lead to lesser parameters for analysis.
As the term suggests, this model deals with detecting unusual data points in data sets and highlighting them. It is also known as outlier detection where rare and suspicious events/ items are identified based on how different they are from the majority of data.
This model helps identify items in data sets that frequently occur and have a connection/ common point. The algorithm uses if/then statements to find and highlight events/ items that seem to have some sort of connection with each other. The events might look independent but form a pattern when seen from a larger perspective.
Is machine learning part of data science?
Yes, machine learning is a part of data science. The term data science includes all aspects of machine learning, while machine learning includes only a section of data science. Data science also deals with the use of data mining and visualization.
As the term suggests, this combines the previous two learning models and uses labeled and unlabeled data for training. This model is used to increase the accuracy of the algorithm. Semi-supervised learning algorithms rely on different types of assumptions such as continuity assumption, cluster assumption, manifold assumption, generative models, etc.
This model is used in finance and eCommerce industries to detect fraudulent transactions despite the availability of limited examples. It helps prevent and reduce losses due to fake and fraudulent transactions.
Small datasets are used to train algorithms to apply labels to datasets, which are then implemented on larger sets. Data labeling algorithm helps employees to tag and label datasets as images, videos, audios, text, etc., for easy identification
Algorithms are taught to automatically translate data/ text from one language to another even if there is no dictionary of words. Understanding the words, tone, and intent of the content is essential to get the right translation. MT is considered a sub-field of computational linguistics that uses machine learning software to understand data and its context.
Reinforced learning is one of the basic ML paradigms where the algorithm is trained to make a sequence of decisions. It uses the trial and error method to identify the best way to increase the performance of the program or to find a solution for a problem. Feedback is used to make the necessary corrections and get closer to the ideal condition.
RL models help enterprises in carefully allotting limited resources to achieve the defined goal. Resource management helps HR departments utilize input data in a heuristic manner. It is also used in laboratories during testing programs.
Robots are taught to mimic human actions and perform day-to-day tasks using reinforcement learning. Generalization is a concept of deep learning that defines how well the model can adapt to new and unseen data. This generalization is essential for robots to complete their tasks in the enterprise. RL algorithms help use past data to train robots to understand even new data and perform actions accordingly.
RL algorithms are categorized as-
Q-Learning is a model-free and off-policy RL algorithm. Off-policy is also known as a greedy policy.
This is similar to Q-Learning, except that SARSA is an on-policy algorithm.
One drawback of Q-learning is that it cannot deal with generalization. If it comes across unseen data, the Q-Learning algorithm will not know what to do. DQN solves this issue by using a neural network.
Is machine learning a technology?
Machine learning is a collection of technologies that helps achieve the goal of simplifying complex tasks and automating processes. It helps in evaluating data to understand the hidden patterns that help in making better decisions.
Machine learning has maths, statistics, and programming language as its primary elements. When designing a machine learning model, you will need to use basic mathematics and statistical concepts to ensure success.
Linear algebra and calculus are two fundamental math concepts used in machine learning. Probability, descriptive statistics, and inferential statistics are other necessary concepts. Mathematics is equally important for data science as well. It helps in developing the best algorithm to deliver accurate predictions and insights.
Is machine learning a good career?
Absolutely! Machine learning is a good career choice in current times. Indeed published a report in 2019 where machine learning was listed among the top jobs in terms of demand, growth, and salary. There has been an increase of 344% in the postings for ML jobs between 2015 and 2018. Considering the trends, the demand for machine learning engineers is only going to increase during the coming years. There are various career opportunities to grow and become successful if you choose machine learning. However, you’ll need to have a strong command of the concepts and be passionate about your job.
Is machine learning hard?
Yes and no. Machine learning seems hard because it involves too many elements like mathematics, statistics and analysis, programming language, frameworks, and various ML models to choose from. Apart from the above, access to development and debugging tools is another issue (though there are several open-source toolkits).
That said, when you join a course to learn machine learning, you’ll find things making sense and understand the concepts as you spend more time on them. The basic courses don’t make you an expert and only provide a chance to get an entry-level job.
The main purpose of using machine learning is to identify the hidden pattern in data and make accurate predictions based on these patterns. Processing a huge volume of data to derive meaningful and valuable insights, recognize trends, and discover patterns are the reasons why different industries use machine learning.
The actual purpose of machine learning changes based on the industry and the business that adopts it. Nevertheless, machine learning is an effective method to analyze data, automate recurring processes, and make better decisions in less time. Enterprises can become industry leaders by adopting machine learning better than their competitors.
We’ll read more about the role of machine learning in businesses and other industries in the coming sections.
There are various methods to develop a machine learning model for an enterprise. The following five are the major approaches used by machine learning engineers and service providers.
ML toolkits are open source and easily available for developers to create a machine learning model. You can either design a generic one or a specific model. PyTorch (developed by Facebook), TensorFlow (developed by Google), Keras (developed by Google and is a higher-level toolkit), and Scikit-learn (native to python) are popular and most preferred machine learning toolkits for Python
Auto ML and ML Ops are two ways to use machine learning platforms to access the necessary tools to develop a machine learning model for your enterprise.
The term notebook refers to the extensive document-style environment where data science models are built and tested in an enterprise.
If you have already invested in business intelligence and data analytics, you can use the same systems to develop the ML models.
Machine Learning as a Service is a cloud-based approach to adopting ML in businesses. You can buy/ lease a cloud-based toolkit or the DS Notebooks to develop an ML model exclusively for your business needs.
Let us look at how machine learning can help your business grow and gain a competitive advantage over others.
Machine learning derives meaningful insights from raw data to find solutions for complex data problems. It helps SMEs become more flexible and scalable by improving business operations and grabbing market opportunities. Machine learning can help your business in the following ways-
For machine learning to help a business become better in all aspects, the company needs to make the necessary changes to its processes, systems, and work culture.
What are Quantum machine learning services?
Quantum machine learning is a combination of quantum computing and traditional machine learning algorithms. The aim is to use machine learning techniques to develop new quantum algorithms and reduce quantum error codes. Machine learning consulting companies offer services to help enterprises use ML to work effectively on quantum computers.
One reason companies fail to make the most of machine learning is that they are not ready to adopt the models in their business.
Companies will find it beneficial to hire machine learning engineers from India to bridge the gap and use machine learning in daily operations to increase overall productivity. DataToBiz is a well-known data science company that provides models of offshore machine learning teams for ML services to organizations around the world.
Is machine learning data science or Is machine learning and data science the same?
Machine learning and data science are not the same. Machine learning is only one aspect of data science. However, machine learning fits within the concepts of data science. A data science team needs a machine learning engineer to work on the algorithms and models. However, data science is a much wider concept and includes data visualization and presentation. Machine learning is used to help machines learn based on historical and real-time data to become better at processing big data. You can say, while data science deals with data processing as a whole, machine learning is limited to algorithms and statistics used to understand data.
The short answer is yes. Machine learning is really useful not just in one industry but in many of them. Each industry and each company uses different machine learning models that suit the nature of their work.
Machine learning makes it easy to simplify complicated problems and work with a large volume of data in less time. It is a great solution to complete repetitive tasks without manual intervention.
The US Postal Service, Netflix, banks and financial institutions, research centers and healthcare organizations, etc., use machine learning models in various methods to discover patterns, find solutions, predict future trends, and make decisions.
Let’s look at some crucial ways in which machine learning helps various industries.
Why, indeed, does your business need machine learning? We’ve already seen how ML models can help change your enterprise for the better. But you must be wondering if there’s more, isn’t it? Let’s find the answers to the question then.
There are many more reasons to invest in machine learning for your business. ML can improve your business processes and increase returns.
Are machine learning and AI the same?
AI is a broader concept that uses machine learning, deep learning, natural language processing, and other techniques to find solutions to complex data problems. In short, machine learning is a subset of AI where machines are provided with data and asked to make sense of it.
Let’s look at some crucial ways in which machine learning helps various industries.
The following examples show us how machine learning is a versatile concept that a company can adopt from any sector.
Machine learning helps government agencies to improve cybersecurity, cyber intelligence, speed up logistics, and reduce failure rates. This is done by improving operational efficiency.
From studying sleep patterns using real-time data from sensors to detecting cancerous cells in the human body and analyzing images, machine learning in the medical industry is helping doctors provide better treatment to patients. It is helping to reduce the mortality rate.
Using robots to man heavy machinery and predictive maintenance of machinery are two areas where the manufacturing industry uses machine learning models.
Be it banks or insurance companies, financial institutions now rely on machine learning models to detect fraudulent transactions. It helps create a safer environment for online transactions. Financial advisers are using ML to identify the ideal investment plans.
Machine learning is being extensively used in marketing sales to improve customer satisfaction. The ML models help you to understand what a customer expects from your business.
The future of retail is said to lie with eCommerce. And a better way to attract and retain customers is to provide the products based on their purchase history, searches, likes, and dislikes. Machine learning helps you achieve this.
Machine learning and artificial intelligence have successfully streamlined supply chains and logistics for several companies. Whether it is moving goods from the warehouse to the final destination or choosing the shortest route to minimize the time taken for transportation, ML is now a crucial part of logistics management.
AI and ML help in finding new energy sources, predicting sensor failures, analyzing mineral deposits, and so on. But most importantly, it is used to create a safer environment for workers.
ML has been transforming the pharma industry by becoming an integral part of the research and development of new medicines, manufacturing automation, and supply chain management.
AI has been making headway in the education sector by-
Will machine learning change your organization?
Yes, machine learning will change your organization for the better, but only if you invest in adopting the correct ML model. Machine learning can make your business more efficient, from segmenting customers to detecting fraudulent transactions and streamlining logistics.
How can you implement machine learning in the real world? Let’s look at some examples below.
Machine learning and natural language processing are used together to translate speech to text without losing the meaning of the content. Voice searches and home assistants are two examples most are familiar with. Voice assistants are also used in enterprises to help employees improve the quality of their work and finish it in less time.
Whether it is analyzing medical images to help detect life-threatening diseases or whether it is tagging people through facial recognition on social media, image recognition is a well-used machine learning model in the real world.
Machine learning can extract structured data from unstructured data. It can automate the process of annotating datasets that are used for predictive analytics.
Machine learning is used with computer vision to study the videos of surveillance cameras and identify the shelves in a retail store with the most and least traffic. This helps retailers understand which products are more profitable and which ones need to be promoted more to increase sales.
What are Cloud-based quantum machine learning services?
Cloud-based quantum machine learning services help companies test their quantum algorithms in real quantum computers located on the cloud. Setting up quantum circuits is expensive and complicated. Cloud services are a cost-effective option to test and deploy quantum algorithms in an enterprise without the risk of loss.
Machine learning is still very much in demand in the market. The future belongs to companies that use machine learning and artificial intelligence as an intrinsic part of their business. The demand for machine learning engineers has gone up during the last few years.
According to FinancesOnline, machine learning applications receive the highest AI funding in the global market. The global ML market is set to expand at 42.08% CAGR between 2018 and 2024. There has been around a 75% increase in demand for AI and ML in the last four years.
With several small, medium, and large-scale enterprises adopting ML across the globe, we can safely say that machine learning is still in demand and will continue to be for a considerable time.
Let’s look at how machine learning applications are helping businesses achieve their goals and have become an undeniable asset for several growing companies.
Ultimately, the success of your company depends on how well you adopt the machine learning models and use the insights for making decisions. The focus here is to move ML into the core business processes.
Using cloud-based machine learning services will save you money when implementing ML in your business. Machine learning is expensive. But it is made affordable by using cheap cloud storage and carefully selecting the ML tools. We see this often in healthcare, retail, and logistics.
While the predictive analysis doesn’t always require machine learning, combining these two will give you a definite advantage in the competitive market. Leveraging outside information in real-time will help in using market volatility to your advantage.
Patterns are important in data sets, be it in retail, banking, or healthcare. Machine learning can process historical and real-time data in less time to identify patterns that are not easily traced through manual analysis.
Manufacturing and transportation industries need to optimize their resources and amplify the output. How else can you find the best possible route to deliver the goods or know the exact time to move the stock from the warehouse to the distribution centers? Machine learning lets you stay one step ahead of others.
Setting a data-driven machine learning system takes time, effort, money, and skills. But it revamps your business processes and makes you one of the best.
Hiring the services of a machine learning consulting company will ensure that you invest in ML models that are a correct fit for your business. You can minimize risk, optimize resources, and increase the return on investment by using the offshore ML services offered by DataToBiz. The company believes in providing high-impact AI and data science solutions for a myriad of business problems and strategy improvements.
AI and ML-powered chatbots keep customers engaged and collect data that helps in improving the products and services of the company. It also takes the load of customer service teams and lets them focus on core problems.
Understanding the customers’ thought processes is the key to retain them. Machine learning analyzes data from various sources to anticipate customers’ next moves and prevent them from going to the competitors.
Managing invoices and payments is a time-consuming job in any enterprise. Machine learning helps in automating the process after understanding and recognizing when and how the payments are sent/ received.
How do you ensure that you hired the right person for the job? Are you sure that there is no personal bias? Machine learning provides a solution. The ML model can screen and shortlist candidates based on pre-defined criteria.
Companies these days don’t have the advantage of spending years researching a product. You need to be fast and efficient and come up with products/ services that your customers want.
Machine learning is solution-oriented. Even when the parameters are not fully measurable (customer satisfaction, brand exposure, etc.,) ML algorithms can process raw data to identify patterns and trends that help arrive at the final solution.
Machine learning also works with big data. A great volume of data that cannot be processed using traditional systems is known as big data. Machine learning makes it an easy job to process vast amounts of data in less time.
Artificial intelligence is not restricted to any industry. It is a vast and versatile concept that helps align the vision and mission of the business with the processes followed by the company and the work culture in the enterprise.
How do you make sure your enterprise is using AI to its full potential? There are so many ways to use AI and ML in a business that it can be overwhelming during the initial days. The following are some areas to start implementing AI and ML in the business–
Customers love personalized products and services. Whether it is product suggestions (Amazon) or movie suggestions (Netflix), show customers that you care about their likes and dislikes.
Enterprises these days have access to so much data that data storage and management have been a problem. The solution lies in AI and cloud storage services. Segregate data, store only what you need, and delete the rest.
Machine learning algorithms can now train themselves and improve the accuracy of predictions. A Google-owned company, DeepMind has developed a system to train neural networks using simulations. This is said to bring machine learning closer to more people.
As technology advanced, hackers became more efficient in attacking entities and individuals to steal data and money. AI and ML play a central role in detecting cyber threats and neutralizing them before hackers gain access to your systems.
There are many areas to cover in risk management. Detecting fraud, averting a crisis, making the workplace safer, preventing loss of money, managing liabilities, etc., comes under risk management and can be effectively managed using machine learning models developed for the purpose.
For an enterprise to become a fully AI-powered business, it is essential to understand how machines and employees (humans) work and interact with each other. It is crucial for the management and employees to adapt to the changing work environment and adjust their procedures accordingly.
Since every organization has its own way of adopting AI, it becomes even more necessary to have a flexible and fluid working process. Reaching AI’s full potential in an enterprise is possible when the management has a complete view of what the business needs and how it can be achieved. Using the services of AL and ML companies like DataToBiz makes the process smoother and increases the success rate.
What makes machine learning so important for businesses, be it an SME or a large enterprise?
Without machine learning, you cannot process big data in real-time and make decisions based on facts and insights. The insights can be plugged into the core business processes to empower employees at different levels to make decisions related to their job.
Machine learning improves the performance of the systems and employees by aligning them with the business mission. It helps achieve your goals faster.
From increasing employee productivity to shortening the product development cycle, ML helps increase the overall productivity of your business.
An SME can compete with a large enterprise only when it has a proper business model that sets it apart from the rest. ML is the ticket to unlock your potential and develop a business/ service model for your company.
ML helps optimize the use of resources, thereby reducing the cost of investment required for daily operations and production.
Python is the best programming language to implement machine learning. It is easy to learn and is being taught as a part of the introductory computer science course. It can be used to build a full application, unlike R, which deals with databases. It is also easy to access Python libraries and ask for community help if you get stuck.
1. Start by installing Python and SciPy platforms. You need to install scipy, matplotlib, numpy, sklearn, and pandas libraries.
2. Then load the datasets in Python by importing modules, functions, and objects. Summarize the dataset by getting its dimensions, looking at the actual data, statistical summary, and class distribution.
3. EDA and data cleaning are essential and performed before data analysis. Exploratory Data Analysis is the first step followed by data cleaning though some follow the reverse order. EDA and data cleaning include:
4. Move on to data visualization using different plots. Evaluate the algorithms by creating a validation dataset, testing, and building the model.
5. Choose the base model and make predictions. Evaluate these predictions and check the classification report.
The above steps are just an outline of how to implement machine learning in Python. You need to learn the programming language before you start working on ML models using Python.
What are Cloud-based quantum machine learning services?
Cloud-based quantum machine learning services help companies test their quantum algorithms in real quantum computers located on the cloud. Setting up quantum circuits is expensive and complicated. Cloud services are a cost-effective option to test and deploy quantum algorithms in an enterprise without the risk of loss.
We have seen several benefits of using machine learning in a business. Let’s look at a few more to understand why experts say that AI and ML are our future.
Machine learning helps you to continuously improve your business and become better, just like the way the algorithms learn on their own to become more accurate.
When you automate repetitive tasks, you are reducing the extent of human involvement in the process. This saves time for employees and allows them to focus on the core job. It also reduces human error, which might otherwise go unnoticed until it’s too late.
Machine learning models can work with structured and unstructured data. Big data is usually in different formats. ML algorithms have no trouble processing all varieties of data at once.
Machine learning models are developed for specific purposes. Not just that, enterprises can get the models customized to suit their exact requirements or build a new model from scratch.
Machine learning empowers your business processes and employees to be ready to tackle any challenge and grab new opportunities before your competitors can. You will always be prepared to explore the market and expand your business.
What are open source quantum machine learning services?
Open source quantum machine learning tools and services help in prototyping and deploying quantum ML models quickly. The necessary tools and framework required to bring quantum computing and machine learning together are provided to enterprises. Using open-source resources will help enterprises save money when working on the quantum ML models.
We have talked about the importance and use of machine learning in various industries for business purposes. Let’s take a brief look at the impact of machine learning on society.
Machine learning and artificial intelligence will only move ahead and become an unavoidable part of our lives very soon.
Artificial intelligence and machine learning are here to stay. The world is changing, and the only way to handle the change is to adapt to it.
Does this mean that the hype around machine learning is valid? Is machine learning worth the investment and effort?
Most experts in the field say yes. Machine learning is indeed worth it. However, your success rate depends on how well you understand machine learning and use it to overcome the challenges and achieve your business goals. Using a machine learning model with little or no idea about what it does for your business can cause more harm than benefit.
Do your research, talk to experts, and understand exactly how and where machine learning can be incorporated into your business and what kind of results it will deliver. The ultimate decision should depend on the extent of positive changes machine learning can bring and the subsequent return on investment you’ll get from it.
They have the experience and agility to understand what’s possible and deliver to our expectations.
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