Want to start your venture based on Machine Learning?
Keywords: Machine Learning, Artificial Intelligence, Venture, Customers, Data Scientists.
Before starting let’s understand what is Machine Learning and how it works in real time?
Machine Learning is a technology which is an outcome of Artificial Intelligence. In addition, machine learning is a form of AI that enables a system to learn from data rather than programming explicitly. Machine learning uses a variety of algorithms that iteratively learn from data to improve, describe data and predict outcomes. A machine learning model is the output generated when you train your machine learning algorithm with data. After training, when you provide model with an input, it will generate an output. For an instance, a predictive algorithm will generate a predictive model. Then, when you provide the predictive model with data, you will receive a prediction based on that particular data that trained the model.
How Machine Learning is an iterative process?
Machine learning enable models to train on datasets before being deployed. Some machine learning models are online and continuously adapt as new data is ingested. On the other hand, other models, called as offline machine learning models are derived from machine learning algorithms, but, once deployed, it cannot be changed. This iterative process of online models leads to an improvement in the types of associations that are made between data elements. Because of this iterative process, there can always be seen a fluctuation in accuracy of the models that are trained on the existing data. This variation in accuracy is always seen in online models rather than offline. So, online machine learning algorithms continuously refine the models by consistently processing new data in near real time and training the system to adapt to changing patterns and associations in the data.
Role of Big Data in context with Machine Learning:
If you want to start your own startup based on Machine learning, then remember that the most important part would be the quality and quantity of data. However, quality of the data matters more than quantity. Because, in machine learning based ventures, the accuracy is given the first priority, that is, how well the model is trained and the model is trained properly only when the quality of data is good. So, machine learning requires the right set of data that can be applied to the learning process. An organization does not have to have big data in order to use machine learning techniques. As I mentioned above, the quality of your data matters the most rather than the quantity of data for any organization. With big data, it is now possible to make the data virtual so as to store the data on premises or cloud and that also in an efficient way.
This combination of technology advances can help industries address significant business problems. Armed with big data technologies and machine learning models, organizations are able to anticipate the future.
Understand and Trust your Data:
It is not simply enough to ingest vast amount of data. Providing accurate machine learning models require that the source data be accurate and meaningful, which is again nothing but the quality of your data. In addition, these data sources are meaningful when combined with each other so that the model is accurate and trusted. It is must to understand the origin of your data sources and whether they make sense when they are combined.
Besides, it is also important to perform data cleaning and handling of missing data. Here, cleaning of data means to perform data preprocessing (transforming the raw data to a understandable format). Data refinement provides the foundation for building analytical models that deliver results you can trust. The process of data refinement will help to ensure that your data is timely, clean and well understood.
The power of Machine Learning:
Companies are experiencing a progression in analytics at maturity levels ranging from descriptive to predictive analytics to machine learning and cognitive computing. These are able to describe how various actions and events will impact the outcome.
Data Scientists and Business Analysts have been constrained to make predictions based on analytical models that are based on past data.Companies need a way to build predictive models that can react and change when there are changes to the business environment. For example, online machine learning models.
Role of Statistics and Data Mining with Machine Learning:
The disciplines of statistics, data mining and machine learning all have a role in understanding data, describing the characteristics of data set and finding the relationships and patterns in that data to build model.
Many of the widely used data mining and machine learning algorithms are rooted in classical statistical analysis.However, it cannot be expected to get good results by focusing on the statistics alone without considering the business side.
Statistics is the science of analyzing the data. To deal with the problem of numeric data, it is always recommended to clear all the basics of statistics and probability. Statistics helps in understanding the nature of the variables. Machine Learning models leverage the statistical algorithms and apply them to predict analytics. On the other hand, data mining, which is based on the principles of statistics is the process of exploring and analyzing large amount of data to discover patterns in that data. Data mining is used to solve a range of business problems such as market basket analysis (apriori algorithm), fraud detection, customer churn prediction/analysis. Industries use data mining tools on large volume of structured data such as customer relationship management databases. Data mining tools are intended to support the human decision-making process.
Role of neural networks and deep learning:
Deep learning is a specific method of machine learning that incorporates neural networks in successive layers in order to learn from data in an iterative manner. It is more helpful when you deal with an unstructured data. Neural networks and deep learning are often used in image recognition, speech and computer vision applications.
There are many areas where deep learning will have an impact on business. For example, voice recognition will have applications in everything from automobiles to customer management. In the IoT manufacturing applications, deep learning can be used to predict when a machine will malfunction.
Applying Machine Learning and Strategy about how to implement in a business:
With the machine learning, there is always the opportunity to use the data generated by your business to anticipate business change and plan for the future. No business is static, so there is always an opportunity to learn new things about the data and to prepare for the future.
So before applying strategy, there is always a need to understand about the business problem that is to be solved. So to solve the business problem(s) it is also important to be in touch with your customers. As I mentioned above that the none of the business is immutable, much of the knowledge about the customers is hidden inside structured, unstructured and semi-structured data. To deal with this, the selection of appropriate algorithms is must as it will let us know what kind of information is hidden and what information the business is lacking to collect.
One of the traps that company leadership falls into is its assumptions and biases. Too often company management looks at the data presented and interprets the results. Because of it, there are chances that business might condone the important information that can be gained from the data. However, those important information is generally available in semi-structured or unstructured data. So, without understanding the data it is likely possible that we might misinterpret the results.
For the business to effectively use machine learning to support business strategy, there is a need of these statistical methods to find patterns and anomalies in these datasets. With the best data available and in the right volume and the best level of cleanliness, it is possible to create a model by using the most appropriate algorithm based on the business problem being addressed.
More data makes planning more accurate:
What difference can machine learning make in business strategy? ? Take the example of a business that executes a traditional data analysis of customer satisfaction. In analyzing the data, it becomes clear that some anomalies in the data exist. Because of the data set being used, the analyst throws out the data that doesn’t conform, assuming that this data is not accurate. However, if more data did exist, it may become clear that those anomalies that were assumed to be errors are actually an indication of a change in customer buying patterns or customer satisfaction. As more data is added into a model, trained, and analyzed with the most appropriate machine learning algorithms, it becomes increasingly clear that there are changes that will directly impact the future of the business. It can be concluded from the above example that, as more data is added to the model, the system learns and gains more insight and becomes more accurate in predicting the future.
Applying machine learning to business needs:
Business leaders are beginning to appreciate that many things happen within their organizations and with their industries that can’t be understood through a query. Some examples are as follows that might help to understand and let you know what kind of problems can be solved with the data and hidden patterns.
→ Customer Churn detection and prediction.
→ Fraud Detection.
→ Cancer Cells detection.
→ Preventing Accidents.
→ Image recognition, etc.
Impact of Machine Learning on applications:
How does a business execute on the goal? As with everything in complex application development and deployment, it requires a planning process for understanding the business problem that needs to be solved and collecting the right data sources.
How does this approach to creating applications have an impact on the business? When building applications from logic, we assume that business processes will remain constant. However, the reality is that processes change. If we can begin by modeling data, it will lead us to changes in process and logic. Therefore, machine learning can make the creation of applications much more dynamic and effective.
The Role Of Algorithms:
Machine learning algorithms are different from other algorithms. The more data is added to the algorithm, the more gentle the algorithm becomes. And as more sophisticated the algorithm becomes, the more accurate results the business will get and as more accurate results the company will get, the more profit the business will have.
There are regression algorithms that can quantify the strength of correlation between variables in a dateset. Besides, these algorithms are also very helpful in predicting the future based on past data. However, without understanding the context of data, regression analysis may lead to inappropriate outcomes.
There are also chances of over-fitting (If our model does much better on the training set than on the test set, then we’re likely over-fitting), which is one of the most trickiest obstacles in applied machine learning. So, to prevent the model from over-fit, we need to perform regularization (Regularization simplifies overly complex models that are prone to be over-fit).
Identify relevant data:
Understanding data is critical to success. If we create a model based on faulty data, our predictions will obviously be inaccurate. In addition, we need to think about what data should be included in our machine learning application. Business decisions need to be made based on constantly changing data from a variety of sources.
Life cycle of Machine Learning:
As mentioned earlier, that machine learning is an iterative process, it can also be seen here from the above flowchart that after selecting the features that are to be included in data-set, there are chances that we might need to perform EDA again if the need arises.
To achieve the goal, proper plan and road map is needed. Without planning, achieving something is quite difficult. Same is in the business. A proper plan and team is needed who will help us achieve our goal. If the business is related to machine learning then it is not like we need only data scientists. It is not like that only a team of data scientists will help us achieve our goals. For that we need to think about how we can get started so we can gain insights from the data generated by the company.If we approach the adoption of machine learning techniques in a systematic way, we will be in a good position to anticipate changes in our market and changes in the way customers expect to do business with us.
Let’s understand how ML can help:
Before picking a target project, we need to begin by helping business management understand what machine learning is all about.
While we will certainly have experts, such as data scientists, it is important that business analysts and business strategists understand how machine learning can be applied to the business to solve some very complex problems. The abundance and variety of data can provide the business with a valuable weapon to help our business grow and change.
The focus should be on business problem. For example, we should be aware about what business problem are we trying to solve ? Where are the hidden data resources that we can take advantage of to better understand opportunities and threats? How to prepare to get data in order?
For many organizations, being able to understand the hidden patterns within their data offers a huge potential advantage. Some of the important data may be found in social media sources. Data may also be found in unstructured data sources such as documents related to new research findings. Data is also found in semi-structured sources such as sensor and IoT-based systems.
ML requires Collaboration:
The appropriate level of collaboration between business units, corporate leadership, and data scientists can create value that leads to true differentiation and meaningful change.
Start Implementing Pilot Project:
Here, the meaning of pilot project means the subset of a larger project on which we are working on. Once, the pilot project is implemented successfully, we might get insights of our whole project including the study of data. We can always learn a surfeit of things when we implement a pilot project. For example, we can know what exactly our customers want, what kind of data we need to execute our project, which features may be useful in our project, etc.
As we add new data sources, the changes in customer requirements become more defined. These answers will then feed into your business planning and can enable our company to move more quickly to try new approaches that can positively impact revenue.
How to determine best learning model?
The selected algorithm has to be generalized enough that it can be accurate with new data. If the algorithm is too tightly tied to an existing set of data, this type of over-fitting will cause problems in the future. Therefore, when we select an algorithm, we need to be sure that the data set being used is a representative sample of our information.
Our pilot will be much more successful if our data set is a representative sample of the aspect of the business that we are focused on.
An increased number of packaging of machine learning algorithms exist through APIs, including Spark MLlib, H2O, and TensorFlow. One of the most important skills for developers is to understand which algorithm is the best fit for the problem. For example, a linear regression model fits the problem when you’re trying to understand how two points are related. On the other hand, if you are dealing with understanding the content of images, you may want to explore TensorFlow. To conclude, the selection of algorithms depends on what type of problem we are trying to solve and based on that the algorithms are selected and trained.
Building your Team:
Here are some points on how to build a team
→ Build a team with a mix of skills that is, a balance team of technical members and business members.
→ Pick a lead data scientist who is well versed in both programming and architectural principles.
→ Bring in a business analyst who knows industry as well as company.
→ Make sure a member of the team can tell a story from the data. A story telling of data in Data Science is a must.
→ Select representative business leaders who understand what they need to gain from the project.
→ Add subject matter experts to the team who really understand the details of how processes work and the nature of the data.
→ Find consultants when needed who can help train the team on new languages or new tools that support the project goals.
→ Bring in specialists for specific technical areas where you don’t have in-house talent. For example, a team of cyber security.
Proactively responding to IT issues:
Applying machine learning algorithms to this complex IT operations data allows organizations to proactively respond to potential IT issues. IT operations have always been complicated because of the array of different network devices, servers, applications, storage systems, endpoints, and so on. Each system has its unique ways of managing its components. As new versions of software are implemented, configuration updates may be necessary to keep the system running as expected. This is the normal way that systems need to interact in order to maintain a steady state. Often a single mistake in one area can lead to a massive outage, which can be difficult to determine the original cause of a problem — despite the fact that there is significant instrumentation within the data center.
Predictions on the future of Machine Learning:
→ Machine learning models embedded in nearly every application and on a variety of devices, including mobile devices and IoT hubs. Two examples where ML is already embedded are retail websites and online advertisements.
→ Trained data as a service will become a prerequisite. For example, a company may provide hundreds of thousands of pre-labeled medical images to help customers create an application that can help screen medical images and spot potential health issues.
→ More machine learning models available for use, specially for offline models.
→ Growing popularity of Machine Learning as a Service (MLaaS).
→ NLP will mature enough to be the norm for users to communicate with systems via a written or spoken interface.
→ Technical users will be able to focus on more challenging work rather than simply automating repetitive tasks.
→ Many organizations can procure hardware that is powerful enough to quickly process machine learning algorithms.
→ By using automation, data scientists will be able to quickly focus on just one or two algorithms rather than manually testing many more.
→ Why a machine learning model recommends a specific outcome will be essential in order to trust the results.
→ We will begin to see machine learning as an end-to-end process from a development and operations perspective.
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So, this is it from my side. Any feedback or suggestion is acceptable. You can also share some tips related to ML or some of your experience with me.
Reference:
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