We can see a lot of hype about AI and Machine Learning, and its potential to transform businesses. More and more сompanies are adopting machine learning solutions, setting up accelerators, opening R&D centers, and investing into startups. On the other hand, there are many companies that are using old-fashioned data analytics tools and labeling them as AI. Also,there is a large number of reports with AI market estimates and forecasts. However, it’s challenging to get the right information on machine learning development that will actually work for your business.
As a company that has delivered successful Machine Learning and Data Science solutions across such industries as Healthcare, Aviation, Media and Entertainment, and Technology, we’ve decided to talk with our experts and collect top guidelines for making your machine learning development project work. Here are our 5 tips:
1. Identify if you need machine learning development
Machine learning is costly and time-consuming, and experts are thin on the the ground and high in demand.
However, if you’ve got data, you can use it to be a driver for your business. It can be something small or big depending on your business objectives, timeframe, and budget.
In fact, you should consider machine learning development if you want to improve your business in the following 3 directions:
- Making predictions and optimizing operations. For example, businesses use big data and machine learning models to predict if a customer responds to a marketing campaign.
- Summarizing and extracting information. For instance, Natural Language Processing, a machine learning technique, can be used for highlighting the most important information from legal documents which may be 10 000 pages, and an end user will have to read just the summary – 40 pages.
- Enhancing security. Many companies are now using AI and machine learning models to ensure cybersecurity. That is especially relevant for financial companies and fintechs. However, cybersecurity is currently a hot topic for all domains, especially in the light of GDPR and other regulatory and security standards.
2. Choose the most suitable type of machine learning
It is the most typical machine learning technique. Around 90 % of projects leverage this type of machine learning. You’ve got an X (input data) and an Y (a target variable, something you want to predict). In the picture, X- all the information you have on a person (e.g., age, gender, location, hobbies, etc.), and Y- whether the person is likely to click on a specific ad or not. It can be simply explained by a picture:
Its typical use cases:
- Analyzing customer data (age, gender, hobbies, etc.) and historical data to predict if a person clicks on an ad or not. The more the data, the better the model learns, that’s one of the reasons why Facebook and Google are so good at it.
- Сredit scoring is often used in alternative lending companies. If a person has got a thin credit history, a company creates their profile based on various data ( e.g personal data, payment history from utilities companies), uses data and credit scores of other people, and generates an alternative credit score based on all the aggregated and analyzed data. X here – all the data on a person who wants to take a loan, and other users’ profiles. Y- a person’s credit score, probability of their repaying the loan.
We have just X (input data) and we don’t have an Y or a target variable. You’re telling the algorithm to use the input data and group things by itself. Companies use it to find dependencies and patterns people can’t see or notice. Here is an example of how it is used for recognizing communities with similar interests:
One of its typical use cases: marketing clustering
It is a new, research-oriented, technique, and it doesn’t have many business cases. In reinforcement learning, you specify the rules, the environment and the final reward. To get the reward, algorithms try different strategies and learn from their own history and the environment. It is used to select a successive course of steps to maximize the final reward.
Its typical use cases:
- Self-driving cars
- Games (for instance, Atari)
It is a broad subtype of Machine learning. It can be supervised, unsupervised, and reinforcement. However, most often it is supervised. It attempts to mimic the activity of human neural networks. That is the ability to automatically extract features which are important for classification (for instance, for distinguishing a cat from a dog).
Deep learning had the most influence and improved results of the following techniques of Machine Learning:
- NLP (Natural Language Processing)
NLP is typically used for sentiment analysis, summarizing information (e.g, legal contracts), and as a tool for creating precisely targeted content. In our cooperation with Mercanto, a UK-based digital marketing platform, N-iX Data Scientists used Big Data technologies, NLP software, and machine learning algorithms to enable creating a product semantic profile (by analyzing a retailer’s product feed) and a customer semantic profile, matching them and delivering a highly customized email to a specific buyer.
- Speech recognition
- Computer vision and Image Recognition (now Face Recognition used by Facebook is 99% precise)
Many startups are moving towards image recognition. That means correlating image to a certain metric instead of correlating metrics to metrics.
For instance, here is a striking example of using deep learning in healthcare – you can take an image of retina, and this would let you know about risk factors, age, blood pressure, gender, and more.
3. Don’t underestimate data preprocessing and cleaning
Data preprocessing takes up around 80 %. There is no automatic way to do it. You need to merge many data tables and use different data sources. Then Data Scientists will be able to play with the models and pick which one best fits a specific business objective.
Data Cleaning is a crucial part of data preprocessing. It is analyzing the data and identifying if it is good enough to be used in a machine learning model. Its key objectives:
- Removing noisy data that may bring misleading results.
- Inputting missing data. However, it should be taken into account that the missing data may be the info itself, and it is supposed to be missing.
4. Choose between Machine learning APIs and your own development
Companies that want to adopt machine learning can go two ways: develop their own solution or use APIs and services of such companies as Amazon, Microsoft, Google, etc. The second way is the easiest one. However, there is a serious tradeoff – you sacrifice configurability and control of the system. For instance, Amazon service is fixed just on one model – logistic regression. Whereas, ability to choose the right model is one of the prerequisites for the success of a machine learning project. Thus, companies should outsource machine learning to cloud only if they’ve got very specific and simple tasks.
Furthermore, as we’ve indicated previously, 80% of machine learning is not about tuning up the models but rather preparing the data to be fit for the models, and you can’t outsource this part to Amazon, for example.
Thus, if you want something more complex, and you don’t want to sacrifice flexibility, you’d better go for your own development (either develop inhouse or outsource).
5. Find the most suitable experts for your Machine learning development
If you’ve opted for your own Machine Learning development, you need to find the most suitable experts for your project.
You need the following Machine learning specialists
- Data Scientists to deploy the algorithms. They often use off the shelf libraries.
- NLP specialists to do something sophisticated with NLP
- Data engineers to create Big Data architectures for the project, ensure system scalability, make it more like production thing
- Computer Vision engineers if you need image recognition models
- Machine Learning engineers if you have to deploy some state of the art algorithms ( e.g for reinforcement learning) and you need to to do some research specific for the project.
- Speech recognition engineers if you need some speech recognition done. However, there are not a lot of business cases.
Top languages for machine learning development
95% of all Data Scientists and Machine Learning engineers are using R and Python. Other languages they use are C and Scala.
R is famous for having a lot of libraries. It is easy to deploy and is good for making prototypes fast. It is also good for data analysis and creating state-of-the art algorithms.
Python has many libraries for Machine Learning and is good for putting into production. Especially that concerns Deep learning. also good for prototyping and making analysis , for putting into production, better for performance than R but not as good as Scala.
C – good for computer vision, good for performance.
Scala is very good for providing high performance and scalability.
Top frameworks for machine learning development
Spark – for big data processing. It has its own machine learning libraries and allows for distribution to different servers.
Hadoop – good for storing Big Data.
However, you can combine them both to make machine learning development most efficient.
Shortage of Machine learning Talent
There is a soaring demand for Machine learning specialists and Data Scientists. Based on an analysis of Monster.com entries, the average salary is $127,000 in the U.S. for Data Scientists, Senior Data Scientists, Artificial Intelligence Consultants and Machine Learning Managers. According to Monster.com, top three most in-demand skills are Machine Learning, Deep Learning and Natural Language Processing (NLP).
Companies like Amazon, Google, and Facebook are luring top Machine learning talent with high salaries.This startup is attracting top ML talent with $3 million pay packages.
As a result, many businesses face a severe problem of shortage of Machine Learning experts, and decide to outsource their development to machine learning software companies in Eastern Europe and Asia.
Machine Learning is one of technological buzzwords, and many companies want to brag using one. However, it can actually help to transform your business, reduce expenditures, and boost profits. There are key things you should know before starting a Machine Learning development project, such as choosing the right type oа Machine learning, paying special attention to data preprocessing and cleaning, and finding a machine learning software company with the most suitable expertise for a specific business case.
And the last but not least, you may hope that the data will show you what you want to see, but it may turn out to be something totally different. However, to use the insights and give your business a boost, you need to be ready to embrace them even if they are on par with your expectations.