Is Your Enterprise Well-Prepared for AI?

With every passing day, businesses are seeking to automate their processes. Organizations have implemented automated processes in customer service, production, marketing, data analytics, and healthcare to name a few. At the core of automation sits machine learning. Machine learning is a subset of AI, based on the concept that systems/machines can learn from data and identify patterns to make decisions with minimal human intervention.

Machine learning is helping organizations to enhance their growth and optimize their business processes such as manufacturing, customer-service, supply chain management, and marketing. Moreover, it improves employee engagement and enhances customer satisfaction.

In this blog, we will look at the various types of machine learning methods and explore why businesses need machine learning. Subsequently, we will look at how Alibaba Cloud Machine Learning Platform for AI is an ideal machine learning tool for businesses.

Classification of Machine Learning Methods

In general, machine learning algorithms can be classified as either "supervised" or "unsupervised" learning. The term "supervise" here does not refer to human intervention but rather the existence of a training dataset with the "correct" values for the machine to learn from. We can further categorize machine learning methods based on its purpose and the system it is applied to. They are as follows:

Supervised Learning: In this method, algorithms train with the help of labeled examples, such as an input where you know the desired output. For instance, an equipment could have data points labeled either “F” (failed) or “P” (pass). The learning algorithm gathers a set of inputs and corresponding correct outputs. The algorithm learns by comparing its actual output with the right outputs to find errors. It then modifies the model accordingly.

Unsupervised Learning: One can use this method against data that lacks historical labels. The user does not provide the system with the "correct answer." The algorithm is expected to figure out what is being represented. The aim is to explore the data and find a structure within. Unsupervised learning works well on transactional data. It is popularly used for marketing campaigns as it helps marketers segment their customers based on similar traits.

Semi-supervised Learning: Its application and purpose are the same as those of supervised learning. However, it uses both unlabeled and labeled data for training. It typically includes a small amount of labeled data with a significant amount of unlabeled data. Organizations might choose to go with this method as acquiring and analyzing labeled data involves substantial costs. Semi-supervised learning facilitates prediction, regression, and classification.

Reinforcement Learning: Although similar to supervised learning, reinforcement learning is focused on maximizing rewards rather than just getting "correct" answers to a problem. This method helps to incorporate machine learning into gaming, robotics, and navigation. Algorithms discover the next point of action through trial and error and decide which actions yield the greatest rewards. Reinforcement learning comprises of three primary components:

Agent: It refers to the decision maker or the learner
Environment: It includes everything the agent interacts with
Actions: It involves all that the agent can do

Moving on, let us look at how machine learning is helping businesses across industries.

Why Businesses Can't Afford to Miss the Machine Learning Bus

Machine learning not only helps organizations to streamline their processes but also improve production efficiency with reduced costs and enhanced precision. Below are some machine learning advantages that make it imperative for businesses to adopt it.

Smoother Supply Chain Management: Organizations that are into mass production, transportation, and retail often face issues with their supply chain management. Adding to the vows of businesses is the interdependency of supply chain management on every connecting factor. A delay in one element results in the overall postponement of the entire process. Machine learning helps organizations with the contextual analysis of logistics data to predict and mitigate supply chain risks.
Personalizing Customer Service: One can trace the key to a satisfied customer to an efficient customer care service. Thanks to machine learning, organizations have introduced chatbots which are replacing humans. Chatbots combine historical customer service data, NLP, and algorithms to interact in almost human form in a much faster manner. You can know more about chatbots in our blog Introduction to Alibaba Cloud Intelligent Service Robot.
Shortlisting Relevant Candidates: In a survey conducted by ideal, more than 50% of recruiters said that shortlisting the correct candidate was the most challenging part of their job as recruiters. The modern-day software uses machine learning to swiftly scan through thousands of job applications and shortlist relevant candidates. It can also automatically flag biased language in job descriptions and detect suitable candidates overlooked by recruiters because they didn't fit traditional expectations.
Fraud Detection: In its 2018 Global Economic Crime and Fraud Survey, PwC observed that 49% of global organizations had experienced economic crime and fraud. With the help of building models based on social network information, historical transactions, and other external sources of data, machine learning algorithms can use pattern recognition to spot exceptions, anomalies, and outliers. Organizations can apply machine learning algorithmic security to a wide range of situations.
Measuring Brand Exposure: Measuring a brand's exposure helps organizations assess the brand awareness of a product or service. However, this is easier said than done. While doing so, organizations often make common mistakes. When businesses automate the program to measure a brand exposure, the results are faster, relevant, and more accurate. Using advanced image recognition, users can track the brand logo positions that appear in video footage of a sporting event, such as a football game.

In the following section, let us look at Alibaba Cloud's machine learning, its features and the industries that can use it to optimize their overall processes.

Alibaba Cloud Machine Learning Platform for Artificial Intelligence (PAI)

In March 2017, at its annual Computing Conference Shenzhen Summit, Alibaba Cloud announced that it had updated its machine learning platform PAI to PAI 2.0. During the announcement, Dr. Jingren Zhou, the Chief Scientist of Alibaba Cloud, mentioned that PAI is aimed at making Alibaba Cloud's AI program an effective tool for assisting businesses and people to resolve practical and real-time issues. As China's first machine learning platform, PAI promises to enhance better health care services and support the implementation of smart manufacturing.

Alibaba Cloud Machine Learning Platform is a one-stop high-end machine learning platform that comprises of data preprocessing, conventional machine learning algorithms, feature engineering, model evaluation, deep learning framework, and forecasting service. With the help of its GPU and CPU clusters, PAI can provide users with efficient computing for PB-level data.

Four Key Features of Alibaba Cloud PAI

Large-scale Computing Power: Alibaba Cloud PAI is powered by Alibaba Cloud's large-scale computing engine and Apsara. It provides immensely large-scale distributed computing capabilities allowing users to handle petabyte-level computing tasks daily.
Innovative and Diversified Algorithms: PAI includes more than 100 sets of algorithms that allow regression, neural network, data preprocessing, classification, predictions, statistical analysis, evaluations, feature engineering, and deep-learning architecture.
Deep-learning Architecture: Alibaba Cloud PAI's entire computing architecture is optimized for multiple deep learning frameworks. It also supports a one-click function to deploy APIs, solving the issue of service integration and modeling.
User-friendly Interface: Like all Alibaba Cloud services, PAI's data visualization capabilities allow developers to swiftly include components into workflows using convenient drag and drop features. This immensely helps in enhancing the efficiency of model building and debugging.

For a much better understanding of Alibaba Cloud PAI's capabilities, let's look at its application scenarios in the following section.

Application Scenarios of Alibaba Cloud's PAI

Unstructured Data Processing:For organizations, processing data is always a challenge, processing unstructured data adds one more level in that challenge. Alibaba Cloud PAI can process unstructured data to make image identification and speech recognition possible using collaborative filtering algorithm and classification algorithm.
Text Analysis:News broadcasting networks are constantly seeking to provide their target audience with real-time news relevant to their needs. They can make this possible thanks to PAI's capability to implement news classification and text keyword extraction through algorithms such as TF-IDF, LDA, TF-IDF, and Doc2Vector.
Prediction:PAI can facilitate timely and accurate disease, weather, and stock exchange prediction. This is possible through its regression and classification algorithm.
Relationship Mining:Organizations can enhance their financial risk control with the help of the platform's ability to provide vast connected subgraph and label propagation. Additionally, PAI can also make SNS social relationship mining possible. It offers K-CORE, maximum connection subgraph, and tag propagation.

So, who can use Alibaba Cloud PAI? Let us find that out in the next section.

Who Can Benefit from Alibaba Cloud PAI?

Similar to various Alibaba Cloud products and services, Alibaba Cloud API comes with several features and benefits. Its application is not restricted to a sector or an industry. Its design is such that it can benefit organizations across sectors.

The following organizations can benefit greatly from Alibaba Cloud PAI:

Government Agencies:In the majority of countries, the local government agencies are responsible for public utilities and safety. Such critical services cannot do without machine learning as they have multiple data sources, which are useful for critical insights obtained through data mining. PAI can help these agencies to detect fraud, forecast weather, and enhance security through face detection.
Financial Institutions and Organizations:Financial institutions like banks, financing companies, and stock exchanges can use PAI to identify data patterns and trends, prevent frauds, identify investment opportunities, and predict financial outcomes.
Health Care Indsustry:The healthcare industry has been quick at adopting AI and machine learning. One can witness the growth of wearable devices and sensors around us. These devices and sensors assess a patient's health in real-time and suggest relevant and accurate diagnosis and possible remedies. Additionally, PAI can also help healthcare experts predict disease-related trends through the information processed by the platform.
Transportation:PAI can significantly enhance transportation services, especially in modern-day congested urban cities. It analyzes data to identify trends and patterns to make routes more efficient and profitable by predicting potential problems, hence increasing business profitability.

Conclusion

AI and Big Data are transforming the way we live our lives and the way industries function. Businesses are looking out for innovative ways to increase the efficiency their business processes and ultimately enhance their business growth. Machine learning is one such technological innovation that is at forefront of the application of technology in businesses. It is a solution formed by the integration of AI and Big Data.

In this blog, we looked at the basic concept of machine learning, its various methods, and the business benefits of machine learning. We have also learned about Alibaba Cloud Machine Learning Platform for AI (PAI), a machine learning platform that was recently updated to cater to more industries across China. To know more about PAI, you can visit http://www.alibabacloud.com/product/machine-learning.

Community

Is Your Enterprise Well-Prepared for AI?

Classification of Machine Learning Methods

Why Businesses Can't Afford to Miss the Machine Learning Bus

Alibaba Cloud Machine Learning Platform for Artificial Intelligence (PAI)

Four Key Features of Alibaba Cloud PAI

Application Scenarios of Alibaba Cloud's PAI

Who Can Benefit from Alibaba Cloud PAI?

Conclusion

Read previous post:

Read next post:

Alibaba Clouder

You may also like

Comments

Raja_KT March 22, 2019 at 3:56 pm

Alibaba Clouder

Related Products

Image Search

Intelligent Service Robot

MaxCompute