From voice assistants to self-driving cars, machine learning is revamping not only the way we interact with machines but also how we interact with the world. predictive analytics, and artificial intelligence has become a norm in IT, and machine learning is leading the way

It is becoming one of the hottest technologies in the market, making mobile apps and services smarter and better than ever. as software applications are becoming smarter to improve our business and personal lives. With massive improvements in hardware and Big Data, machines can sense, understand, interact, predict, and respond to solve industry business problems. According to IBM, about 90% of data existing in the world has been generated in the last 2 years. This massive amount of data can’t be processed and managed by humans physically and this is where Machine learning comes into the picture.

Machine Learning

Artificial Intelligence is the concept of making machines capable to performing tasks without human intervention, such as building smart machines. While Machine learning (ML) is a subset of AI based on the idea of making computer algorithms that automatically upgrades themselves by discovering patterns in existing data without being explicitly programmed.

The whole processing of ML tools depends on data. More the data an algorithm obtains, more accurate it would become and thus, more effective results it will deliver.

Machine learning uses various techniques for data extraction and data interpretation. However, the two prominent techniques are Supervised learning and Unsupervised learning.

Supervised machine learning creates a model that could make predictions based on data in the presence of uncertainty. Whereas, Unsupervised machine learning determines hidden patterns or inherent data structures, taken into consideration for drawing conclusions from data sets comprising of input data without labelled results. There are a number of potential use cases for machine learning in life sciences. Here are some that you may wish to incorporate into your business model.


Quality must be enforced in supply chain and manufacturing business process for regulatory compliance. Root-cause analysis is a key aspect of corrective and preventive action (CAPA), which aligns with industry initiatives like QbD (quality by design), PAT (process analytical technique), and CPV (continued process verification). There is a clear need to identify main causes for reported defects in material assets and understand the impact of identified causes to manage the overall defect count. Based on gathered data, machines can predict what production can be produced vs. planned for a specific duration (based on historical production), thereby preventing deviations and nonconformances. Analyzing the cause of deviation from standard cycle time for manufacturing equipment, and prescribing measures to achieve standard cycle time, affect yield and scrap.

Learning management

Learning management is critical in regulated industries, and training is a big part of human resources’ duties in life sciences. In hiring, HR business partners can identify the best candidates by parsing resumes into structured information, then visualize candidate profiles by skills, education, and experience, to compare and generate best-fit scores of profiles to jobs and vice versa. Talent management can take a more personalized approach towards career mapping based on employees’ unique situations, skill trajectory, and training, thereby opening opportunities to employees for fast-track growth.

Fraud Detection

Machine learning is also significantly used in Banking/finance industries to cope up with fraud. ML tools scan the transactions you make in real-time and gives a fraud-score. If the fraud-score exceed a specific threshold, your account is automatically rejected. If asked to done manually, this would have been nearly impossible to review thousands of data in seconds and take a decision.

Sales and marketing

Sales and marketing can leverage machine learning during sales negotiations with wholesalers, hospitals, clinics, and retail pharmacies by capturing keywords, sentiments, competitors, and new contacts to feed into deal scoring, ultimately improving the win rate. Bio-pharma sales reps can share marketing collateral of interest to physicians and key opinion leaders. Third-party prescription data can create target groups for behavior-based marketing campaigns to boost sales. Thus, machine learning can help build customer loyalty with proactive retention strategies in the life sciences industry.

Health Diagnosis

Machine learning is also becoming a buzzword in the healthcare industry. It is taken into account for different purposes like drug discovery and robotic surgery.

Recently, Google created a machine learning algorithm that helps detect cancerous tumours on mammograms, while Stanford is using the technology to identify skin cancer.

Predictive Analysis

Machine learning tools along with Big data analytics is used by the mobile app developers and marketers to understand how the users interact with a mobile app and group the data under different categories for predicting next step to be taken for engaging users and increasing the conversion rate.

Smart business process enabled by machine learning, automation, and artificial intelligence can help achieve intelligent enterprise goals for the life science industry, particularly as the IoT technology adoption rate improves.

Machine Learning and AI in Real Life

You May Also Like

One thought on “Machine Learning and AI in Real Life

Leave a Reply

Your email address will not be published. Required fields are marked *