Machine Learning solutions continue to change the core processes of enterprises and are becoming more pervasive in our daily lives. 

Although it is a trending topic, the term “Machine lLarning” is often used interchangeably with the concept of artificial intelligence. Machine Learning is a field of artificial intelligence based on algorithms that can learn from data and make decisions with little or no human intervention.

Many companies have already started using machine learning algorithms because of their potential to make more accurate predictions and business decisions. Machine Learning companies have received $3.1 billion in funding in 2020. Machine Learning has the potential to drive transformational change across all industries.

Because machine learning is prominent in our lives today, it’s hard to imagine a future without it. Here are our predictions for machine learning in 2021 and beyond.

Quantum computing could shape the future of machine learning

 Quantum computing allows for simultaneous operations with multiple states and faster data processing. In 2019, Google’s quantum processor completed a task in 200 seconds that would have taken the world’s best supercomputer 10,000 years.

Quantum machine learning can improve data analysis and get deeper insights. This high performance can help businesses get better results than with more traditional machine learning methods.

To date, there is no off-the-shelf quantum computer. However, several large technology companies are investing in the technology, and the emergence of quantum machine learning is not far off.

 The end-to-end model development process

Automated machine learning or AutoML is the automation of the process of applying machine learning algorithms to real-world problems. AutoML simplifies the use of complex machine learning models and techniques easier than a machine learning expert.

AutoML makes machine learning accessible to a larger audience, suggesting its potential to alter the technological environment. A data scientist can utilize AutoML to rapidly locate algorithms that can be used or to determine if any algorithms have been missed. Here are some steps in developing and deploying a machine learning model that AutoML can automate:

• Data pre-processing – improving data quality, transforming unstructured data into structured data through data cleaning, data transformation, data reduction, etc.
• Feature development – using automation with machine learning algorithms to create more adaptable features from the raw data.
• Feature extraction – using different features or data sets to produce new features that will improve results and reduce the amount of data being processed.
• Feature selection – selecting only useful features for processing.
• Algorithm selection and hyperparameter optimization – automatic selection of the best hyperparameters and algorithms.
• Model deployment and monitoring – framework-based model deployment and model state monitoring using dashboards.
• Industries where machine learning is expected to change
• Healthcare and pharmaceuticals.
• Vast amounts of data are generated in healthcare. The application of machine learning techniques can significantly improve prediction and treatment.

Disease prediction

 Advances in technology improve the prediction and prevention of possible diseases rather than treatment after diagnosis. The traditional approach to disease prediction involves a limited number of variables such as age, weight, height, gender, etc. The machine learning technique examines various characteristics based on research, patient demographics, medical data, and other sources, which can result in improved illness prediction findings.

Drug development

Drug development is time-consuming and expensive. According to a recent study, the average cost of bringing a new drug to market is $985 million. Using data sets with the chemical structure of a drug compound, machine learning algorithms can predict its effects on different cell lines and genes and identify possible side effects. Using machine learning will speed up drug testing times, speeding up the process of bringing a drug to market.

Electronic Health Records

Electronic Health Records (EHRs) include data in different forms and sources. Applying machine learning techniques, such as natural language and image processing, can help transform this data into a standard format. EHRs based on machine learning can optimize and improve the process of identifying clinical patterns, leading to better predictive outcomes.

Manufacturing

Machine Learning is still in its early stages of adoption among manufacturers. In 2020, only 9% of survey respondents used artificial intelligence in their business processes.

Applying machine learning tools to manufacturing can facilitate various processes and operations, including monitoring performance and equipment conditions, predicting product quality, and predicting energy consumption. Given the ongoing advances in machine learning, you can expect to see more robots in production facilities shortly.

Among many other benefits, machine learning in manufacturing can reduce costs, increase quality control and improve supply chain management.

Cars and self-driving vehicles

Tesla, Waymo, and Honda are car development companies exploring introducing self-driving vehicles. And while manufacturers have already introduced cars with partial automation, fully autonomous vehicles are still under development. Machine Learning is one of the leading technologies to help make these dreams a reality.

Deep Learning, a machine learning technique, can enhance vision and navigation in self-driving cars by assisting with path planning, scene classification, and obstacle and pedestrian identification.

As new technologies develop, machine learning algorithms can be used more productively as part of software engineering in automotive engineering. The future of machine learning will open up many opportunities for businesses. Make sure your business is ready to make the most of the options.

Contact Altezza Creative Solutions to learn more about ML solutions. We’ll walk you through the core aspects of your ML project.