Machine learning solutions are incorporating changes into core business processes and becoming more prevalent in our daily lives. According to forecasts, the global machine-learning market will grow from $8.43 billion in 2019 to $117.19 billion by 2027.
Because machine learning algorithms have the potential to make more accurate predictions and business decisions, many companies have already begun to use them. Machine learning companies received $3.1 billion in funding in 2020. Machine learning has the potential to transform entire industries.
With machine learning being so prevalent in our lives today, it’s difficult to envision a world without it. Here are our predictions for machine learning development far beyond.
Growth and future of machine learning:
- Quantum computing has the potential to shape the future of machine learning.
One technological advancement that has the potential to improve machine learning capabilities is quantum computing. Quantum computing enables simultaneous multi-state operations, resulting in faster data processing. In 2019, Google’s quantum processor completed a task in 200 seconds that would have taken 10,000 years for the world’s best supercomputer.
- AutoML will make the entire model development process easier.
AutoML, or automated machine learning, is the process of automating the application of machine learning algorithms to real-world tasks. AutoML simplifies the process so that anyone or any business can use complex machine learning models and techniques without being a machine learning expert.
Here are some stages of machine learning model development and deployment that AutoML can automate:
- Data pre-processing entails improving data quality, and transforming unstructured data into structured data through data cleaning, data transformation, and data reduction, among other things.
- Feature engineering is the process of using automation and machine learning algorithms to create more adaptable features based on input data.
- Feature extraction is the process of extracting new features from existing features or datasets in order to improve results and reduce the size of the data processed.
- Feature selection entails selecting only useful features for processing.
- Algorithm selection and hyperparameter optimization – choose the best hyperparameters and algorithms automatically.
- Model deployment and monitoring entail deploying a model based on the framework and monitoring the model’s health via dashboards.
Scope of machine learning
If the current state of machine learning is exciting, the future of machine learning offers significantly more and more complex opportunities for technologists. Let’s go through them one by one.
- Industry of Pharmaceuticals and Healthcare
In the healthcare and pharmaceutical industries, machine learning has virtually limitless applications. Today’s healthcare industry generates massive amounts of data, which aids in the automation of administrative processes in hospitals, the mapping and treatment of infectious diseases, and the personalization of medical treatments.
- Automobiles and self-driving cars
Tesla, Waymo, and Honda are among the automakers investigating the possibility of deploying self-driving cars. While manufacturers have already shown off cars with partial automation, fully autonomous vehicles are still in the works.
- Increasing Efficiency in Operations
Document management is the most common use case for optimizing operations. Today, many robotic process automation and computer vision companies, such as UIPath, Xtracta, ABBYY, and others, enable this. However, the future of machine learning will aim higher.
- Fraud Prevention
Banks and other financial institutions use machine-learning-based fraud detection technology to prevent fraud (though the irony of proving “I am not a robot” to a machine is not lost on us!).
- Personalization at Scale
ML is used in retail, social media, and entertainment platforms to provide customers with personalized services and experiences. E-commerce and media platforms are utilizing ML to provide hyper-personalized experiences as well as freemium payment models.
Prediction for the future of machine learning
ML, a branch of Artificial Intelligence, has grown by leaps and bounds in the last two years, fueled by data processing. Enterprises with a technology zeal will be able to innovate and experiment in order to develop something new and compete.
There are a few trends and technologies that may accelerate. However, these are possibly the most accurate to forecast. So, here are three forecasts:
- Enterprise Resource Planning and Machine Learning
There is no doubt that machine learning will have a significant impact on enterprise resource planning (ERP). AI and machine learning discoveries will persuade organizations to optimize their operating models, which are based on governance structures, software applications, business practices, and technology infrastructure.
- Human Resource Analytics and Machine Learning
According to a survey, 38% of organizations use smart tools for conducting interviews. The future of human resources is changing at a rapid pace. Companies are currently incorporating ML-enabled tools to identify the best candidate for recruitment. These advanced tools assist them in streamlining operations in order to find the best talent, managing training campaigns, and developing strategic recruitment plans.
- Research and Development (ML)
Advanced ML analytics has created not only a sophisticated interaction but also the incorporation of intelligence into the composite that can work with artifacts. The Market is working to develop and deliver more value by addressing social issues. By replacing labor, expanding capabilities, replacing labor, and then developing a new value.
Machine learning algorithms can be used more productively as new technologies emerge. The future of machine learning clearly shows an increased application of machine learning across various industry verticals.
LinkedIn currently has over 21,000 jobs for ML engineers, with hiring continuing throughout the pandemic. Head over to Skolar to start learning machine learning from scratch.