Data science and data analysis continue to become more and more complex as technology evolves and artificial intelligence makes new strides every day. How data science models are built and adopted by the business quickly changes from year to year. With multiple techno-breaks in 2022, such as ChatGPT, Understanding the industry changes and realigning the direction sets a good start for 2023!
- Automated Machine Learning Maturity
Automated Machine Learning (AutoML) is the poster child of the artificial intelligence revolution. The field of machine learning has matured rapidly in recent years, thanks to big leaps in hardware processing power, cloud-based computing, and droves of new data sources due to the proliferation of the Internet of Things(IoT).
With all this new data comes increased complexity — and now, thankfully, automation. Automation in data science aims to transform existing repetitive work into reusable pipelines, allowing data scientists to focus on new complex ideas.
2. Predictive Analysis in Widespread Sectors
Trends such as predictive maintenance are likely to become more widespread, with more businesses automating ML applications for operational decision-making. From large tech firms to traditional small businesses, data science and technology are penetrating every industry and boosting productivity with data.
3. Deep Learning and Neural Networks Moving to GANs
Deep learning/neural networks continue to grow more complex. Convolutional neural networks have led us into Generative Adversarial Networks (GANs), which can allow for a deep neural network that learns how to generate imagery based on images it’s been shown.
Conceptualizing what a GAN looks like is tricky at best — which is why online tools and demonstrations like Google’s Inceptionism exist. These types of tools will increase in popularity, especially if GANs lead to advances or rapid innovations in commercial applications (for example, image recognition).
4. Enhanced Open Data Access
Over time, more government data will find its way onto public databases where open-source code developers can create products and services, not just enterprise software.
5. Rapidly Developing AI/ML Talent Pool
As organizations across industries begin to see value in deploying artificial intelligence throughout their operations, they will need access to skilled people. According to consulting firm McKinsey & Company, artificial intelligence could boost global GDP by $13 trillion dollars between now and 2030. This means job opportunities and newly created industries for anyone interested inbuilding expertise around machine learning technologies.
6. Customization and Collation in Data Preparation
The demand for customized datasets will continue to grow, meaning that business analysts (not just data scientists) will need easy ways to create custom datasets. Also, with more variables being introduced into datasets every day, software developers will need more powerful tools for data preparation.
It won’t be enough to simply load a file into R or Python and run commands — that process has become slow and clunky. Instead, users will want more access to interactive development environments (IDEs) that make it quick and easy to prepare a dataset from beginning to end. Think spreadsheet-like drag-and-drop functionality.
Traditionally made for coding tasks, these data-intensive IDEs will allow easier access to data prep tasks. That way, instead of each step of preparing a dataset being an isolated task, users can perform complex operations directly on their dataset as if they were working in a traditional Excel spreadsheet.
7. Trusted AI
Trusted AI refers to artificial intelligence systems that are designed and implemented in a way that is transparent, accountable, fair, reliable and SAFE.
Overall, the development of trustworthy AI is an important goal, as it can help to ensure that AI systems are used ethically and responsibly, and that they have the trust and confidence of the people who use them.
Reference: Launch Consulting (launchconsulting.com)