This year’s hot topics include machine learning and artificial intelligence. Various research publications and technology users are continually looking for new applications of machine learning platforms. From the ongoing analytics assistance to the vendor and customer benefits, it’s important to leverage machine learning in your business intelligence efforts. Of course, with these analytics’ potential, it can be difficult to discern the particular purpose of such data-driven architecture. Trend forecasting is a critical component of data science, and the same goes for its applications in machine learning. To see how the industry has grown over the course of the previous year, it’s important to understand the trends and business value afforded by this technique.
While automation is a key component of almost all machine learning platforms, the Gartner Data Science Report shows the impact machine learning has on hyper-automation. The concept also bears the designation of digital process automation or intelligent process automation and is a top tool for data scientists. Hyperautomation vendors cannot rely on static software packages. Instead, data science and analytics need to be able to adapt to unexpected situations and developing circumstances. A rapidminer and other affiliates can positively impact hyperautomation and parse data bricks more capably.
With the converged capabilities of both machine learning and artificial intelligence, it appears as though these data science platforms are the future of analytics as we currently know it. As pioneering analytics leaders continue the proliferation of hyperautomation, business processes will continue to improve over time. This collision of analytics and the critical capabilities of deep learning can impact startups and enterprises across the U.S. and global markets.
Machine learning continues to cross paths with the Internet of Things and interconnected devices. Machine learning is being used to make IoT devices smarter, more capable, and secure. For your own data, enhanced security is paramount. Coupled with IoT devices’ critical capabilities, it’s possible to develop products that garner the highest ratings from brands like the Gartner research organization. This can lead to an augmented consumer as it gives insights into smart device behaviors, user interface capabilities, and market challenges.
The overlap between ML and IoT can have a domino effect because model development is concerned for the niche player. It can also lead to a more empowered class of analytics consumers. For completeness of vision, IoT devices need to have data access for specialized workflows. With ML, data preparation is simpler and can lead to devise growth and evolution on a larger scale but in a shorter timeframe.
While Gartner notes that many home security platforms have yet to leverage AI and machine learning, the integration is on an upward trend. As this continues, security components will spot triggers, deliver alerts more capably, and learn user preferences. They can also provide a full report of learnings and share key insights with their users. Using a data robot can prove especially impactful in these scenarios. While Gartner is correct that this may be a ways off, it’s still worth noting that ML can lead to a more capably secured home system.
On top of that, the security system will be better equipped to combat physical and digital threats. This can help with warranties of merchantability and the mining of security data bricks on a greater scale. To learn more about ongoing market trends, it’s a good idea to refer to Gartner research publications such as the Magic Quadrant. It’s a quick way to review statements of fact, learn Gartner’s opinions, and discern who the ML market leaders are. ML stands poised to transfigure numerous industries. It’s likely the service will continue its rapid expansion through 2021.