Machine Learning, Data Science What does the future hold for us?

As long as the human enthusiasm for building artificial consciousness exists, the future of emerging technologies such as data science, artificial intelligence and machine learning cannot be locked in. According to a report by marketresearch.com, the global machine learning market was valued at INR 839.55 billion. in 2020.

It is expected to reach INR 7632.45 billion by 2027 growing at a CAGR (compound annual growth rate) of 37.16% during the period 2021 – 2027. With the incredible amount of data available and the fuel they offer companies to develop, explore and experiment with innovative technologies, we in the 21st century can witness some truly cool, incredible and life-changing applied scientific inventions.

Augmented data analysis

Augmented data refers to the type of automated data analysis, where the examination of large amounts of data (to obtain meaningful insights) is done by combining AI, machine learning, and natural language processing. Conclusions obtained by these methods are more precise and accurate, allowing experts to merge data obtained from inside and outside the organization to extract better insights for business sustenance. Additionally, the increased number of visual data discovery tools helps data scientists and business owners understand data, derive insights from it, and leverage it for better business insights. According to firstsiteguide.com, by 2025, more than 150 zettabytes of big data will need to be analyzed. This means more information to grow businesses and help them meet customer needs with greater accuracy.

ML automation

ML automation or Auto ML or Automated ML refers to the technique of applying machine learning (ML) models to real-world situations through automation. Here, the entire process of selecting, building, and tuning machine learning models is automated. This helps produce faster and more accurate results than traditional hand-coded methods. However, this cannot be considered a substitute for human expertise. It is simply a tool that can be used to quickly and accurately complete and execute some of the monotonous jobs, allowing professionals to focus freely on more complex or unique activities.

Cloud-Based Enterprise Solutions

The pandemic has dramatically accelerated the transition to cloud-based enterprise solutions for all data needs. However, the real challenge here is not to produce the necessary data, but the lack of a safe space to collect, label, clean, organize, format and analyze this huge volume of data to extract insights.

The solution to this pressing problem is a reliable cloud-based platform that can efficiently store and protect the data on it. The next few years will be crucial for the data science and machine learning industry as the war to build the most sustainable cloud ecosystems for enterprises will continue.

Improved natural language processing

Most companies constantly follow the latest trends and successful models that help their products/services/organizations. It is in this context that natural language processing is most often integrated to analyze data and identify these patterns and trends. This type of automatic data analysis is ideal for obtaining reliable and meaningful information about Twitter Analytics, Customer Satisfaction Analytics, Customer Content Engagement Analytics, etc.

Data is currently an organization’s most important and exclusive asset for all kinds of business development and strategies. Using scientific, traditional, or automated methods to clean, store, and analyze data helps push the boundaries of actionable analytics. For over a decade, Jigsaw has curated technically sound, high-engagement learning experiences with industry best practices. These programs are designed and delivered by industry experts who have extensive experience and knowledge in the field. Emerging technology enthusiasts can find an array of programs to hone their skills in their specific areas of interest with Jigsaw. They are sure to benefit from the thorough practice and solid pedagogy.

(This author is Program Director for the PG Certificate Program in Data Science and Machine Learning at Jigsaw (A UNext Company))

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