What is Machine Learning?

Machine learning (ML), in simple terms, is the ability of machines to learn from data and make decisions and again unlearn and relearn through the new data sets available. Netflix’s recommendation system is one of the best examples of machine learning. Netflix applies machine learning algorithms to generate recommendations to the viewer using a combination of experts to tag the content on their platform and the user data it collects. This has become a core competency for Netflix that helps it to stay ahead of the competitors.

Autonomous vehicles are another example of machine learning without which driverless vehicles are just a sci-fi fantasy. As per a prediction by Gartner, 10 per cent of all new vehicles will have autonomous driving capabilities by 2021.

The practical benefits are ML can be amazing. It is not that necessary to have big data sets and in-house data science experts to execute machine learning-based analytical and prediction systems. One can start small by leveraging cloud computing platforms. “Pay as you go” models on cloud platforms help one to keep a check on one’s bills.

Trends in Machine Learning Space

Augmented Analytics

Augmented analytics employs automated machine learning for data preparation, analytics and business intelligence, and automates data science itself to reduce the need for expert analysts.

Examples of how Augmented Analytics can achieve automation:

  1. Augmented analytics will be used widely for data preparation. As the augmented analytical models become more mature, citizen data scientists, who don’t need a profound background in statistical analysis, can help the analytical models to get the results, eliminating the need for data science experts.
  2. Another way is where the need for experts is minimal and top-level executives can directly interact with the system to obtain key insights that help to optimize the business operations. Organizations that adopt augmented analytics will have better chances to disrupt and innovate in the right direction.

Optimizing Cloud Platforms Using Machine Learning

As per a forecast by Gartner, the worldwide public cloud market will grow from USD 175.8 billion in 2018 to USD 206.2 billion in 2019. With the ever-growing number of services on the public cloud, the usage has become complicated for new cloud adopters and oftentimes they require certified experts to use the platform. This provides an opportunity for new players to disrupt the market and make cloud adoption easier.

Digital Data Forgetting Using Machine Learning

Compared to the past, data storage costs have gone down and storing large volumes of data has become efficient with the rise of cloud computing. However, storage expenses can increase exponentially as massive amounts of data is being generated. Also, regular software is unlikely to be able to handle massive data. Thus, a data center setup or a cloud solution is necessary to hold such massive amounts of data.

It can be time-consuming for humans to decide which chunks of data to let go. Machine learning can help to understand these scenarios better and unnecessary data can be identified and deleted on command. Organizations can use this technique as an effective tool to control expenditures and can remove the hassles of handling unnecessary data.

Machine Learning for Effective Marketing

Digital marketing teams can leverage machine learning tools and techniques to figure out effective marketing strategies by extracting the patterns from existing user data as well as the user’s openly available data such as tweets and reviews. Marketing tools and software providers are already experimenting with machine learning. It is going to revolutionize how marketing is approached.

For Sales Intelligence

Organizations can employ ML to understand their customers and their preferences to improve existing products and launch new products. They can also fine-tune their sales strategies. Professionals can do those same things. However, the collaboration of professionals with machine learning can be much faster and less time-consuming. With growing competition in both B2B and B2C, organizations can leverage machine learning-based sales intelligence to stay on top.

Conclusion

Machine learning has already been used by many tech companies to improve and optimize their products and services using machine learning. It is significant in analyzing and understanding the data organizations collect and to empower their business. The full impact of machine learning is yet to be seen and it will be exciting to witness how machine learning would impact humanity, as a whole.