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5 key Machine Learning trends for 2019

Machine learning is the ability of machines to learn from data and make decisions and again unlearn and relearn through the new data sets available.

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By 2021, 10 percent of all new vehicles will have autonomous driving capabilities, Gartner predicts. Machine Learning (ML) — a subset of Artificial Intelligence (AI) — heavily fuels autonomous vehicle capabilities. Simply put, without machine learning driverless vehicles are just a sci-fi fantasy.

Machine learning, 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. A real-life example is, how Netflix’s recommendation system works. Netflix uses a combination of experts to tag the content on their platform and the user data it collects. It then applies machine learning algorithms to generate recommendations to the viewers. This is one of the core competencies of Netflix that helps them to stay ahead of competitors.

The practical benefits are ML can be amazing. However, it is not that necessary to have big data sets and in-house data science experts to implement machine learning based analytical and prediction systems. You can start small by leveraging cloud computing platforms. Pay as you go models on cloud platforms help you to keep a tab on your bills. On the other side, Indian cloud layers like E2E Networks understand the budgetary difficulties associated with experiments. They specifically focus on startups who struggle with budgets when experimenting with ML and other bleeding edge technologies.

Moreover, chasing every fad is a red flag; such practices can make organizations largely unsuccessful. So it is essential to identify which technologies can help to solve customer problems and increase operational efficiency and which technologies are shiny new things that fade away with time. That said, here are some trends in the machine learning space that are poised to shape the future of business.

Augmented Analytics

Data science is the big picture and machine learning is the driving engine. 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.

Augmented analytics can achieve automation in all three major areas of data science; however, it only happens gradually. In the first wave, 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 strong background in statistical analysis — can help the analytical models to derive the results, removing the need for data science experts.

The final wave 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

Worldwide public cloud market will grow — from $175.8 billion in 2018 — to $206.2 billion in 2019, Gartner forecasts. With the ever-growing number of services on the public cloud, however, it has become complicated for new cloud adopters and oftentimes they require certified experts to use the platform. This is where other players can disrupt and make cloud adoption easier.

That is one challenge we saw with our customers early on, and we made our cloud platform easy to onboard and efficient to manage. However, we see a lot of opportunities to innovate. Right now, we are experimenting with machine learning to understand and improve customer experience and even, to help them deploy machine learning based applications with ease. It is also interesting to see how other innovators would optimize their cloud platforms with machine learning.

Digital Data Forgetting Using Machine Learning

We are producing more and more data every single day. 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. On the other hand, big data is being called so because regular software systems can’t handle it. A data center setup or a cloud solution is necessary to hold such massive amounts of data.

Every single piece of data, however, is not useful. It is important to understand which data is not helpful. Scenarios where a permanent data deletion required can be complicated. 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 then, can be deleted on command. This is still early for digital data forgetting. However, 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

Sometimes, marketing can make or break a business. The need for effective digital marketing is increasing more than ever. However, many organizations depend on well-known marketing practices and some in-house experiments to understand and to reach out to prospective customers.

Digital marketing teams, however, 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. We are already seeing marketing tools and software providers experimenting with machine learning. It is going to revolutionize how we approach marketing.

For Sales Intelligence

Organizations can employ ML to understand their customers and their behavior to improve and launch new products and 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. As the Indian market, both B2B and B2C, is becoming more competitive than ever, organizations can leverage machine learning based sales intelligence to stay on top.

Conclusion

Many tech companies are already improving and optimizing their products and services using machine learning. It is key in analyzing and understanding the data organizations collect and to empower their business. On the other side, It is exciting to see how machine learning would impact us, humanity, as a whole.

The article is written by Tarun Dua Managing Director and Co-Founder E2E Networks.

  • Published Date: February 27, 2019 4:32 PM IST