The Evolution of Machine Learning
Machine learning (ML) has evolved in leaps and bounds. While some still remain skeptical about its progress, most enterprises are excited about the ample opportunities that would open up with the growth of machine learning.
Machine learning is a part of the gigantic field of Artificial Intelligence (AI). It has been just a few decades since AI was introduced. With machine learning, like the name implies, the machine learns. Instead of the conventional systems with rigid codes, this machine can continuously learn and adapt itself. Internet is everywhere and there is so much data to handle. The processing and handling of massive data require adaptive learning algorithms to make them more efficient and quick.
While the scope is limitless, some of the popular areas where machine learning is being used are:
• Medicine and healthcare: Wearable technologies that continuously monitor the patients and alert the practitioner when required.
• Government: Government needs data mining more than any other field, be it for fraud detection or any criminal investigations.
• Financial sector: Especially in trade, processing a large amount of investment data and assisting investors.
Some of the major milestones in the evolution of Machine learning are:
First came Cloud:
With several major players like Google and Amazon switching to a cloud-based infrastructure, a whole new era began in the IT sector. Amazon’s Elastic Compute was a major breakthrough.Which business would not want a resizable capacity of computation?
Then came the Big Data:
Internet expanded and mobile internet grew exponentially. This lead to the “Big Data”. A tremendous amount of data from a variety of different sources has to be accurately handled andrapidly processed. Thanks to cloud computing, data management took a new shape. Hadoop, Spark, and so many other data analytic tools came in to handle the ever-growing amount of data.
Then the era of Machine Learning begins:
Well technically the era began quite a while ago but thanks to Cloud computing and Big data and other revolutions in the IT industry, more people are stepping forward to tap the maximum potential of machine learning.
When in the past it was understood that no computing machine could be as efficient as the human brain, artificial neural networks came into the picture. But it is doubtful whether the full scope of this field was comprehended when it first was established.
Blending well all the powers of data analytics and machine learning, big names like Amazon, Microsoft and Google have again come up with solutions to help industries in making important decisions based on the predictions offered by accurate data analysis. This means that even if you are not an expert who understands every machine learning algorithm, you could still use the full capacity of machine learning for your business with the help of these services.
• TensorFlow Serving:
This service from Google comes with a lot of flexibility and is great for production environments. Your APIs and the architecture of the server can be retained while you feed and run new algorithms. This library can serve many machine learning models. It comes with a modular architecture.
• Cortana Analytics:
This one from Microsoft comes fully loaded with big data management and analytics. The analytics engine in Cortana Analytics is Hadoop-based.
• Amazon Machine Learning:
Amazon launched this service to help businesses make machine learning models without having in-depth knowledge about the machine learning algorithms. It is based on Amazon’s own ML models which have been used by Amazon for years. This analyses the patterns in the old data, understands and processes the new data and accordingly comes up with the predictions.
These services have created a new space for the growth of machine learning. But remember that the field is deep and unless you get the nuances of it, you might end up being caught in the whole whirlwind of complicated technologies. Is your business ready to handle the advanced services and adapt accordingly? That is a question you should be asking right now.
Implementing Machine Learning:
What are the current technologies that could help you make the most use of machine learning capabilities?
If you are planning to use machine learning for your enterprise, then there are a lot of tools you could rely on. Broadly for all the algorithms used for machine learning like Support Vector Machine, Linear regression and the others, you could do away with Python and R codes.
If you are looking to implement machine learning, it has become an easy task with a long list of free software that can help you do the task. Some of the open source developments like Shogun, Scikit Learn, Mahout, H2O and lots more could also help tap machine learning potential. These frameworks and libraries are all that you need to get the right predictions with the machine learning models.