What’s Up with Machine Learning?

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What’s Up with Machine Learning?

A visualization of machine learning with the shape / figure of the brain and chip.

A lot of us have heard about artificial intelligence, the Internet of Things, virtual personal assistants, and all that jazz. But not machine learning. Machine learning is not exactly a new concept, but because it has become feasible for the mass-market only recently, it is not something that many people are familiar with.

The concept of machine learning has been around for decades. In fact, in the 1950’s, the idea of machines learning from available data had already surfaced. And then there’s IBM. The company revolutionized the IT industry in 1988. It introduced the principles of probability-based data algorithms to what’s previously been rule-based machine learning.

 

Machine learning as we know it

While machine learning is a term that’s probably new to you, the technology is something you have surely encountered several times before. But where and how?

One is through virtual PAs like Siri, Google Now, and Alexa. They all use machine learning to gather and analyze information based on our interactions with them and then use the data to understand our behavior and anticipate our needs. By doing so, these virtual PA technologies are able to tailor their services according to our preferences.

There are also social media sites, which apply machine learning in recommending friends to add and pages to follow. Of course, machine learning is also behind Facebook’s facial recognition capacity in photos, which save us a considerable amount of time and effort when tagging.

Then there’s also machine learning keeping financial services secure. Protecting us from credit card fraud by detecting our payment patterns.

Needless to say, machine learning is a big part of how technology is advancing in the 21st century. New gadgets, new appliances, and new IT offerings include features that utilize machine learning in one way or another.

 

Machine learning in the business world

A growing number of today’s businesses, regardless of the industry they are in, want to cut labor costs, improve consistency, and streamline operations. And to do this, they need to make their data do more work for them. And this is where machine learning comes into the picture.

For businesses, the current growth in the volume of data and sources will continue to go faster. And the volume and speed by which data becomes available through digital channels will continue to outpace manual capabilities, including decision-making. To deal with this, ML is needed to make use of the data and to automate the ever-increasing streams of information. It is only through this that businesses will be able to make data-driven business decisions in a timely manner.

In other words, businesses can infuse ML into core business processes that are connected with their data streams. Real-time learning will help them significantly improve their decision-making processes.

This is why many enterprises are now spending on ML and its related technology. But even if it does come at a price and considered an excessive expenditure, businesses consider it an investment for their future. Not to mention one that could jack up revenues in the long run.

Developers are also experimenting with technology and algorithms. This allows ML to be more easily applicable in business plans and budgets.

 

Making the most of machine learning algorithms

So how can organizations make the most of the latest machine learning algorithms? Here are a few tips:

 

Invest in data quality services

One of the best features of ML technology is its flexibility. You can use this to leverage everything in your operations. And every application will need a single repository where your data can be collected and stored to allow the algorithm to analyze it and evaluate its usefulness. This database must provide a steady stream of clean, detailed, and accurate data for ML algorithms to generate informed assessments and offer recommendations.

To put it simply, invest in data quality and consolidation services to make your data serviceable for all machine learning applications.

 

Learn the language

You must consider the different programming languages that you want to add to your software ecosystem. Of course, you also need to consider your business’ end goals, the programming skills that are at your disposal, and the properties of each programming language.

You can master the appropriate programming language by studying about it, experimenting on it, and learning from others who know what they are doing.

One example of these programming languages worth looking into is Python, which is one of the most popular globally according to the latest Tiobe Index. In fact, Python has overtaken its competitors in terms of popularity, thanks to its readability, versatility, flexibility, and simplicity. There are countless individuals and groups around the world who are learning and using Python, and they share tips, programs, and entire algorithms with each other online. This network of users offer a broad range of learning materials to those who hope to get started with Python.

Moreover, organizations that want to utilize ML across all teams must provide an underlying data infrastructure through which everyone can feed and take. For the Business Intelligence team, this will typically be SQL, yet data scientists should be able to run scripts on data using the languages they prefer. This is called the standardization and democratization of data. This will enable enterprises to apply ML across all parts of their operations in more experimental and creative ways.

 

Look to the cloud

Your IT infrastructure may be able to host many open-source frameworks upon which you can build ML solutions. However, you may lack the scalability and power to support these solutions in an efficient manner. This is not a big problem. You can look to the cloud for solutions.

Hyperscale cloud offers consumption-based access to Graphics Processing Unit (GPU) compute. It also offers x86 compute. With it you can build a performance infrastructure for your in-database analytics, which will feed your algorithms.

The flow of required or serviceable data must keep pace with the ML algorithms that work in near real-time. The cloud’s elastic scalability can be exploited to make sure that workloads are supported throughout a particular project. This will give businesses the freedom to experiment with ML capabilities.

ML infrastructure and data analytics are critical for data-centric industries. Those that are planning on new technology strategies should make sure that their analytical database infrastructure functions across on-premise and cloud applications in unison. This will allow them to migrate workloads between on-premise and third-party data centers. Therefore, enabling them to optimize costs and plan for data governance requirements.

Machine learning may look intimidating and challenging in its application and complexity. However, providing the infrastructure for your ML projects is actually achievable. Businesses are even using the necessary technologies in their standard IT processes already. Examples are databases, Infrastructure as a Service, and programming languages. And to take the next step as far as optimizing for ML is concerned, these technologies simply need to be utilized in a different and less passive capacity.

Image courtesy of www.vpnsrus.com.

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