3 Machine Learning Trends for 2019 Combined With Apache

2019 Combined With Apache

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Finding a way to stay on the cutting edge of technology is something modern business owners should be passionate about. Each year, more and more innovative trends and disciplines hit the technology world. Researching and utilizing these trends is a must when attempting to stay competitive in the modern business world.

For years, business owners have leveraged the power of machine learning to meet their needs. Nearly 51 percent of all American business owners claim to be an early adopter of this technology.

If you are trying to build a scalable microservice for your business, using machine learning combined with Apache is a great idea. Are you curious about the latest machine learning trends combined with Apache? If so, take a look at the list below.

1. The Power of KSQL and Machine Learning

If you are not familiar with KSQL, then you may not realize just how powerful and convenient it is when attempting to build mission-critical and scalable services for your business. Kafka Streams are used by tech giants like Uber and even Netflix in the development of apps and software. The KSQL is built into the Kafka Streams program, which makes it readily accessible to Apache aficionados.

When pairing machine learning technology with KSQL, you can embed neural networks into your streaming programs. Many hospitals use these neural networks to detect anomalies when checking out patients. If anomalies are detected in these scans, medical personnel is alerted in real-time. This allows them to provide a patient in need with quick and comprehensive care.

Keeping these systems functional and accurate will require lots of maintenance and reviewing of Apache error logs. You can find out more about this practice by reading Apache Logging Basics -The Ultimate Guide to Logging.

2. Embracing Automated Machine Learning

If you are like most business owners, your main concern is reducing the workload you and your team have. One of the best ways to accomplish this is by using automation whenever it is available. Auto machine learning allows a person to develop various analytic models with limited knowledge. These types of programs use various implementations to create things like neural networks and decision trees.

The only thing you have to do to get the ball rolling with an auto machine learning program is to upload your dataset history. With the power of auto machine learning, you can improve the existing data management and automated processes you have in place without having to hire a data scientist.

You may be familiar with Google’s AutoML and DataRobot, which are two of the most popular cloud-based auto machine learning tools on the market. Once you spend about 30 minutes with one of these programs, you will surely be amazed at how quickly and efficiently they work.

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3. Using AutoML on the Apache Kafka System

One of the first things you will notice about most auto machine learning tools is that they offer their own deployment models. While these deployments models can be helpful, they may not be the best fit for your particular needs. Luckily, you can deploy these tools into your application due to the fact that autoML tool manufacturers offer easy export models.

With a bit of Java code knowledge, you can embed these tools into Kafka Stream s with ease. With the autoML tools, you can build a scalable machine learning app without extensive knowledge of machine learning.

If you are unsure about how to make the trends mentioned in this article work for your business, consulting with an IT professional is a good idea. These professionals can help you choose and implement machine learning tools with ease.