Top 9 Java Libraries For Machine Learning

In 5 years, the machine learning (ML) market size is projected to top $31 billion. This growth is mainly due to the advancements we’re seeing in AI, but right behind that is the increasing need companies have to reduce costs and streamline processes. Machine learning at its most basic level is a data management tool that retains information and improves from experience which is something every company wants from employees. The difference now is that it’s scalable and has less margin for error in addition to its work capacity which continues to improve over time. Managing data is among the highest in-demand skills globally for businesses right now. 

According to a report from Cision, the 10-year period up to 2030 will double the market size of global enterprise data management. What this means, in a broad sense, is that companies across various industries, investors, and especially tech leaders are recognizing the value of effectively managing and utilizing data. Ultimately they know something others don’t, or they’re just accepting something others won't which is that data holds the most potential for business growth in the future. They’re putting their money where their mouth is by investing in and utilizing systems that leverage machine learning and ultimately make this process more accessible. 

Where Java Comes In

Java, being the versatile programming language it is, offers tons of libraries and frameworks that facilitate the development of machine learning. These libraries have pre-built algorithms and tools that simplify the implementation of machine learning models and make the development process way more efficient. In this blog, we’re looking at some of the top Java libraries for machine learning that can help developers to leverage it effectively in their applications.

Before we get to that, here’s what you want to be thinking about when selecting a Java machine learning library:

• Algorithm support: Assess the library's support for different machine learning algorithms, like linear regression, decision trees, support vector machines, and of course neural networks. 

• Ease of use and improvement: Look for libraries that offer easy-to-use APIs and utilities for training machine learning models. Consider the availability of tools for cross-validation, hyperparameter tuning, and model evaluation.

• Feature engineering and data preprocessing: Does the library have functionalities for feature extraction, transformation, and normalization? Look for utilities that simplify common data preprocessing tasks, such as handling missing values, categorical encoding, and feature scaling.

• Support for big data processing: This is a big one. If you're working with large-scale datasets, you’ll likely want libraries that seamlessly integrate with distributed computing frameworks like Apache Spark.

• Visualization and interpretation: Check if the library offers tools for visualizing data, or interpretability. Visualizations are going to help with understanding your data and model behaviour, while interpretability tools help you gain insights into the factors driving your model's predictions.

• Deployment and integration: Evaluate how easily the library can be integrated into your existing software stack and deployed in production environments. Look for libraries that offer options such as model import/export or support for common deployment frameworks like TensorFlow Serving or Apache Kafka.

• Performance optimizations: Consider libraries that offer optimizations like parallel computing, GPU acceleration, or distributed training. 

There’s a lot to consider when choosing an ideal framework, these variables will help guide your choice but ultimately your unique variables will be the key factors such as project requirements, team expertise, and the overall goals you want to achieve.

With that, here are some of the top options that Java offers for ML libraries:

1) Deeplearning4j (DL4J):

DL4J is a Java library that specializes in deep learning. It provides sets of tools and algorithms for building and training deep neural networks. With its integration with Apache Spark and Hadoop, DL4J enables distributed deep learning on big data platforms. It also supports various neural network architectures, such as convolutional networks (CNNs) and recurrent networks (RNNs).

2) Smile:

Smile, or Statistical Machine Intelligence and Learning Engine, specializes in a range of AI tasks. When it comes to machine learning model integration and data analysis, Smiles's interface is user-friendly and has a ton of algorithms for classification, regression, clustering, dimensionality reduction, and so on. 

3) Weka:

Weka, an open-source Java library, has been a go-to among machine-learning enthusiasts for many years. It offers a vast collection of machine learning algorithms and tools for data preprocessing, classification, regression, clustering, and especially association rule mining. 

Weka's graphical user interface, called the Weka Explorer, lets users try out different algorithms. It also provides extensive support for data visualization, which makes it easier to understand and interpret the patterns in the data.

4) MOA:

Massive Online Analysis (MOA) is an open-source Java framework designed specifically for online learning and mining big data streams. It offers a variety of machine-learning algorithms that can handle consistent data streams in real-time. For developers, MOA allows them to build scalable and efficient models that adapt to changes in data over time. 

Like the last two, it also includes algorithms for classification, regression, clustering, and additionally anomaly detection. MOA's focus on online learning makes it a great tool for applications where data arrives continuously and needs to be processed immediately. 

5) DL-Learner:

DL-Learner focuses on machine learning with description logic (DL). It specializes in knowledge extraction from structured data and supports creating logical knowledge bases. DL-Learner includes algorithms for ontology learning, rule induction, and concept learning. It can be used to build intelligent systems that not only extract knowledge from data but also reason with logical rules. 

DL-Learner is particularly useful in domains where say for instance formal representation and reasoning are essential, so things like semantic web applications and knowledge-based systems for instance.

6) Apache Mahout:

Apache Mahout is a scalable machine learning library that has algorithms for the typical clustering and classification, but also recommendation mining. It integrates with big data platforms like Apache Hadoop and Apache Spark, which allows developers to leverage a more distributed computing landscape. 

Apache Mahout supports various machine-learning techniques, including collaborative filtering, clustering, and classification. It’s suitable for large-scale data analysis which is why it’s widely used in industries like e-commerce, social media, and anything that leverages personalized recommendations.

7) ADAMS:

Advanced Data mining And Machine Learning System (ADAMS), is a data-driven workflow engine, and an open-source, modular framework. When it comes to machine learning, ADAMS is great for data preprocessing and feature engineering to model training, evaluation, and deployment.

8) JSAT:

JSAT includes popular algorithms such as k-nearest neighbours, support vector machines, decision trees, neural networks, and more. One of the notable features of JSAT is its emphasis on parallel computing and performance optimizations. It leverages multi-core processors and implements parallel algorithms to speed up computations, making it ideal for managing large datasets. 

It’s also great in scenarios where data is high-dimensional and contains many zero values - which is something text-based applications, particularly natural language processing will benefit from.

9) JavaML

JavaML emphasizes two things: scalability and efficiency. It uses incremental learning which is particularly useful in scenarios where new data arrives consistently or when resources are limited. In addition to that, it integrates with the distributed computing framework Apache Hadoop, which enables the system to handle large datasets.

What’s Next?

A solid infrastructure is pivotal for organizations to get the most out of machine learning. In 2023, Java is a staple in the machine learning landscape, with ongoing advancements and developments. As we look to the future, integration with emerging technologies, expansion of libraries and frameworks, and collaboration and interoperability will shape the evolution of machine learning in Java.

Written By Ben Brown

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ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.

Artificial Intelligence’s David vs Goliath: Comparing Big and Small Generative AIs

There’s always going to be the notion with technology that bigger is better. The more powerful, the more capable, the more sophisticated – these are the qualities tied to larger and more complex systems. Yet, when it comes to generative AI models, the story isn’t as straightforward. There’s an interesting David versus Goliath dynamic at play between big and small generative AIs. Let me explain…

In recent years as we know, big generative AI models have garnered tons of attention and acclaim. Models like ChatGPT-3, with its 175 billion parameters, GPT 4 with an estimated 170 trillion parameters, or Midjourney with its large language and diffusion models as well as its comprehensive list of parameters; these models have demonstrated remarkable capabilities and can pretty much generate anything you want. They’re trained on massive amounts of data, that allow them to capture complex patterns and produce the outputs we value so much. Why is that important to know? Well, it’s instances like this that have earned big generative models such a grand reputation. 

On the other hand, we have smaller generative AIs. Right off the bat, these models have fewer parameters and less computational power, which might make them seem like underdogs compared to their larger counterparts. But make no mistake; they possess unique advantages that make them fierce competitors in the AI landscape.

Generative AIs in Action

One of the most notable advantages when it comes to small generative AIs is their efficiency. Due to their smaller size, they require less computational resources and can be deployed on devices that have limited processing power. This makes them ideal for applications that require real-time generation or that have strict resource constraints.

Think about a mobile app that generates customized images based on user prompts. Since a small generative AI doesn’t have to depend on a remote server, it can process prompts directly on the user's device. This eliminates the need for constant internet connectivity and reduces latency, resulting in a super responsive user experience. When you compare this to a big generative AI, it’s not as powerful in terms of the sheer scale and volume of outputs. However, a small generative AI model, in this case, embedded in a mobile app is independent, efficient, secure, and highly customizable which makes it a versatile tool, especially for something such as personalized image generation.

Where One Compliments the Other

Using a big generative AI, like that of ChatGPT does offer a lot more in terms of capabilities. If a multi-billion dollar corporation sat down and decided they wanted to develop an AI system with immense scale and resources that was going to revolutionize healthcare (For instance) a big generative AI model combined with a small generative AI model would be the ideal solution.

First off, the big generative AI model would be trained on vast amounts of medical data. With its scale and resources, it will capture complex patterns and relationships within the data, enabling it to provide advanced diagnostic support, predict outcomes, and assist in drug discovery and development.

However, deploying a system like this is going to require a lot of computational power and an infrastructure that can handle the sheer scale of data being processed. This is where the small generative AI model comes into play.

The small generative AI model is now embedded within medical devices, wearables, and mobile applications, which again, enables data processing in real-time. With that, it now analyzes patient-specific data, such as vital signs, symptoms, and lifestyle factors, to provide immediate feedback, personalized recommendations, and continuous monitoring.

Don't Compete - Balance and Complete

To break this down simply, the big model is the brain that processes and stores the information, and the small model is the hands that carry out the actionable. Achieving a balance between the two is simply leveraging the strengths of each and coordinating to ensure data can be exchanged easily between models. How do you enforce this? By following these 4 guidelines:

• Each model has clear tasks

• Protocols are in place to facilitate data exchange

• Workloads are distributed based on computational requirements

• The system is constantly monitored and being improved on

These actionables are very general and could be applied to any industry but they give you a sense of what it takes to achieve balance and coordination between big and small generative AI models. While the specific implementation may vary across industries, these general guidelines provide a framework for companies to start with.

The Results Generative AIs Deliver in a Business

Big or small, generative AIs deliver results, however, the size and complexity of the model will be a big factor in determining the quality of results attained. From what we know about big generative models, we know that they excel at generating high-quality content, predicting trends, optimizing systems, and driving innovation. By that same token with small models, we know their efficiency offers agility, responsiveness, and personalized experiences. They’re awesome for tasks such as personalized recommendations, interactive applications, and ultimately enhancing customer engagement.

The Takeaway

While big generative AI models have garnered attention for their remarkable capabilities and ability to generate high-quality content, small generative AI models shouldn't be underestimated. Businesses have a lot to gain by leveraging each but ultimately it comes down to the strategy you put behind them.

Written By Ben Brown

—

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.

11 Programming Languages to Integrate AI Into Your Insurance Platform

The insurance industry has long been a sector that relies on person-to-person interaction. When a customer calls up an insurance broker, they’re talking to someone who understands and can even sympathize with their needs. They’ll look over policies and agree on an ideal plan… We could go on but frankly, this is all pretty dry, even for a technology company.

Insurance is yet another sector that’s about to be turned on its head by Artificial Intelligence. Like many other major industries in 2023, the insurance sector has begun to realize the possibilities available through the use of AI and is now exploring ways to integrate this technology into its service delivery model. 

A good example can be seen from Pypestream which is sort of like Chat GPT meets your Insurance provider. Pypestream alone saw its revenue jump around 450% between 2021-2022 which gives a sense of the level of opportunity available in this market. 

Anyone could argue about the implications of AI and try to dance around it as much as possible. The point is these tools are efficient which is really what AI is all about - promoting efficiency through process automation. With that being said, the only way companies can truly embrace this change is by having the right software infrastructure in place that can support the service model. 

At ISU Corp, we have years of experience serving the insurance industry so we’ve seen a lot of different approaches and want to give our insights on some of the best programming languages that can assist companies when trying to integrate AI to their platforms.

Development Teams Handling AI Integration

Any development team taking on AI integration must be proficient with Machine Learning (ML) infrastructures and the programming languages that go into them. The IT Project Manager must ensure that they bring on developers who are proficient specifically with Python, R, and Java as these are the keys to a solid infrastructure. 

Now, this alone won’t do a lot for a business starting its digital transformation journey, so with that said, let’s dive into some of the best options for programming languages and what they do:

1) Python - When it comes to data manipulation, statistical analysis, and building machine learning models, Python is the top choice. Insurance companies can use Python to develop predictive models that analyze customer data and identify risks, which will also improve underwriting and pricing decisions.

2) R - R is another popular language that we’ve outlined before, it’s commonly used for data analysis and machine learning. R is great for statistical modelling, data visualization, and exploratory data analysis. In the case of an insurance company, they might use R to create advanced analytics models, such as predictive modelling and fraud detection.

3) Java - Yet another widely used language for enterprise applications that is known for its reliability and scalability. Insurance companies will use Java to build AI applications that need to process large amounts of data and integrate with existing systems.

4) C++ - C++ is a top choice language for AI and just building overall high-performance applications, which of course makes it ideal for developing AI algorithms that need to run quickly. Insurance companies are going to be using C++ to build models that analyze large datasets and make predictions in real time.

5) MATLAB - This is a programming language commonly used in data science and machine learning. It’s particularly useful for performing complex computations and creating algorithms for statistical modelling and predictive analysis which is essential for AI integration in any platform. For insurance companies, this can help with tasks such as fraud detection, risk assessment, and claims analysis.

6) Scala - This is certainly a versatile programming language as it combines object-oriented and functional programming paradigms. It’s designed to be concise, expressive, and scalable, which makes it ideal for building large-scale, distributed systems. For insurance companies, this can help with tasks such as processing large volumes of data, managing complex workflows, and implementing real-time analytics.

7) Julia - Specifically designed for scientific computing and numerical analysis. Julia is very easy to use and has a syntax that is similar to MATLAB, which makes it a popular choice for data science and Machine Learning applications. Insurance companies might use Julia to help with tasks such as actuarial analysis, risk modeling, and predictive modeling.

8) Swift - Swift is a programming language developed by Apple for building iOS and macOS applications. It is a fast and efficient language that is designed to be easy to learn and use. For insurance companies, this can be useful for developing mobile applications that integrate with AI platforms, such as chatbots for customer support or mobile apps for claims submission.

9) Go - Go was created by Google to build fast, scalable, and reliable software. It has a simple and efficient syntax which makes it easy to write and read code. For insurance companies, this will be a great tool when building distributed systems, processing large volumes of data, and implementing real-time analytics.

10) Ruby - Ruby is a programmer's best friend during web development. Like the majority of these languages, it too has a clean and easy-to-learn syntax. For insurance companies, this is going to be the go-to when building web-based applications meant to integrate with AI platforms, like chatbots or processing systems for web-based claims.

11) Kotlin - Kotlin is a programming language that was developed by JetBrains for building Android applications. It is designed to be a more concise and expressive alternative to Java, with features such as null safety and extension functions. For insurance companies, this can be great for building mobile applications that integrate with AI platforms, again, this includes chatbots or mobile claims processing systems.

Choosing The Right Tools For Your Insurance Company

Artificial Intelligence is something that demands businesses' full attention at this moment in time - we strongly believe that companies who fail to implement AI solutions in their service delivery models will be obsolete in 5 years. 

With that said, just like AI, programming languages are meant to do different things to produce different outcomes which will all be based on the company's vision. The whole point of custom software development is to create solutions that fit the specific needs and objectives of YOUR business. 

Software developers can’t necessarily come up with the dream for you, but they can bring yours to life with the right tools. We can also identify the gaps in your processes that can be filled with AI. This is something insurance companies will want to make sure they’re aligned with when consulting with Artificial Intelligence software architects.

Written By Ben Brown

—

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.