6 Best Machine Learning Tools to Leverage for Software Development

Table of Contents

Introduction

Machine Learning is a subset of Artificial Intelligence that works entirely on data; leveraging it to predict future outcomes is where Machine Learning excels.

You must have noticed that once you watch a thriller genre web series on Netflix, the next time you open Netflix, it suggests similar kinds of shows.

Or, you must have even received proactive notifications of the call on Truecaller before the actual call; this is a game of Machine Learning tools and algorithms.

Both the above examples are part of some Machine Learning software tools, be it Azure or Amazon, Google, or it can be IBM Cloud. And regarding Machine Learning in software development, it delivers customized experiences to hook customers. It offers predictive models to forecast upcoming trends and ensure automation with autonomous systems to outstanding anomaly detection mechanisms.

Machine Learning leaves no stone unturned to deliver desired results, from big names to startups. So, what are you waiting for? Read this blog for more details on Machine Learning (ML) tools and see what benefits different industries have enjoyed.

6 Best Machine Learning Tools

Undoubtedly, several ML tools are available online, but what matters is which one suits your software and what dramatic changes these tools for ML bring to your business. Considering how to overcome the challenges you’re currently facing in your software and deliver desired results, here are the six best Machine Learning tools for you.

1. Azure Machine Learning

Microsoft’s Azure Machine Learning software is an excellent choice for developers due to its drag-and-drop interface and easy learning curve. Apart from this, Microsoft offers multiple Machine Learning platforms that you can adopt as per your requirement.

But when we talk about entrepreneurs, decision-makers, or product owners specifically, Azure Machine Learning is worth being on top of this list. Its extensive list of features ensures a competitive edge for your business. Besides, the development time of Machine Learning models is half what it used to be, making it an economical choice.

Below are a few unique features of Azure Machine Learning Software Tool for you:

• Predictive Analytics: Azure ML helps business owners forecast demand, sales, and customer preferences. Such predictive capabilities help them to allocate resources, plan business, and manage inventory efficiently. As a result, you can be well-prepared with accurate costing to optimize business operations.
• Azure ML Designer: Data preprocessing, feature engineering, model training, and deployment become easier with Azure Machine Learning. It fastens the development process due to its no-code feature, meaning the developers just have to drag and drop modules, resulting in faster time to market.
• Power BI Integration: This Azure ML tool feature helps product owners integrate Power BI with Azure ML. It results in achieving real-time reports and insights.

Real-Life Use Case of Azure Machine Learning Tool Example: Rolls-Royce (Automobile)
Rolls-Royce found Azure Machine Learning to be a result-driven tool, which is why it leveraged one of its prominent features – Predictive Maintenance. They utilized it for measuring jet engines’ shelf life.
What did Rolls-Royce do with Predictive Maintenance?
Using predictive maintenance, Rolls-Royce gathered real-time data on jet engines, analyzed, and predicted the chances of its failure.
How has Predictive Maintenance benefitted Rolls-Royce?
The car and aero-engine manufacturing company could schedule maintenance proactively before the engine ran out of operating condition. As a result, the company was able to keep the engine ready, prevent downtime, and decrease maintenance costs significantly.
Benefits enjoyed by Rolls-Royce by leveraging Azure ML
✅ Data Collection: Collected data using several sensors, including vibration sensors and pressure sensors.
✅ Machine Learning Algorithms: Leveraged anomaly detection to predict impending failure & prognostics to predict the shelf life.
✅ Predictive Maintenance: Ensured the long shelf life of engines by scheduling engine maintenance and eliminating downtime.

Rolls-Royce Enjoyed Perks of ML Tools: What Are You Waiting For?
Irrespective of your industry, these ML tools assure solutions to all business challenges. Hire software developers and understand how to uplift your business with ML tools.

2. Neural Designer

This is one of the machine learning tools based on neural networks. Just like other ones on the list, this is also a Graphical User Interface tool, making data entry and results interpretation easier. You can leverage Neural Designer to recognize patterns, forecast possibilities, and unveil relationships between data.

Below are the unique features of the Neural Designer Machine Learning tool, making it a game-changer for you:

• Non-Linearity: This feature captures complex relationships available in the data, with which it will be easy to extract worthy insights & patterns.
• Pattern Recognition: Neural Designer offers anomaly detection, fraud prevention, and predictive maintenance through which there will be efficiency improvement and secured business.
• Deep Learning: A simplified way to learn complex data without manual feature engineering saves time for entrepreneurs.

Real-Life Use Case of Neural Networks Tool Example: Uber (Transportation)
What did Uber do with Neural Networks?
Uber leveraged ML models empowered by Neural Networks to predict demand for rides. Upon getting insights, Uber further segregated the demand based on weather, records, and events.
How has Neural Network benefitted Uber?
✅ Uber monitored the demand for rides in different areas during different hours of the day. Such tracking helped Uber to identify similar patterns, which further became helpful in predicting future ride demands from a particular area at a particular time.
✅ Besides, Uber even monitored ride demand during different weather. This has helped them know that a rainy day can make Uber the talk of the town.
✅ Lastly, Uber benefitted from events since they noticed a surge in ride demands during concerts or events.

3. Microsoft Cognitive Toolkit

In the middle of our best Machine Learning software tools list, there comes Microsoft Cognitive Toolkit. This is another Microsoft AI solution after Azure Machine Learning with a different machine training methodology. It uses deep learning algorithms to train machines and handles data from C++, Python, and other programming languages.

An open-source toolkit that helps build, train, and deploy deep neural networks. This machine learning software toolkit has various uses, such as:

• Image Recognition: You can leverage image recognition to detect objects in images. Besides, various uses include facial recognition, image classification, and object recognition.
• Natural Language Processing: This feature can be leveraged for sentiment analysis, question answering, and text translation.
• Speech Recognition: Lastly, this feature by Microsoft Cognitive toolkit can be leveraged for dictation, transcription, and voice control.

4. Amazon Machine Learning

One of the best Machine Learning software on the list is built by Amazon, which helps developers to develop, deploy, and train Machine Learning models.

Through Amazon’s Machine Learning tool, you can:

• Create & train Machine Learning models using Amazon SageMaker Ground Truth & Autopilot.
• Prepare your data for ML by leveraging Amazon SageMaker Automatic Data Preparation.
• Deploy ML model to production.
• Track ML model performance.

Real-Life Use Case of Neural Networks Machine Learning Tool Example: Bank of America (Banking)
Bank of America leveraged AML to improve credit risk assessment and fraud detection.
How has Bank of America leveraged Amazon Machine Learning?
✅ Credit Risk Management: The bank-built ML models to predict the loan default of customers. This way, the Bank made informed decisions and noticed a loss reduction.
✅ Fraud Detection: Bank of America leveraged AML models to predict malicious transactions, preventing fraud and financial loss.
What challenges does Bank of America face without AML?
✅ Data Quality: While implementing Amazon ML, it was the sole responsibility of Bank of America to maintain the data quality, for which it ensured the cleaning and formatting of the data.
✅ Model Development: For training Amazon ML, Bank of America ensured ML algorithms and techniques and developed accurate ML models.
✅ Model Deployment: The bank had to ensure scalability and reliability for deploying ML models in a productive environment.
What benefits did Bank of America enjoy by leveraging Amazon’s Machine Learning?
✅ Improved credit risk assessment models by 10% and noticed a massive loss risk reduction of $100 million.
✅ Detected 50% of frauds proactively and prevented a $50 million financial loss.

5. IBM Cloud

IBM Cloud is a hub of cloud computing services, enabling both Platform-as-a-Service(PaaS) and Infrastructure-as-a-Service(IaaS). Another great feature of IBM Cloud is it helps develop, run, and manage apps on-premises and public cloud.

IBM Cloud offers a list of services useful for you as an entrepreneur, businessman, or startup:

• Computing: Offer virtual servers to run apps and workloads, and IBM Kubernetes for deploying and managing containerization apps.
• Databases: Offers several databases like IBM Db2, IBM Cloudant, PostgreSQL, and MongoDB.
• Analytics: Enables AI services using IBM Watson, including language processing, image recognition, and language understanding capabilities.
• Internet of Things: Gather, organize, and analyze data through IoT devices. Together, Machine Learning and IoT are revolutionizing businesses in many ways.
• Security: Offers security services like IBM Cloud Identity, Access Management, Key Protect for Key Encryption, Cloud Security, and Compliance Center.

Real-Life Use Case of IBM Cloud Machine Learning Tool Example: UnitedHealth Group (Healthcare)
UnitedHealth Group is a Fortune 500 healthcare company leveraging IBM Cloud Machine Learning for various purposes.
✅ Predictive Analytics: To identify chronic diseases, measure risk, and implement preventive measures.
✅ Fraud Detection: Used IBM Cloud Machine Learning for detecting frauds from EHRs and benefitted by reducing the number of frauds.
✅ Chatbots: Leveraged Chatbots to make the customer experience delightful and stress-free.

6. Google Cloud AI Platform

Closing our top Machine Learning tools list with the Google Cloud AI platform that enables training and hosting your Machine Learning models in the cloud. Besides, this AI platform by Google also ensures that the trained models predict after analyzing the data. The combination of AI and ML plays a pivotal role in enhancing customer experience.

These are some unique features of the Google Cloud AI Platform:

• Unified AI Platform: Easy to develop and deploy ML models and implement them for desired functions.
• Pre-trained ML Models: ML models can be used for image classification, NLP, and fraud detection.
• AI Pipelines: Automates ML lifecycles, saving time in building and deploying ML models.

Real-Life Use Case of Google Cloud AI Platform Machine Learning Tool Example: Amazon (Retail)
Amazon, an online marketplace, has optimally leveraged the Google Cloud AI Platform. From product recommendation to fraud detection and image recognition, Amazon ensures everything remains streamlined.
How has Amazon leveraged the Google Cloud AI Platform?
✅ Product Recommendation: This feature of Google Cloud AI recommends products based on their purchase history.
✅ Image Recognition: Identifies objects in images and helps in product categorization, facial recognition, and scene understanding.
✅ Fraud Detection: Protects customers from financial fraud by leveraging ML tools.

Explore a World of Opportunities
Hire ML engineer to develop and deploy ML models that ensure desired result-driven solutions. Harness the power of machine learning and overcome modern-world challenges.

Beyond the Core 6: Explore Additional Popular Machine Learning Libraries

In the realm of Machine Learning tools, the previously mentioned six hold a significant position in the market, not just due to their association with well-known tech giants but also because of their capabilities in solving crucial issues and ensuring a seamless customer experience with your software.

However, the market offers even more options. This is where software consulting company become essential, helping businesses navigate the complex ML landscape. By providing tailored guidance, their experts ensure that companies choose and implement the right tools and libraries to meet their unique needs, driving efficiency and innovation.

Let’s delve into some of these libraries to ensure a comprehensive understanding.

1. TensorFlow

This ML tool helps in image classification, facial recognition, and speech recognition and can implement recommendation systems.

2. Keras

A machine learning tool capable of executing object detection models, streamlining data augmentation, and creating generative content.

3. PyTorch

What amazes us about PyTorch’s machine-learning library is its decision-making capabilities and game-playing skills.

4. Scikit-Learn

Scikit-Learn enables leveraging algorithms for data prediction and interpreting it for decision-making.

5. Matplotlib

Matplotlib enables interpreting data through histograms. Besides, it even helps with heatmaps to understand customer behavior.

6. NumPy

Data visualization or manipulation is easily enabled using NumPy. This ML library is in demand for data analysis and numerical computation.

7. SciPy

SciPy is high performing ML library that computes large datasets and simplifies CPU-intensive tasks.

8. Theano

Theano is flexible machine learning software that works amazingly well with other ML tools like Keras.

9. Pandas

This library can prepare data and interchange between libraries. With Pandas, you can manipulate data and promote computations.

10. MLPack

This one also helps build recommendation systems and detect & prevent anomalies in your software.

These are just a few use cases of particular machine learning libraries. You can delve deeper into each of them for detailed information.

Conclusion

Frequently Asked Questions (FAQs)

Why use Machine Learning tools?

Machine Learning tools are essential to leverage because they put you one step ahead of your customers by letting you know what they seek. Presenting them with what they need or preventing them from troubles is where Machine Learning tools help and assure a high customer retention ratio.

Which is the best Machine Learning software tool?

The answer to the question depends on the problem you want to solve with the tool. All the Machine Learning softwares are investment-worthy; however, you must identify the challenges and select the best solution.

Can I implement a Machine Learning tool in my existing software?

Yes, a machine learning tool(s) can be integrated into the existing software. All it requires is first to analyze the requirement of ML in the process, and then it can be concluded which tool would be the most suitable ML tool.

What would be an estimated cost to integrate Machine Learning functionalities in software?

The cost to integrate machine learning functionalities depends on several factors like use case, time to market, availing subject matter expertise, and many more. You can definitely get in touch with our adroit ML engineer for further technical details.