5 Best Machine Learning Tools for Software Development

“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.

#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

Concluding our list of top Machine Learning tools is the Google Cloud AI platform, a powerful tool that allows you to train and host your Machine Learning models in the cloud. Not only does this platform enable you to analyze data and make predictions using the trained models, but it also emphasizes the significant impact that the blend of Artificial Intelligence (AI) and Machine Learning (ML) has on improving customer experience.