6 Best Machine Learning Tools to Leverage for Software Development.
Summary:
Gone are the days of just welcoming customers to your brick & mortar store without knowing anything about their preferences, whereby chances of conversions — close to zero. But with Machine Learning tools, gathering a wealth of information about your customers and leveraging online platforms to drive more sales is possible. Get to know those outstanding ML tools and look for the benefits each tool offers.
Table of Contents:
1. Introduction
2. 6 Best Machine Learning Tools
3. Beyond the Core 6: Explore Additional Popular Machine Learning Libraries
4. Conclusion
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.
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 used Amazon's ML tool to detect unusual patterns in transactions. As a result, the bank could prevent fraudulent activities, securing the accounts of its customers.
5. Google Cloud Machine Learning Engine:
Google Cloud Machine Learning Engine, a cloud-based Machine Learning service that allows developers to build and deploy ML models. It supports popular frameworks like TensorFlow and scikit-learn, providing a scalable and flexible environment for machine learning.
Key features of Google Cloud ML Engine:
- Scalability: You can easily scale your training jobs and prediction requests based on your needs.
- Integration with TensorFlow: This allows you to build and train your models using TensorFlow, a popular open-source machine learning framework.
- End-to-end Platform: Google Cloud ML Engine provides an end-to-end platform for building, training, and deploying machine learning models.
Real-Life Use Case of Google Cloud ML Engine Example: Airbus (Aerospace)
Airbus utilized Google Cloud ML Engine for predictive maintenance of aircraft components.
How has Airbus leveraged Google Cloud ML Engine?
Predictive Maintenance: Airbus used machine learning models to predict maintenance needs for various aircraft components. This allowed them to schedule maintenance proactively, reducing downtime and increasing overall operational efficiency.
6. IBM Watson Studio:
IBM Watson Studio is a comprehensive platform for data scientists, developers, and subject matter experts to collaborate and operationalize AI. It supports various machine learning frameworks and languages, making it a versatile choice for building and deploying machine learning models.
Key features of IBM Watson Studio:
- Collaborative Environment: Watson Studio provides a collaborative environment for data scientists and developers to work together on building and deploying machine learning models.
- Open Source Integration: It supports popular open-source machine learning frameworks like TensorFlow, PyTorch, and scikit-learn, allowing flexibility in model development.
- AutoAI: Watson Studio includes AutoAI capabilities, enabling users to automatically generate machine learning models with minimal effort.
Beyond the Core 6: Explore Additional Popular Machine Learning Libraries:
While the above-mentioned Machine Learning tools are powerful and widely used, there are also several popular machine learning libraries that developers frequently use to enhance their models. Some of these libraries include:
- TensorFlow: An open-source machine learning library developed by the Google Brain team. It is widely used for building and training deep learning models.
- PyTorch: An open-source machine learning library developed by Facebook. It is known for its dynamic computation graph and ease of use.
- scikit-learn: A popular machine learning library for classical machine learning algorithms. It provides simple and efficient tools for data analysis and modeling.
- Keras: An open-source high-level neural networks API written in Python. It is known for its user-friendly interface and seamless integration with TensorFlow.
Conclusion:
In conclusion, Machine Learning tools play a crucial role in various industries, offering predictive analytics, pattern recognition, and automation capabilities. The six highlighted tools, Azure Machine Learning, Neural Designer, Microsoft Cognitive Toolkit, Amazon Machine Learning, Google Cloud Machine Learning Engine, and IBM Watson Studio, cater to different needs and preferences. Each tool comes with its unique features and has been successfully applied in real-life scenarios by companies like Rolls-Royce, Uber, Bank of America, and Airbus.
Whether you are looking for a user-friendly interface, advanced deep learning capabilities, or cloud-based scalability, there is a Machine Learning tool to suit your requirements. Additionally, exploring popular machine learning libraries like TensorFlow, PyTorch, scikit-learn, and Keras can further enhance your machine learning models. As technology continues to advance, staying informed about the latest tools and libraries in the field of Machine Learning is essential for businesses aiming to leverage data-driven insights for success.