Software engineering entails creating algorithms that can solve deterministic issues using logic. However, because of the recent rise of AI, the way software engineers create codes has altered.
AI tools have transformed the programmer’s perspective because of the advancement of artificial intelligence and machine learning in software development for problem-solving.
Artificial intelligence has aided in processing vast quantities of data. It is an application of separate technologies working together to enable the computer to execute operations normally reserved for people to expedite the development method for a programmer. And if you’re looking for AI Development Tools for streamlining your development procedure, you’re at the right place. In this article, we’ve reviewed the finest AI development tools in the market.
Let’s get going!
What are AI Development & Coding Tools?
AI Development Tools are software applications and frameworks specifically designed to support the development of artificial intelligence (AI) systems. These tools provide developers with various functionalities, such as data processing, model training, testing, and deployment, which are critical for building robust and effective AI and Machine Learning applications.
AI development tools are essential for reducing the time and effort required for building AI applications. They offer a standard platform for developers to work on and ensure that the models are scalable, modular, and maintainable. Moreover, with these tools, you can get a wide range of resources such as documentation, tutorials, and forums, which make it easier for developers to learn and collaborate with the community.
How Is AI Used in Software Development?
Automated debugging is facilitated by artificial intelligence tools and software, namely pattern recognition for comprehensive error analysis, identification, and classification. The debugging process is sped up, and errors are more readily detected using the deep learning system. In addition, it significantly lessens the burden of code generation. If an AI-based software learns the fundamental patterns, it can construct a whole program simply by assembling a set of pre-set modules, much like LEGO blocks are assembled.
Artificial intelligence software assistants are becoming more popular due to their ability to accelerate the software development process intelligently. Even for newcomers, it speeds up the learning process. AI assistant tools may act as a tutor by suggesting solutions, providing code samples, and even preventing rookie errors like forgotten closing brackets. Furthermore, AI tools can execute the testing by reviewing the historical logs and generating a set of test cases to be executed on the system. Without doing the tests, it can also accurately anticipate the results.
What Can AI Do in Software Programming?
Code Generation: AI can generate code based on the requirements and specifications provided by the programmer.
**Code Optimization: **Automatically identify and eliminate inefficiencies in code. This can include identifying redundant or unnecessary code, optimizing algorithms and data structures, and improving memory usage. AI can also be used to predict and prevent potential performance issues before they occur. Additionally, it can automatically generate optimized code based on specific performance goals or constraints.
Bug Detection: One of the most common ways of AI bug detection is through the use of machine learning algorithms that can analyze code and identify patterns that are indicative of bugs or errors. These algorithms can be trained on large datasets of code to learn what types of errors are most common and how to identify them. Another way that AI can be used in bug detection is through the use of natural language processing (NLP) algorithms. These algorithms can analyze bug reports and other documentation to identify common patterns and keywords that are associated with specific types of bugs.
Code Completion: AI can suggest code completion options based on the context and the developer’s coding style.
Testing: Automating the testing process by generating test cases and identifying potential issues. Increasingly intelligent software testing capability to drive test execution, qualify and reproduce issues reliably, shortening the development cycle and ensuring higher quality results.
Code Review: Automating code reviews and performance optimization – using parameters that are machine-learned and avoiding human-driven repetitive regression and performance tests.
Top 6 AI Development & Coding Tools Widely Used in 2023
Want the best AI development tools for your SDLC?
These tools can help:
1.MxNet
Mxnet is a powerful and adaptable deep-learning library. Researchers and engineers from several institutions and businesses can work together on MXNet. MXNet’s extensive Python application programming interface (API) suits Python’s large user base. When used by developers, the sophisticated tools provided by MXNet unlock the full potential of GPUs and cloud computing. While these methods and tools can be applied to various mathematical computations, MXNet focuses particularly on facilitating the creation and deployment of massively scaled deep neural networks.
MxNet’s Key Features
- Provides flexibility and support through a hybrid front end that can easily switch between an imperative and symbolic mode.
- Research and production can use scalable, distributed learning and performance optimization.
- Scala, Julia, Clojure, Java, C++, R, and Perl support, and tight interaction with Python.
- Has applications in computer vision, natural language processing, time series, and more.
Supported Platforms: Windows, Mac, and Linux
Pricing: Free to use
TensorFlow is a set of machine learning frameworks, tools, and resources. A large community of academics globally relies on this framework to create cutting-edge ML models.
It is a free and publicly available machine learning library for creating reliable and effective ML-based software. Because of this tool, developers can use more than just the features offered by individual vendors to address machine learning challenges.
TensorFlow’s Key Features
- Distributed model training across several devices is made possible by DTensor in TensorFlow.
- Provides compatibility with both Parallel spatial training and parallel training utilizing data partitioned across dispersed devices.
- Allows rapid debugging and model iteration with support for high-level APIs like Keras, simplifying model creation.
- Offers a set of resources aimed at streamlining developer work on AI projects.
Supported Platforms: Windows, Mac, and Linux.
Pricing: Free to use
3.H20
H2O is a cloud-based AI platform that allows any data analyst, developer, or data scientist to create high-quality AI models and apps in minutes, swiftly delivering AI to business customers. Before, this process may take months or even years. But its powerful automated ML features allow cutting-edge AI models and programs to be easily developed.
H2O’s Key Features
- Consists of a user-friendly AI app store that makes it simple for developers to deploy new apps to clients
- The models you build with this tool are portable between on-premises, cloud, and other infrastructures.
- Converts text to useful features utilizing techniques for extracting document characteristics like TFIDF, CNN, and GRU.
- Includes Superior ML models for spotting deceptive practices in massive data sets of transactions for application in real-time security monitoring.
Supported Platforms: Windows, Mac, and Linux.
Pricing: Free to $30 per month
4.Mintlify
By automation, and more especially automated documentation generation, Mintlify hopes to solve the problems associated with documentation. Using tools like NLP and web scraping, the company’s platform can read code and generate documentation that describes it. Just highlighting the code will trigger this tool to generate the documentation process.
It saves time and improves your codebase by creating documentation. Moreover, Mintlify saves senior developers time in the future when explaining their code to others.
Mintlify’s Key Features
- Improves the document’s readability by scanning for “outdated” sections and detecting user interaction.
- It doesn’t keep any code and encrypts all user data while stored and in transit.
- Includes support for integration with well-known tools like Slack, Dropbox, and GitHub to streamline and automate development and project management processes.
- Includes a user base of 6000+ active accounts.
Supported Platforms: Windows, Mac, and Linux
Pricing: Free to $120 per month
Scikit Learn (also known as sklearn), a popular open-source machine-learning library, is designed to provide a range of supervised and unsupervised learning algorithms for classification, regression, clustering, and dimensionality reduction tasks.
Scikit-learn provides a user-friendly and efficient API for building machine learning models. It includes a wide range of pre-processing techniques for handling missing data, scaling features, and transforming data into a suitable format for modeling.
Scikit Learn’s Key Features
- Includes a range of model evaluation techniques for assessing the performance of the models, such as cross-validation and grid search.
- Can be easily integrated with other tools and libraries in the Python ecosystem.
- Contains many computations for common artificial intelligence and data mining tasks, such as bunching, relapsing, and order.
- Built on top of other popular scientific computing libraries for Python such as NumPy, SciPy, and matplotlib.
Supported Platforms: Windows, Mac, and Linux
Pricing: Free to use
6.Rainbird
Rainbird is a cutting-edge artificial intelligence platform with a short history. The Rainbird suite includes the Rainbird Infer module, the Rainbird Flow module, and the Rainbird AutoML module, all of which may be accessed via the Rainbird Studio interface. You can use the tool’s brand-new artificial intelligence component Rainbird AutoML with machine learning for model training and deployment.
Rainbird’s Key Features
- Uses a robust inference engine to automate the decision-making process.
- You can get better quality forecasts using the results of the trained model with Rainbird Infer.
- The users can generate visualizations of the decision logic with the help of the knowledge maps.
- Allows for the development of comprehensive approaches for complex decision-making.
Supported Platforms: Windows, Mac, and Linux
Pricing: Free to use
For more AI development and coding tools, please check check my this blog post. In addition to the six tools already mentioned above, it also introduces:
- Tabnine
- Otter.ai
- PyTorch
- GitHub Copilot
- Wing Pro
Over all, Artificial intelligence (AI) has revolutionized the way we develop software. With the rise of AI-powered software development tools, developers can streamline the development process and create high-quality applications faster than ever.
A plethora of AI Development Tools are available, from data processing to natural language processing, and we hope you’ve found the best one after reading this article.
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