In today's tech and innovation-driven world, there is a high demand for AI engineers. The AI industry is expected to grow nearly 20x by 2030 according to statistics obtained from BlueTree. This sky-rocketing demand comes with a demand for more AI Engineers.

In this article, we are going to explore the top 6 tools that can make you stand out when it comes to doing AI Engineering tasks and how they can help you complete tasks faster. Keep in mind having a powerful toolkit can be a big difference between coding and creating cutting-edge solutions.

Ready to level up? lets dive right into the future of faster AI development and task completion:

1. Taipy - Build Python Data & AI web applications faster

Taipy is an open-source library for building Python-based data and full-stack AI applications. With Taipy, you can design GUIs for your AI and data-driven application with many interactivity controls and visuals, develop the backend, and come up with production-ready web applications in a shorter period of time.

This tool comes with support for data integration, tasks orchestration using Taipy Scenarios, What-if analysis, scenario-based problem solving & data management.
As an AI Engineer, you need to analyze data using various tools before you use it for training your AI model, and after that another long and time-consuming model deployment and API development before you create the frontend for your AI app. Taipy helps you do all this faster than you could have expected, from building your model in the backend to designing and developing an appealing and user-intuitive frontend for your applications while keeping performance at its peak, unlike other libraries that get slower when dealing with larger datasets.

Some of the sample work done using Taipy includes a Sales dashboard, tweet generation, background remover, emotion detection, real-time face detection, and so many more. You can explore them at Taipy demos to get your hands dirty and master how to use Taipy.

Taipy brings you a VS Code extension, Taipy Studio which you can use to build your Taipy configurations and define GUI page content in Markdown syntax as well as design dataflows.

Taipy accepts the integration of Machine Learning and AI development tools like Sci-kit learn, Tensorflow, Huggingface, and all the AI development algorithms. It also integrates with leading data and AI developer platforms.

To date, Taipy has a huge appreciation and following on Github evident with its 13.3k stars. To get on board the list of Fast AI Engineers, dive into the Taipy Getting started docs and learn more.

2. Streamlit - A faster way to build and share data apps

Just like Taipy, Streamlit is also an open-source Python framework developed to simplify and fasten the development of AI and machine learning applications. With Streamlit, you as an AI engineer can turn a dataset into an interactive and fully-fledged application faster and share with clients. This is easy as you can develop frontend applications for your AI solutions even without having prior knowledge of frontend development skills like React, Javascript, and CSS.

Streamlit accepts integration of machine learning models, data-driven insights, and data visualization tools. This makes it a perfect tool for AI Engineers as there will be no need to install more libraries for visualization purposes and frontend development. Some of the AI tools it is compatible with include PyTorch, Matplotlib, Keras, and many more.

Streamlit has a tonne of demos that could help you get started with the journey of being a faster AI Developer and Engineer. These demos show you some of the basic things you can do with Streamlit.

Also, note that Streamlit applications are easier and free to deploy into production.

Streamlit also allows the developer to create custom UI components and share them with the community or even for personal use hence ensuring uniqueness.
To get your hands dirty with Streamlit, dive into the docs right here: Streamlit docs only for faster AI Engineers and Developers.

Now you could be wondering, What's the difference between Streamlit and Taipy? Here goes your answer, Streamlit is mainly used for building the frontend of AI applications while with Taipy, you can build both the frontend and backend. Simple!!!

3. Gradio - Build & share delightful machine learning apps

When talking of 10x better AI Engineering, it's unethical to leave out Gradio. It is similar to Streamlit but with tonnes of unique features. With Gradio, you can create simple and interactive web interfaces for your AI models with fewer lines of code. It also comes with prebuilt UI components for model performance visualization, prediction visualizations, and many more.

Gradio is mainly used to create demos for Machine Learning and AI models. This reduces the time that would be spent developing frontend and backend applications with related model APIs so as to present what a client can understand, with Gradio you develop what a client understands faster.

This tool comes integrated with Python and it is also compatible with TensorFlow, PyTorch, and Scikit-learn, the most popular Machine Learning and AI development tools. If engineers at some of the most innovative AI companies are using it, why not hop on the team of faster AI developers?

To get started with Gradio, explore the Gradio docs and become 10x faster at what you have been doing. For sample demos, explore the Gradio Playground.

4. Dash - Put data & AI into action by creating scalable, interactive data apps for your business

Dash, a tool developed by Plotly, is powerful for developing analytical applications in Python. As an AI Engineer developing analytical models is one of your roles. This tool makes all that simple for you.
With Dash, you can develop interactive dashboards and apps with Python and no need for CSS or JavaScript. Dash applications also give a point-and-click interface to models written in Python, vastly expanding the notion of what's possible in a traditional "dashboard." With Dash apps, data scientists and AI engineers put complex Python analytics in the hands of business decision-makers and operators.

Some of the notable use cases of Dash include Predictive analytics and forecasting, developing Generative AI apps, image and audio recognition apps, and many more. Click here to explore various demos.

Dash also supports API integration that enables Dash apps to interact with multiple systems and as well share data.
Dash boasts of 21 Github stars which shows how much people are appreciative of what it does. To get on board the team of faster AI engineers, utilizing the capabilities of Dash alone could take you a step ahead of many AI Engineers, start here in the Dash Guide.

5. Flask - Build Python Backend for AI-driven applications faster

Flask is a lightweight Python framework that provides the capabilities of building web applications with a simple and minimal setup. Here you choose how you are to build your frontend be it using Javascript and CSS or utilizing Streamlit and Gradio after developing the model API via REST APIs with Flask.
This tool is popular in the development of AI web applications due to its compatibility with AI development tools like Tensorflow, PyTorch, Scikit-learn, and many more. It also offers scalability of AI-driven web applications. It is an alternative to Django which is a heavy and comprehensive Python framework.

For AI developers who want to develop the backend logic for their AI-driven web applications faster, using Flask would be the best choice over other backend development frameworks that are heavier and more complex to use. To join the faster AI Engineers team, get into utilizing the capabilities of Flask here --> Flask Quick Start Guide

6. AI-Flow - Connect multiple AI models easily

AI-Flow is an open-source framework for creating custom AI tools through a simple drag-and-drop interface. With AI-Fow, you can connect different AI models from ChatGPT, Llama, Claude, Mistral, and many more to offer various AI model capabilities to the tool you are developing. Here, you do not need time to master a programming language as it is a no-code platform.
Within a blink of an eye, you will have your tool powered by some of the greatest AI models, up and running. This tool is for those AI Engineers who are interested in developing products for the market faster.

To get started with AI-Flow, here is a Quick Guide.

Conclusion

These are not the only tools that could provide superspeed development for AI projects. There are many more, and if you have come across some, let's discuss them in the comments.

I hope you found the ones I shared here helpful, if you haven't tried out any yet I suggest you try them.

Have a great day! Until next time.