Summary: AI frameworks are toolkits that simplify building AI applications. They offer pre-built components and features, saving development time. Popular frameworks include TensorFlow, PyTorch, and LangChain. Choosing a framework depends on your business needs and project goals. Consulting an AI development team can help make the best choice.
You are rushing up and down the grocery store aisles after a busy day at work. You have a list of things you’re looking for in mind but can’t remember them. Your phone chimes, and you see an AI Assistant notifying you with a grocery list. It recommends food items based on your past eating habits and dietary intake. You are relieved in a moment, complete your grocery, and head home to relax.
An AI framework was employed to perform this intricate task. The scenario also highlights AI’s ability to learn behavior, process data, and streamline day-to-day human interactions. By using artificial intelligence frameworks, humans are making their lives easy and efficient at the same time.
In this blog, we will look at what AI frameworks are, what makes them useful, their main types, and future trends to pursue.
What are AI Frameworks?
You might be thinking of developing a complex neural network from scratch to improve your application’s search suggestions. Not quite! AI frameworks are the quick fix.
Essential toolkits for developers: these frameworks provide prebuilt code components that handle various stages of AI development. You can refer to them as links to the same long chain that simplifies the AI integration process.
By using AI frameworks, your team can leverage powerful algorithms like deep learning and machine learning without the need to code everything from scratch. This saves development time and gets your improved search results to market faster.
Why Use an AI Framework for Your Project?
Building AI products can be complex, but AI frameworks offer a helping hand for developers. Here are three key benefits:
- Pre-Built Tools: AI frameworks come with ready-made tools and features, so developers don’t waste time building everything from scratch. Uniquely, it helps developers to focus on the real things and avoid wasting their crucial time.
- Unified Approach: When multiple developers work on a project, different ideas can pop up. AI frameworks act like a set of instructions, ensuring everyone uses the same coding style and similarly approaches problems. This keeps things organized, improves code quality, and keeps everyone in the loop.
- Smarter Use of Resources: Time is money, and AI frameworks help developers save both. By offering pre-built tools and streamlining the process, they reduce development time and keep project costs under control. Additionally, popular frameworks have large communities of developers who share knowledge and best practices. This means your developers can learn from others and build even better AI products.
Open-source AI frameworks
There are a bunch of open-source AI frameworks. We’ve picked only the three most popular ones based on their Google search trends and GitHub data.
- TensorFlow
Developed by Google and first released in 2015, TensorFlow is a purpose-built AI framework. It is open-source and works well for training and prediction-oriented tasks. Trained primarily in Python, TensorFlow allows developers to create multi-layered neural networks. More so, It is super flexible with easy deployment across a variety of platforms.
Application:
Since its release nine years ago, it has been used to build products in the
- healthcare industry,
- language translations,
- eCommerce apps,
- And facial recognition.
Tensor gets its name from its ability to process data in the form of matrices, arrays, and vectors. Another plus side of this framework is the adaptability to both simpler and complex tasks.
To use TensorFlow for your next project, a steep learning curve is compulsory. Moreover, complex and intricate modules are another disadvantage this framework comes with. Lastly, Google is working on a new AI framework, JAX, that can make TensorFlow obsolete.
- PyTorch
Facebook’s AI Research Lab introduced this open-source ML framework. It has detailed libraries and a line-up of tools in its toolbox. Data parallelism is when operations on different aspects of the system take place at the same time. PyTorch works on the same principle. PyTorch 2.0 takes a step ahead in assisting developers due to its enriched flexibility. It also performs faster and better.
Application:
- Industries such as finance, marketing, and healthcare are its regular users.
- Tesla uses it for self-driving features.
- Mobile phones also use its AI framework to create better user experiences.
- New methods to perform algorithmic calculations are invented by using PyTorch’s capabilities.
PyTorch, a Python-built AI framework, is at an advantage due to its small community and infrastructure. But it does come with benefits such as easy to use interface and comparatively less complicated modules.
- Keras
The third most used open-source ML framework, popular for simple and consistent user interface. Keras developers strongly emphasize its application for humans and not machines – thanks to its user-friendly orientation.
During the model training process, Keras streamlines the development process by offering compatible elements. Its code is smaller and more readable compared to PyTorch and TensorFlow.
Application:
Its application is seen in a variety of products, such as;
- Image Classification
- Object Detection
- NLP
- Segmentation
- Data Generation
In essence, Keras is the user interface and is run by a strong AI backend. To list a few demerits, Keras is not widely used and has a small community. More so, in complex case models, it doesn’t perform that well and may be lacking in multiple ways.
Furthermore, its primary reliance on the backend frameworks limits its functionality.
LLM Orchestration Frameworks
LLM integration strengthens your products with capabilities such as usage pattern identification, prediction analysis, content generation, and data analysis. The reason we added it here is to familiarize you with this not-so-popular AI framework so that you can train your model from scratch.
- LangChain
LangChain stands tall on the biggest community on GitHub that continuously contributes to its framework. It can also integrate with major cloud providers such as Google, Azure, and Amazon. It comes with an open-source data toolbox that also contains prebuilt elements that developers can utilize as they see fit.
Application:
Here’s a list of diverse applications of this LLM orchestration framework:
- Automatic Agents that can make decisions
- Chatbots creation for seamless communication
- Personal assistants to understand questions
- Systems that comprehend code
Dropbox and Zapier are using LangChain as their main AI framework. More so, Saas products also benefit from its autonomous capabilities.In a few instances, it has been noticed that it makes simple tasks complex. It can also affect the upcoming AI frameworks with these complications
- LlamaIndex
A versatile framework that joins multiple data sources – databases, documents, and websites. It also has some common features with LangChain.Its two main features include indexing and distributed querying of LLMs. Both functions optimize the query process and increase searchability.
Application:
- Creation of Intelligent Agents
- AI Chatbots
- Structured and Unstructured data utilization such as getting answers from PDFs
LIamaIndex performs tasks such as text processing efficiently. Lastly, the answer you get to your query will depend on the quality of input this framework receives.
- Haystack
To build and deploy search and question-answering systems, backed by large language models, Haystack is used. It is flexible in deployment due to multiple source integration such as Hugging Face and OpenAI. Moreover, it provides Haystack Rest API – the ability to access your system via the web by deploying your system as an API.
Application:
Haystack performs tasks that are closely related, such as;
- Answering questions/FAQ software
- Search across documents
- Data Extraction
It lags when it comes to scalability. As the complex interface grows, Haystack can start under-performing.
How to choose an AI framework for your needs
Two things can be done in this regard:
- Understand your Current Business Needs:
The first basic step is to understand your business needs. It can be accomplished by looking at factors such as scope, budget, and project duration. More so, you can also work on your vision and value system that will go into the development process. It can ensure the project stays on the pre-determined timeline and achieves its goals.
- Consult an AI App Development Team:
Once you have a clear product vision, you need a dedicated team to turn your dream into reality. To make sure you don’t waste your time and resources, consulting a reputable company should be your priority. Once you are on board with a trusted company, they’ll take care of the rest for you.
Conclusion
In AI space things are moving fast. Every day comes with new libraries, technologies, and frameworks that you almost stay unaware of most of the time. AI frameworks make it easy to build your dream project with pre-built resources. These resources save your time, reduce efforts, and streamline the integration process.
Furthermore, AI frameworks are available in three major forms: open-source, LLM orchestration, and some alternates as well. The critical step involves making sense of your business needs and gathering a team to work on it. This guide was an effort in that regard.
