Artificial Intelligence and Machine Learning are the IT industry’s fundamental core. Artificial intelligence is among the absolute best companions industries may have on their path to digitalization and robotization.
You could create much better user profiles, customization, and suggestions, incorporate better lookup, a voice interface, or smart assistance, or improve your application in a variety of different ways by utilizing AI technologies.
The numerous benefits of AI exceed its shortcomings, allowing it to give the best business solutions with the greatest reliability. Artificial intelligence (AI) can help your company or business become more efficient, agile, and cost-effective.
Let us have a look at some of the interesting statistics regarding the Artificial Intelligence
- The worldwide AI industry is expected to reach $1,811.8 billion by 2030, at a CAGR of 38.0%.
- 35% of businesses report adopting AI in their operations, while another 42% exploring the technology.
The stats are enough to show the potential that AI holds for companies and businesses. But, Artificial Intelligence is a big space, and with dozens of technologies to select from, determining which language would provide the maximum benefit to your project can be tricky.
As a top mobile app development company, we’ll help you make the decision.
In this blog, we’ve compiled a list of some of the best backend technologies for AI/ML app development to help you comprehend and build solid AI/ ML Software. So, let’s start!
AI-assisted estimates and analytics improve business performance, reduce production costs, and streamline operations. It’s no surprise that AI and machine learning are being used by numerous sectors to speed things up and accelerate business operations.
However, picking up the best backend technologies is important for AI solutions development.
Best Backend Technologies to Use for Developing AI-enabled Apps
Below are the best backend technologies for AI/ML app development which you can use for your AI app development project. So, take a look!
1. Python
Python has remained the best backend language for mobile app development, and creating AI and machine learning solutions in the last several decades. Python provides not only good community support and an extensive library, as well as the versatility that the programming language provides.
Platform independence and comprehensive frameworks for Deep Learning and Machine Learning are two advantages of Python that you may find useful. Python includes readable keywords, supports object-oriented programming, and could be used in combination with other languages such as Java.
Python has a higher development speed and allows you to test programs without actually running them.
Below are the features and benefits that make Python a prominent choice for AI solutions. Take a look!
Features and Benefits
- Due to its portability, it can run on various OS, including Windows, Mac OS, Linux, and Unix.
- Best for MVPs
- Its basic syntax makes it simple to learn. It facilitates the safe and inexpensive implementation of AI algorithms.
2. Tensorflow
TensorFlow is an open-source numerical calculation framework written in Python that enables data mining algorithms quicker and easier. TensorFlow apps can be deployed on almost any device, including a local PC, a cloud cluster, iOS and Android smartphones, CPUs, and GPUs.
Amongst the libraries used to build AI-based apps, TensorFlow is widely regarded as the greatest. Besides, businesses hire Tensorflow developers for complex projects such as developing neural networks, speech recognition, object recognition, text processing, image recognition, and several others.
Below are the features and benefits that make Tensorflow a prominent choice for Artificial Intelligence software. Take a look!
Features and Benefits
- Quick and versatile library for dealing with AI/ML and data sets
- Fast debugging
- Scalable
- Offers deep learning capabilities
3. Pytorch
Pytorch is a Python-based AI/ML and deep learning framework that is frequently used in language processing and image processing apps.
PyTorch includes numerous specially created modules, such as Torchtext, Torchvision, and Torchaudio, for working with various aspects of deep learning, such as natural language processing, computer vision, and voice recognition.
It uses an imperative programming model, which means that each line of code adds a new component to the graph, and each component may be run, evaluated, and modified separately from the entire graph structure, giving it a lot of flexibility.
Below are the features and benefits that make Pytorch one of the top backend technologies for developing Artificial Intelligence solutions. Take a look!
Features and Benefits
- Cloud support
- Easy to change network behavior with a dynamic graph computation
- Easy debugging
- Easy to scale
- Open-source
4. DialogFlow
It’s a Google-developed human-computer interaction technology centered on NLP (Natural Language Processing). It allows businesses to create conversational apps and experiences for their clients/customers in various languages and across multiple platforms.
Apart from this, DialogFlow is used to develop AI apps, IoT apps, business apps, conversational apps, serverless applications, AI chatbot development, and several others.
Below are the features and benefits that make DialogFlow a prominent choice for Artificial Intelligence apps. Take a look!
Features and Benefits
- Scalable
- Includes Google attributes such as Machine Learning and speech-to-text translation
- Multi-lingual
- Easily integrate with other languages
- Makes possible to develop AI systems like Alexa, Cortana, Google Assistant, and others
5. Scikit-Learn
Scikit-Learn is a free Python machine-learning library. It is mainly built in Python and heavily relies on NumPy for high-performance linear algebra and array operations. Furthermore, it offers simple and effective data mining and data analysis tools.
Scikit-learn is the best backend framework for machine learning and often the primary pick for many data scientists and machine learning practitioners due to its straightforwardness, flexibility, and reliability, particularly for small to medium-sized datasets and rapid prototyping of machine learning models.
Also, the features and benefits that make scikit-learn the prominent choice for developing AI/ML apps are listed below.
Features and Benefits
- Simple and Consistent API
- Provides state-of-the-art machine-learning algorithms
- Seamless Integration with other libraries
- Provides ensemble methods to combine multiple base models to achieve higher predictive accuracy and reduce overfitting.
6. Apache Spark
Apache Spark is a fast, open-source data-processing engine for massive data sets supported by the massive open-source community in big data.
It provides the computational speed, scalability, and programmability required for Big Data applications, notably streaming data, graph data, machine learning, and artificial intelligence (AI) apps.
The Spark analytics engine analyzes data 10 to 100 times quicker than others. It grows by dividing processing work among large groups of computers, with parallelism and fault tolerance built in.
It also contains APIs for common programming languages used by data analysts and data scientists, such as Scala, Java, Python, and R.
Features and Benefits
- Easily integrates with Big Data Ecosystem.
- Process and store data in memory, enabling faster data processing and iterative computation.
- Resilient Distributed Datasets (RDDs).
- It has a Machine Learning Library (MLlib) that provides extensive machine-learning algorithms and utilities.
7. IBM Watson
IBM Watson is an automated technology that can respond to natural language questions. Developers utilize IBM Watson to incorporate Artificial Intelligence and Machine Learning components into their apps without developing them from scratch.
The capabilities of IBM Watson vary from natural language processing to image recognition and beyond.
It has been widely embraced as the best backend technology across industries, bringing AI and advanced analytics to enterprises of all sizes and fostering innovation across multiple sectors.
Features and Benefits
- Watson’s natural language processing (NLP) skills comprehend text, interpret context, identify entities, and evaluate sentiments.
- It comes with tools for developing and implementing machine learning models.
- It allows users to discover patterns, correlations, and insights in massive datasets automatically, boosting data-driven decision-making.
- Provides customized solutions for specific industries
8. RASA
RASA is an open-source conversational AI framework for developers that allows them to build, train, and deploy chatbots and virtual assistants. It is a versatile and influential framework for constructing AI-powered conversational agents.
RASA is programmed in Python and includes both NLU and Core components for producing interactive and contextually aware conversational experiences. Companies utilize it extensively to create virtual assistants, customer support chatbots, language understanding systems, and other applications.
Rasa constantly grows and advances because of its flexible design and community-driven development, making it the best technology for the backend and an effective tool in the conversational AI industry.
Features and Benefits
- It includes an NLU component that evaluates user messages and extracts crucial information such as intents and entities.
- It supports interactive learning.
- Seamlessly integrate with external services and APIs
- Designed to handle contextual conversations
- Scalable and customizable.
9. Hadoop
Hadoop is a free and open-source distributed computing framework used to analyze and store enormous volumes of data across commodity hardware clusters. It is written in Java and is widely used in big data analytics, where it provides a reliable and cost-effective approach for dealing with large datasets.
While Hadoop is a strong tool for large data processing, it is important to note that the large Data environment has evolved, and new innovations such as Apache Spark, which provides in-memory processing and faster data processing, have gained popularity.
Hadoop surpasses Apache Spark in several business applications, while Apache Spark outperforms in terms of processing speed and ease of usage. The most important fact is that these two cannot be replaced.
Features and Benefits
- Hadoop Distributed File System (HDFS) stores vast amounts of data across multiple nodes in a Hadoop cluster.
- It stores various data, such as structured, semi-structured, and unstructured data.
- MapReduce in Hadoop is used to perform distributed data processing.
- Integrate seamlessly with machine learning frameworks and libraries.
10. MySQL
MySQL is an open-source relational database management system. Like other relational databases, it stores data in tables with rows and columns. Structured Query Language, or SQL, allows users to define, manipulate, control, and query data.
As a reliable and scalable backend database, MySQL is instrumental in storing structured data required for training AI models. It also facilitates real-time decision-making, storing up-to-date data used in recommendation systems, customer support automation, and fraud detection.
Moreover, it serves a significant role in NLP applications, housing text data for sentiment analysis, chatbot interactions, and virtual assistant responses.
With applications spanning various industries, AI backend developers see MySQL as a versatile and essential component in the corporate landscape for AI-driven app development.
Features and Benefits
- Based on the relational model.
- It supports various data integrity constraints.
- Handle large data volumes, and is known for its high performance and optimization capabilities.
- It stores structured data needed for training AI models.
- Companies use MySQL to store metadata and configurations related to their AI models.
Conclusion
Artificial intelligence isn’t going away anytime soon. The quicker you grasp this, the faster you’ll be able to take a competitive advantage, whether you’re just getting started with Artificial Intelligence development or want to streamline business processes with the best AI development solutions.
The type of business, goals, intended audience, pricing policies, and, most crucially, why you want to apply AI will all influence your decision.
If you want more in-depth information regarding the best backend technologies for mobile apps, it is advisable to contact a top AI development company. They will better suggest you the right one.
Quytech’s AI experts will assist you in identifying your business objectives and selecting the appropriate technology, and assist you in developing top-notch AI/ML solutions.
Frequently Asked Questions
Q1. What are the best backend technologies for AI/ML app development?
The best backend technologies for AI/ML app development are:
- Python
- TensorFlow
- Pytorch
- DialogFlow
- Scikit-Learn
- Apache Spark
- IBM Watson
- RASA
- Hadoop
- MySQL
Q2. Which language is best for AI/ML solutions development?
Python has remained the best for AI/ML solutions development in the last several decades.
Q3. Which are the top AI development companies in 2023?
The top AI development companies in 2023 are:
- Quytech
- TCS
- Accure Technologies
- HCL
- Wipro
- Accenture
- Infosys
- Appetiser
- Waracle
- MindSea
Q4. Do all AI/ML apps need a backend?
Backend server necessity depends on AI/ML application complexity, data requirements, and tasks. Simple, lightweight applications may function without, but sophisticated applications require more.
Q5. What is the development cost of an AI/ML application?
The cost of developing an AI ML app can vary based on various aspects. It may range from $30000 to $100000. Depending on the functionalities, the cost can go up to $500000 or even more.