Not pretending to cover all the nuances of planning, developing and testing AI applications, we think that both experienced developers and beginners will find useful positions in the long-form instructions and examples. The range of application technologies based on the usage of neural networks is huge: from a simplified informer bot to an application equipped with functionality for planning the volume of trade operations, delivery, calculation and forecasting of profits, controlling employees and interaction with customers. Some successful examples of AI implementations that have started with minimal investment include Grammarly and Duolingo, the Waze and Canva services, and the FaceApp photo editor.
Overview of AI application areas
The functioning of AI takes place within the procrustean bed of several rules and categories, including:
- having powerful GPUs, thousands of gigabytes of data, and RAM from multiple nodes networked together to train the model;
- embedding the Internet of Things and algorithms for combining information from multiple layers into AI models;
- predicting events, understanding paradoxical situations, and coordinating high-precision systems;
- implementing APIs for generating new protocols and interaction patterns.
Typical ML machine learning replaces the operator at the beginning of communication with the support center, clarifying basic questions. When VCAs are involved in a deep format, requests are personalized, and contact security is increased thanks to the speech recognition and psychological status of customers. The automation of current tasks like searching for tickets, ordering goods, selecting waypoints is a part of virtual operators' functions. For this reason, the choice of ML or VCA depends on the issues to be solved.
Logistics, customer assessment and recruitment
AI-coordinated supply chain and logistics simplifies business by showing stock availability of items, indicating reserves, forecasting efficiency and payback periods. This is the work of high-level AI applications and services, with prices starting at $100,000. Auditing revenue and expense items, identifying trends in profit segmentation is an example of AI application in the financial industry. The application works in a similar way, personalizing each client and analyzing sales efficiency: media promotion strategies improve marketing positions.
AI's NLP capabilities provide initial search for employees and identify their professional skills. In the process, AI HR recommends changes to staff job descriptions if it sees progressive skills assimilation and automaticity, which promotes career growth.
Foundation: the right tasks and accurate data
The first two phases for planning organizational and technological operations for the development of an AI application are a fundamentally sound program with several steps. The diagram clearly shows that the first part includes problem formulation, tool selection, required resources, expected costs and profits. The second step is responsible for the formation of validated and accurate databases ready for model training.
This is how
AI development company of cross-platform applications start to work. The chain “requirements - goals - vision alignment - unified style” is thought out according to the SMART structure and categorized step-by-step in Scrum or Agile. Objectives and resource availability determine what scope of services and goods can be provided in the planned mode and reduced or expanded in case of scarcity or funds abundance.
Common Crawl, platforms like Kaggle or AWS provide databases that have been checked for accuracy, informativeness, repetition and error-free content in the event of a digital and graphical scarcity of source material. To check your own database, run through the Tibco Clarity utility (launched in 1997) or OpenRefine software.
Constant improvement and multimodal solutions
Python is a popular programming language, which at the same time represents the basis for creating AI applications due to the simplicity of commands. The cases of product developers are full of AI solutions for Google and Netflix, video hosting and
video streaming services. AI applications need to be constantly improved:
- train them to analyze sensitive and confidential information;
- remove inappropriate and creepy elements from generated photos and videos;
- form algorithms with encryption of databases of clients and companies with which cooperation agreements have been signed;
- perform anomaly detection on proposed solutions developed by AI.
Chameleon model action type modal data processing brings AI closer to the paradox-exclusive format of human reflection. Autoregression using the 34B protocol has been trained on 10T data tokens, so the multimodality model ensures generation of content and pictures with realistic parameters.
4D in the PSG4DFormer model and development in the time domain
Learning according to 4D rules - time-based learning - interprets information (data, audiovisual content, video) on a timeline. Dynamism 4D is the understanding of ongoing processes in time. The PSD-4D model forms volumetric nodes on the edges of which the objects to be studied are located.
Afterwards, the model by applying the annotated database with 4D masks performs segmentation and elaborates situations in detail within a certain time range. This is similar to storyboarding a movie where the director distributes scenes and events minute by minute. The PSG4DFormer model predicts the creation of masks and subsequent development on a timeline. Such components are the basis for generating future scenes and events.
Testing before startup
Application testing is accelerated by integrating the Python package with the Django framework. Python and web developers, DevOps engineers use built-in Django tools for this purpose, write test cases for unit tests and then embed the package into the framework.
In Featuretools library, features for ML models are developed automatically: for this purpose, the variables are selected from a database to become the basis for the training matrix. Data in time format and from relational databases become training panels during the generation process.
Libraries, platforms and languages are stack elements
The list of frameworks that improve the performance of AI models includes the open source TensorFlow library and the TFX platform, which speeds up the deployment of a finished project. These are honed for images. The PyTorch module is written in several languages, including Python, a basic version of C++, and the CUDA architecture designed for NVIDIA processors and graphics cards.
When physical storage and deployment environments are scarce, SageMaker, Azure, and Google cloud solutions are used. Julia is one of the most popular new languages for generating AI applications: when using commands written in Julia, more than 81% of commands are executed quickly, accurately, and with minimal errors. JavaScript and Python, R also show good results with 75+% accuracy.
In the application stack we add JupyterLab environment, NumPy library for multidimensional arrays or a simpler variant Pandas. Dask library is designed for analysis of large databases with clusters, visualization and parallelization, integration with environments and systems to reduce hardware maintenance costs.
Features XGBoost, TensorFlow, FastAPI
XGBoost 2.0 works on the principle of multivariate and quantile regression, including many features in the operation tree. The new functionality includes improved ranking and optimized histogram sizes, and the PySpark interface has become clearer. If you compare MXNet and TensorFlow, it is better to choose the latter platform because of better learnability, debugging and data loading speed.
Asynchronous and fast FastAPI operations make the framework preferable to Django, where on servers the WSGI standard needs to be configured to the new asynchronous ASGI. Due to the interface being 6 years old, it has limited data capacity for JWT tokens and S3 storage. We take into account that asynchronous libraries often have problems with unreadable information and sometimes we have to do writes by invoking execute() after passing the SQL query and materials. Note: the root_path attribute is not changed to “/api”, which is inconvenient.
Containerization, deployment, and AI model architecture
The containerization process is started when the components for creating an AI application are brought together (code and libraries with frameworks). A standalone container is abstracted from the host and ported to another environment without recompilation. Docker Engine and Kubernetes are the pioneers of this segment, the OS in demand is Linux (cloud or local), OCIs work in read mode, without modification. VMware and LXC are on this list. Containers are sometimes stored on the GitHub platform: especially when there is joint work on a project.
Deployment tools include the proprietary PaaS platform Heroku, the more sophisticated Elastic Beanstalk, and Qovery, which takes the best of both resources. For testing, they use:
- Selenium with three types of services WebDriver, IDE and Grid;
- PyTest platform with scalable tests on Python 3.8+ or PyPy3 versions;
- Locust with load tests.
Model Architecture |
Assignment |
Special Features |
Convolutional (CNN) |
Video and images |
Accurate identification, elimination of noise and errors |
Recurrent (RNN) |
Digital data and language |
Sequence processing |
General adversarial (GAN) |
Generating new data and pictures |
Simulation with generation of new data, as bases for training |
Afterwards, the AI model training is fine-tuned in a filigree. If the scenario includes high requirements with precise parameters, training continues with observation - such conditions are more expensive. To find artifacts and patterns in clustering, it is preferable to opt for self-training. For projects in robotics and simple
tap to earn games, reinforcement (encouragement or punishment - the “carrot and stick” method) is used.
Development timing, error checking
The time cost of developing, testing, and running an AI model looks something like the diagram. The algorithm requires an accurate description of task execution - so that the result is a new solution for pattern discovery. The “iterations-predictions-correction” chain is completed by hyperparameters entered manually before starting cross-validation in subsets.
In order for the model to perform productively in real-world scenarios, we need to assess correctness and speed of response. Therefore, the measurement parameters include precision and repeatability, ROC-AUC metrics, where there is no need to cut off the threshold (for an unbalanced database), F-score, specifying the proportion of positive solutions, MSE mean square error and R-squared coefficient of determination. An error within 5% is considered acceptable; when reduced to 1 and 0.1%, the result is considered highly accurate.
RAG and customization, backend or frontend integration, testing
RAG method is used to develop generative models, where vectors and semantics are approximated segment by segment based on context and relevance. The basis of RAG is to extract information from voluminous databases and then generate it into a model to obtain an accurate answer. The fine-tuning for specialized experiments includes normalization (reduction to common parameters) and, after adaptation,
tokenization. To make the AI model productive, integration is done, depending on the task, in the backend or frontend. It is better to integrate the language model into the server part, and to work with clients - into the interface.
In IoT, peripheral operation on the device is preferred as it preserves privacy and provides fast performance. At the core of IoT is data generation, the essence of which is the convergence of AI with IoT. This synergy builds up the functionality of the two parts, giving birth to AIoT. However, to enhance the power and scalability of the functionality, it is better to apply cloud-based technologies using embedded API protocols. If it is important to hear customer feedback (convenience, clarity, speed), we build in a feedback function.
Updating the AI model is a necessity to avoid “drift” when the underlying patterns become outdated and the accuracy of the response decreases. Therefore, iterative testing extends the model's lifecycle. Automated unit testing, periodic integration testing to evaluate the aggregate performance of individual functions, and UAT acceptance testing are the three mandatory “whales” of performance evaluation and testing.
ZBrain - open source and seamless integration
ZBrain is an example of an elaborate platform for symbiosis of enterprise processes and information with embedded AI functionality. Open source code with templates and memory integrated LLMs provide:
- storage and exchange of fiat and cryptocurrency in pairs, with blockchain-based transaction registration;
- productive work on a clear and detailed infopanel;
- management of multi-platform and cross-platform applications at micro and macro stages;
- implementation of cognitive technologies and project-oriented solutions.
It clearly democratizes and simplifies business process transformation when users themselves develop and deploy AI models for marketing and production logic and workflows without writing code. For example, Flow's seamless integration dynamically selects the right data and prepares AI solutions based on it.
Quantum computing: getting away from Neumann bottlenecks and reducing energy costs
Quantum computing is used to process large data sets. Algorithms used in quantum technologies accelerate AI-learning processes in medicine, materials, biological and chemical processes, and reduce CO2 and greenhouse gas emissions. To enable learning on billions of parameters, ultra-powerful graphics processors or TPUs are needed that are designed to perform several parallel operations.
Meanwhile, the Neumann bottleneck (VNB) problem must be overcome so that the processor can't wait for RAM to provide access to the process. The goal is to increase the speed of data retrieval and transfer from the database or storage. Even the high speed of multi-core processors with 32-64 GB or more of RAM may not justify the investment in capacity when the transfer of information from the cloud is limited. To solve the VNB problem, they expand the cache, introduce multithreaded processing, change the bus configuration, supplement the PC with discrete variables, use memristors and compute in an optical environment. In addition, there is also modeling by principle of biological processes such as quantization.
Digital AI paradigm in parallel processing increases energy consumption and time of learning processes. For this reason, qubits in superpositions (multiple positions in one time period) and entanglement positions are preferable to classical bits, provided that stability is preserved. For AI, quantum technologies are better due to the reduced cost of development and data analysis in multiple configurations. “Tensorization” compresses AI models and enables deployment on simple devices while improving the quality of the raw data.
Cyber defense rules
Pay attention to cyber defense - AI algorithms identify patterns in threat-carrying activities, predict potential cyber threats, and protect privacy, which is a legal and ethical imperative. GDPR and CCPA regulations, like other defense protocols, must be upheld by guaranteeing:
- anonymizing customers and ensuring there are no loopholes for third parties to identify them;
- differentiating sensitive points in passport data, emails, phone numbers and other documents that cannot be disclosed;
- joint analysis of information segments in two or three disconnected systems, without disclosing the full base.
Pattern poisoning (introduction of malicious elements) in AI, presence of adversarial vulnerabilities lead to misclassification. This is why a holistic approach should include protection principles from the development stage to testing and deployment to minimize challenges and risks.