We’re thrilled to share that Yalantis is now an official partner of Amazon Web Services (AWS) and a proud member of the AWS Partner Network (APN).
With a solid 10+ years of experience using AWS and successfully completing 200+ AWS-based projects for our clients, Yalantis has undergone thorough training and certification and achieved the status of AWS Select Tier Services Partner. Now our company is a member of a global community with access to AWS technologies, resources, training, and support. This recognition proves that Yalantis:
- has the experience and capabilities to assist clients in architecting, migrating, deploying, and managing advanced workloads on AWS
- is uniquely positioned to help client fully reap AWS benefits with advanced technologies like IoT, AI, and Data Analytics
- has in-depth knowledge and expertise of AWS best practices, providing customized solutions optimized for unique business needs
As such, we are empowered to continuously improve our skills and deliver innovative solutions tailored for the AWS Cloud.
Migrate confidently, innovate quickly, and transform your business through the power of the cloud.Explore our cybersecurity services
Ensuring information privacy is vital in today’s business landscape, and Yalantis fully supports this priority. As such, we are pleased to announce that Yalantis has attained ISO 27701 certification for privacy information management. Referencing various data protection regulations, including the General Data Protection Regulation (GDPR), the certification confirms that our controls and protocols for managing personal data meet international benchmarks.
Our clients and partners can be confident that we handle their personal data responsibly. This certification validates that Yalantis:
- safeguards personally identifiable information, never sharing it for unlawful or unauthorized purposes
- trains teams on protocols for proper handling of client information to support compliance
- continually monitors and evaluates privacy controls for ongoing improvement
ISO 27701 complements our existing ISO 9001 and ISO 27001 certifications, which reflect our commitment to continuous improvement across all systems, ensuring the highest standards for our customers.
We’ll continue evolving our cybersecurity expertise to remain your dependable partner for secure solutions. Stay tuned for more!
Need a reliable technology partner for your new project?
Enlist Yalantis to help protect sensitive data, ensure user trust, and provide uninterrupted service.Explore our cybersecurity expertise
September was officially announced as the Amsterdam season for the IoT Unit at Yalantis. We attended two international conferences well-known to every expert in the IoT domain – IoT Tech Expo and The Things Conference. We had the lucky chance to chit-chat with representatives of prominent industry players, listen to reports from industry leaders, meet our respected partners outside of Zoom meeting rooms, and delve into the background of emerging and established trends in IoT project development.
Obviously, one of the key directions of The Things Conference was LoRaWAN technology, a wireless protocol for data exchange between devices and the cloud.
Participants from multiple countries, including China, Japan, Germany, the Netherlands, and the US, joined the event to shed light on LoRaWAN and its significance in the digital business realm. It was truly fascinating, and the numbers prove it: about a thousand people attended the conference, and hundreds of stands with various IoT devices and solutions were showcased in the exhibit area. Speakers represented companies specializing in developing solutions for smart homes, manufacturing, and agriculture.
Of course, the event wouldn’t be complete without IoT gateway vendors disclosing the latest trends in gateway development and management. One of them was our long-term client, RAKwireless, presenting their LoRaWAN-based devices.
Their CEO, Ken Yu, shared insights about the current state of the IoT market and the concept of ‘installation of things.’ He also brought up the topic of optimizing the implementation of IoT solutions for streamlined user experience and accelerated time to market.
“So, IoT, Internet of Things, well, RAKwireless, we believe actually that means installation of things. That is where there’s a huge challenge. So, the next big thing in installation of things is actually making that user experience and deployment journey as easy as possible to scale and to get those solutions out into the market. So, that’s the next big thing.”
— Gavin Brown, Principal Product Designer at RAKwireless.
Additionally, Yalantis’ representatives delved into reports on the exploitation of IoT in the healthcare domain, specifically how IoT solutions are used to improve people’s well-being.
The main concept of the event emphasized how rapidly the IoT sphere and LoRaWAN technology have been developing recently, as well as their potential in being domain-agnostic.
“I reflect on two days filled with insightful dialogues, insightful presentations, and a demonstration of the famous Wall of Fame featuring LoRaWAN devices. The key narratives of the conference are to collaborate and partner. This is the way to success for IoT end-to-end solutions: IoT device & hardware partners, LoRaWAN gateway solutions, and IoT software development companies, like Yalantis, to deploy and manage it all in a single software platform.”
— Andrii Burlutskyi, Marketing Director at Yalantis
The second conference, IoT Tech Expo, is one of the most significant events dedicated to IoT.
During those exciting two days, our experts had the pleasure to meet with industry giants and get into the recent insights, updates, and achievements in the industry. We are pleased to share the IoT-related topics we enriched our knowledge in:
- Utilizing AI and ML for forecasting energy consumption and transport maintenance. This topic was particularly enlightening for us, as we have several similar projects at Yalantis.
- Ensuring cybersecurity for IoT networks using cutting-edge technologies
- Providing connectivity and ensuring the stable functioning of IoT devices, even in the face of unstable connections
“IoT Tech Expo is undoubtedly about high-quality and inspiring content. Speakers with international renown shared their case studies and insights, discussing real-world problems and their solutions. In the exhibit area, we saw numerous companies – from hardware manufacturers to representatives from the blockchain, artificial intelligence, and online banking sectors. We were particularly interested in observing how IoT is utilized in the edge computing and AI segments. One of the main advantages was excellent networking. Open and friendly participants valued their time and the time of other attendees.”
— Den Hukov, Head of the IoT Unit at Yalantis
The Yalantis team had the opportunity to engage with highly skilled professionals interested in the Rust programming language as a powerful tool for software development. Our company has deep expertise in utilizing Rust and is currently involved in several projects in the green energy, medical, and industrial IoT sectors.
At the conference, Yalantis also exchanged working experiences, including projects with companies such as RAKwireless, Toyota, and projects related to electric vehicle charging systems (EV Charging).
Finally, this conference allowed us to demonstrate our expertise in developing software for IoT-based solutions as well as emphasize our approach to fostering effective partnerships with our clients. Our objective extends beyond offering outsourcing development services; we are dedicated to thinking outside the box and enhancing our clients’ products to help them gain a competitive edge in the market.
We also prepared a quick video recap from The Things Conference for you:
It’s been fifteen years since Yalantis took on its first project as a small team of recent graduates interested in iOS software development. Now, Yalantis is a large software development company operating in multiple industries that has offices in several countries and provides a range of services that extend far beyond iOS software development.
Today, we want to outline how we reached the point where we are capable of bringing the bravest, most creative, and most innovative ideas to life.
Part #1. A brief overview of how it all started: From small team to company
Everything started in Dnipro, Ukraine, when young and motivated iOS developers Oleksander Kholodov and Serhii Fesenko decided to form a small development team:
“It all started in 2008 when I discovered freelance platforms oDesk (now Upwork) and Elance and was fascinated by the numerous requests for iOS development. Apple’s innovation became the foundation for Yalantis, as professional iOS-based application development was our initial focus and strategy.
We didn’t even have an aim of earning thousands of dollars or something like this. We were absorbed by the very process of creating nice and convenient applications, and it was our source of inspiration.”
— Oleksander Kholodov, CEO and Co-founder of Yalantis
Oleksander Kholodov (left) and Serhii Fesenko (right)
Developing iOS applications was a success, resulting in a large influx of clients with requests that the team couldn’t even manage to accept and deliver. This led to expansion of the team and, finally, the establishment of Yalantis.
Part #2. Active business scaling: Mistakes, achievements, and lessons learned
Then, a new period started: Yalantis underwent active expansion, left freelance platforms, and encountered its first difficulties.
First success: Scaling to fifty people
Yalantis faced a shortage of developers, but we managed to fill this gap by hiring students and teaching them the necessary skills, including in quality assurance, design, and other fields. That period was also marked by our partnership with our first marketing specialist, who helped Yalantis build the best digital marketing strategy in Ukraine at the time.
First mistakes: Missing critical points in the company’s operation
However, the initial success wasn’t long-lasting. In the background, Yalantis faced issues with delivery, project management, and sales. As a result, we had to bid farewell to 20 out of 100 employees and streamline the company’s structure, its internal operational processes, and its strategic development.
“Some people have to go through what life has prepared for them. Without such experiences, they won’t understand how to grow and mature. Similarly, business scaling, in most cases, brings crises that lead to inevitable changes in mindset regarding established things within the company.”
— Serhii Fesenko, CFO and Co-founder of Yalantis
First big client: An important lesson Yalantis learned
“We found and won our first big client through oDesk because we had the strong expertise they needed and offered relatively low rates. However, securing big clients isn’t just about having a few teams available for projects; it’s about building strong relationships. One of the biggest mistakes small companies make is not actively working with their clients. Instead, they often focus solely on lead generation and sales.
Many people don’t have a clue about account management, and this oversight can lead to a client leaving, even when you thought everything was fine. I encounter this issue frequently when consulting with small companies. They initially seek marketing consulting, but in the end, we often find ourselves working on the fundamentals of account management.”
— Oleksander Kholodov, CEO and Co-founder of Yalantis
Part #3. Yalantis today and tomorrow
Today, Yalantis is:
- A well-known software development and IT consulting company with 15 years of experience, 500+ specialists, and centers of excellence in Ukraine, Poland, Cyprus, and Estonia
- A company dedicated to developing solutions for clients interested in IoT, data science, artificial intelligence (AI), machine learning (ML), DevOps, embedded development, and other technologies
- Trusted by clients from the USA, Israel, and the DACH region
- A partner of companies including Google X, Bosch (Home Connect), Toyota Tsusho, ViewTrade, RAKwireless, KPMG, Healthfully, Lifeworks, and others
Tomorrow, Yalantis will:
- Establish partnerships with industry-renowned leaders, assisting them in implementing innovative technologies and delivering state-of-the-art solutions that enable dramatic changes
- Bravely immerse ourselves in delivering ready-to-use solutions for businesses in various industries, enhancing their digital transformation efforts and adopting best practices in business operations and optimization
- Continue actively working in alignment with our motto: Together everyone achieves more. Our corporate values include voluntary work, openness, transparency, support, ownership, and deriving satisfaction from the things we develop and deliver.
When we adopted this motto, Yalantis conducted a survey among employees, allowing them to choose the motto that reflects values they appreciate most and want to align with.
“The values upon which we build all our processes are not merely documented somewhere in Yalantis policies, nor are they forgotten under the dust. They are an integral part of our everyday life. The primary value of our company is people, and our strategy fully reflects this commitment.”
— Oleksander Kholodov, CEO and Co-founder of Yalantis
This is just the beginning. We are confident that the future holds even more milestones and achievements. We hope you’ll continue to be by our side throughout this journey, because together everyone achieves more.
Yalantis has launched its own line of ready-to-use products that have been proven efficient in a diversity of operations including managing delivery teams, facilitating development processes, and serving as a foundation for complex IoT product delivery. All of these products will be available in the Solution Hub section of our website.
Why launch a Solution Hub?
As a long-term player in the outsourcing software development industry, we considered the pain points and technological priorities our clients faced with their product delivery throughout more than a decade. Our objective was to identify which problems remained relevant despite changing market circumstances.
“As the Chief Technology Officer, I am delighted to unveil the Yalantis Solution Hub, a transformative initiative designed to empower businesses with a robust strategy for exponential growth. This initiative is rooted in our commitment to deliver unparalleled business value to our clients.”
— Denis Doronin, Chief Technology Officer at Yalantis
Tailoring solution adaptability and flexibility across diverse industries
All of our products and solutions can be tailored to your specific needs and branded according to your corporate style. Their architecture allows for a streamlined and quick integration with various third-party services necessary for your daily operations, such as Jira, Confluence, and more.
To expedite the adoption of any solution, our technical team is available to deploy, configure, and develop these solutions into your comprehensive end-to-end business product. Additionally, we can organize workshops for your internal teams to ensure seamless integration and effective utilization of the solutions.
Key solutions in our pilot release
The first release includes the following solutions:
- Yalantis IoT Accelerator: A domain-agnostic and easily scalable data management platform with a set of customizable features focused on receiving, sending, processing and analyzing real-time IoT data on energy consumption.
- Yalantis Predictive ML Model: A customizable and easily integrated model for real-time IoT data processing and analysis that works based on machine learning algorithms.
- Yalantis Enterprise Resource Planning (ERP): An ERP system providing opportunities for accurate and convenient financial management and reporting, streamlined human resources allocation, and efficient project management.
- Yalantis Competency Evaluation Platform (CEP): An internal system for evaluating employees’ performance and competency levels throughout their cooperation with the company.
- Yalantis Payments: A corporate platform for the comprehensive management of employees’ compensation.
- Ya.me: A custom HRM system with flexible roles and permission flow that can allow employees to easily monitor their available leaves and get a transparent overview of the company’s structure.
All of these products are distributed under the Yalantis license, and, if needed, you can get a comprehensive consultation from our support team or technical specialists.
However, the work isn’t finished yet. In the near future, there will be even more updates within the Solution Hub, and you’ll find that instrument you’ve been lacking in your business processes. Stay tuned!
A skilled developer can speed up development timelines, deftly handle challenges, and provide ongoing maintenance, giving you the ability to stimulate innovation, preserve competitiveness, and deliver excellent user experiences while maintaining complete control over the development process. With their technical expertise, your software will be built with cutting-edge technology and accepted industry standards in addition to being customized to your specific specifications.
Partner with one of the best software developers in the market today, partner with Yalantis! Yalantis stands out as a reputable software engineering and IT consulting firm, having amassed more than 13 years of expertise and proudly holding ISO 9001 and ISO/IEC 27001 certifications. Our extensive presence across Europe through various development centers and a skilled team exceeding 500 professionals which enables us to deliver great solutions.
As a matter of fact, we’ve been recently named as one of the most-reviewed software developers in Warsaw by The Manifest. This award means a lot to us and we are extremely proud to be featured on this list.
The Manifest is a business blog platform that aims to gather and verify the hard data, expert insights, and actionable advice that you need to build your brand and grow your business – to provide the practical business wisdom that manifests in your success.
We would like to thank our clients for being a significant part of this award. We couldn’t have done it without you! Thank you for your support and for trusting us with your business.
We’d love to work with you.
Collaborate with Yalantis today!Contact us
Machine learning (ML) can enable a business to harness the power of data to drive innovation. A successful ML solution can achieve revenue growth, offer a competitive advantage, improve automation, bring efficiency gains, and provide actionable insights.
Statista predicts the ML market will experience a compound annual growth rate (CAGR) of 18.73 percent between 2023 and 2030, leading to a market volume of $528.10 billion by the end of that period. This strong growth creates the feeling that literally every business needs to implement ML — especially when there are so many widely discussed use cases. So, is ML the magic pill for your business needs? To determine if ML can help you achieve your business goal or solve a business problem (and how), you need to understand:
- how this technology works
- its place in the hierarchy of artificial intelligence (AI) technologies
- its main capabilities and limitations based on examples of machine learning use cases
This knowledge is critical, as there’s a lot of confusion about AI and ML that leads to:
Overgeneralization. AI and ML are often used as broad terms encompassing various technologies, leading to misunderstandings of their specific capabilities and limitations.
Unrealistic expectations. Due to media portrayals and hype, it’s a common misconception that AI and ML can solve any problem, leading to disappointment when the technology’s actual capabilities are understood.
Skepticism. AI and ML are complex topics. A lack of understanding can lead to skepticism about them and unjustified refusal to implement them even when they are likely to be highly beneficial.
That said, you don’t need broad knowledge of ML to check if it can be beneficial for your company or a specific business case. To figure that out, just read this article, which describes common machine learning use cases and offers an overall roadmap for setting up and implementing a successful ML-driven project.
Yalantis has entered the list of leading AI development companies.See the details
The role of ML in the AI landscape
Let’s figure out what AI, ML, and deep learning (DL) are at a basic level, which is sufficient for our purposes.
Artificial intelligence refers to systems capable of performing tasks that typically require human intelligence. ChatGPT, a language model that has recently become the stuff of legend, is a great example of an AI use case. ChatGPT can process and understand text, engage in dialogue, and provide intelligent human-like responses.
Machine learning is a subset of AI focused on designing algorithms and models that automatically learn patterns, make predictions, or take actions based on data. In an ML-driven project, a set of algorithms and models are fed with structured data to carry out a task without being programmed how to do so. When it’s effective, a trained ML algorithm uses data to answer a question, and this answer can be accurate and impossible even for human experts to provide.
Deep learning is a specialized subset of ML that concentrates on training artificial neural networks with multiple layers (deep neural networks). ChatGPT uses advanced DL techniques to understand and generate human-like text based on complex patterns and relationships it has learned from training on vast amounts of textual data.
Is ML suitable for your business case?
Here is a list of the main preconditions for a successful ML-driven project to answer the question above:
1. Set specific goals and identify the key problem
Having clear project goals and a well-defined problem statement provides focus and direction, enabling effective planning, data collection, algorithm selection, and evaluation of results. It helps align stakeholders’ expectations, ensures efficient resource allocation, and facilitates communication and collaboration throughout the project.
For example, the goal of an ML-driven project might be the development of a predictive model that accurately detects fraudulent transactions in real time for an e-commerce platform, reducing financial losses and improving customer trust. In this case, the problem statement might be leveraging historical transaction data to train an ML model capable of identifying fraudulent transactions with a high level of accuracy, minimizing false positives and false negatives.
2. Make sure you have enough quality data
Having a significant amount of quality data is critical for achieving accurate and reliable results in AI-driven projects. This need is especially acute for ML-driven projects where sufficient high-quality data allows the model to capture a wide range of patterns, relationships, and variations present in the data. A large amount of quality data provides the foundation for building accurate and reliable ML models. On the contrary, insufficient or low-quality data can result in incomplete or biased learning, leading to suboptimal performance.
You can understand if you have enough reliable data for your ML-driven project by evaluating the volume, quality, relevance, distribution, and accessibility of available data in relation to the project’s requirements and the performance expectations of ML algorithms.
3. Rule-based or ML: What’s your cup of tea?
There are cases when the rule-based approach is preferred over the ML approach for problem-solving and decision-making. Rule-based systems operate according to a set of predefined rules programmed by humans. These rules define the logic and decision-making process and are typically in the form of if–then statements, where specific conditions (if) lead to predetermined actions or outcomes (then). Rule-based systems are well-suited for problems with clearly defined and predictable patterns, where human expertise and domain knowledge can be explicitly encoded into rules. For example, quality control, medical diagnosis, and workflow management systems might be rule-based.
See that your problem can be solved with a rule-based approach? Then go for it. It will allow you to build a transparent and explainable system. ML models are typically more complex. Moreover, taking the ML approach in such a case would be counterproductive, since it requires extensive data, training, and computational resources to learn patterns and make predictions. But you should choose the ML approach if you need to deal with complex patterns, large datasets, unstructured or ambiguous data, and adaptive systems.
To help you conclusively determine if the ML approach is effective for your business case, let’s look at the most common machine learning business use cases based on business type and size. Most likely, one of them will intersect with your project idea.
Common ML use cases for a successful ML-driven project
Machine learning is well-suited for the use cases below because such projects benefit from identifying complex data patterns, the use of large datasets, and the ability to learn from examples.
Fraud detection and risk management. This is one of the most common uses for machine learning. In finance, insurance, and e-commerce, ML can help identify fraudulent activities, detect anomalies, and assess risks.
Predictive maintenance. ML can be used for predictive maintenance in the manufacturing, transportation, and energy sectors. By analyzing sensor data and historical maintenance records, businesses can predict equipment failures, schedule proactive maintenance, and minimize costly downtime.
Natural language processing (NLP) helps to analyze and get insights from textual data. NLP tasks include text classification, named entity recognition, and language translation.
Computer vision. This expertise allows businesses to use ML for visual recognition, image analysis, and object detection tasks. Computer vision is applicable in healthcare, retail, manufacturing, and security, enabling businesses to automate processes, enhance quality control, and improve the user experience.
Speech recognition. ML models can be trained to turn spoken language into written text. This can improve voice assistants, transcription services, and voice-controlled systems. Speech recognition is applicable in customer service, healthcare, automotive, transportation, and other industries.
Traffic pattern prediction involves utilizing ML algorithms to predict traffic conditions and patterns in transportation networks. By analyzing historical traffic data, real-time sensor data, weather conditions, and other relevant factors, ML models can predict traffic flow, congestion, travel time, and potential bottlenecks.
Algorithmic trading. ML algorithms can help analyze vast volumes of financial data and make automated trading decisions by identifying patterns, trends, and correlations in market data to generate trading signals, execute trades, and manage portfolios.
Sentiment analysis. As a subset of NLP, sentiment analysis specializes in interpreting the sentiment or emotion behind text data, such as social media posts and customer reviews. ML models are trained to analyze language, context, and linguistic cues to define if sentiment is positive, negative, or neutral. Sentiment analysis has applications in market research, brand monitoring, customer feedback analysis, and reputation management.
Email monitoring involves leveraging ML techniques to analyze and process email communications for various purposes, such as filtering spam, detecting malicious content, categorizing messages, and extracting valuable information.
Customer journey optimization. The use of ML algorithms can help to analyze and optimize the end-to-end customer journey across various touchpoints and interactions with a business or brand to improve the customer experience, customer satisfaction, and business outcomes.
Complex medical diagnosis. ML algorithms and techniques can be used to assist in diagnosing patients with conditions such as skin cancer. ML models can be trained on large datasets containing medical records, patient information, symptoms, and diagnostic outcomes to learn patterns and identify correlations.
Recommendation engines. ML algorithms can help online businesses offer users personalized recommendations. Recommendation engines analyze user preferences, behaviors, and historical data to suggest relevant items or content that users are likely to be interested in. Recommendation engines are widely used by e-commerce platforms, streaming platforms, news aggregators, and social media platforms.
If the preconditions and use cases for machine learning above have convinced you that the ML approach is the way to go, it’s time to learn the specifics of implementing ML depending on the size of your business.
Peculiarities of ML implementation for businesses of various sizes
Implementing machine learning can look different for businesses of different sizes due to variations in available resources, budgets, data volume and quality, technical expertise, and organizational complexity. The bigger a business is, the more challenging it becomes for it to ensure smooth data integration due to the increasing number of internal systems and data as well as the need for its synchronization.
Small businesses typically have limited resources and need to prioritize specific machine learning cases that align with their budget, data availability, and capabilities. They tend to build targeted solutions (demand forecasting, fraud detection, customer segmentation, sentiment analysis, and others) to gain a competitive advantage.
Midsized businesses try to grow their technical capacity for improved scalability and innovation. To achieve this goal, they can implement predictive analytics, NLP, image or video recognition, process optimization, and anomaly detection solutions.
Large businesses or enterprises may have dedicated AI departments along with sufficient resources and enough relevant data to benefit from the enterprise-wide impact of ML-related initiatives. Such a large-scale impact might boost decision-making across departments and functions and improve operational efficiency and risk management. Moreover, enterprise-wide ML implementations can even drive the development of new products, services, and business models.
No matter how large your business is and what ML use case you want to implement, you need an effective plan for implementing it. Further, we talk about how to plan and execute a successful ML project.
ML project lifecycle: steps on the road to effective implementation
The ML project lifecycle refers to the sequence of stages and activities involved in the development and deployment of an ML project.
- Define the problem and set project goals. Analyze your business and determine where ML can be effectively used. The main purpose here is to define the objective and scope of the ML project. Consider your limitations, including your budget, timeline, available expertise, and available data. Make a detailed overview of the case you want to solve with ML. Articulate the problem statement and establish goals, metrics, and success criteria.
- Collect data. Data collection involves gathering the relevant data required for the project. This can include acquiring existing datasets, collecting new data, or a combination of both. Data should be representative, diverse, and of sufficient quality to ensure accurate model training and evaluation.
- Analyze and clean the data. Analyze the collected data to gain insights and identify any issues or inconsistencies. Data cleaning involves handling missing values, removing outliers, addressing data inconsistencies, and transforming the data into a suitable format for analysis.
- Perform a feasibility study. Conduct a feasibility study to assess the viability of applying ML techniques to solve the defined problem. This involves evaluating factors such as data availability, computational resources, expertise, time constraints, and potential ethical or legal considerations.
- Develop and train the model. Design and develop an ML model based on the problem statement and data. Split the data into training and validation sets, and train ML algorithms on the training data to learn patterns and relationships. Iteratively refine the model to improve its performance and ability to generalize.
- Fine-tune the model. Fine-tuning involves optimizing the model’s hyperparameters — such as learning rate, regularization, and network architecture — to achieve better performance. This step aims to strike a balance between underfitting and overfitting, ensuring the model can effectively generalize unseen data.
- Integrate the model. Once the model is trained and fine-tuned, integrate it into the desired application or system. This involves setting up the necessary infrastructure, APIs, or interfaces to incorporate the model’s predictions or insights into the target environment.
- Refine the model. Continuously monitor and evaluate the model’s performance in a real-world setting. Feedback from users, performance metrics, and ongoing data collection can help you identify areas for improvement. Refine the model by incorporating new data, retraining the model, or updating algorithms to enhance its accuracy and relevance.
- Monitor and maintain the model. After deployment, regularly monitor the model’s performance. This involves tracking key performance metrics, detecting any drift or degradation, and conducting periodic maintenance and updates to ensure the model’s effectiveness, reliability, and alignment with changing requirements.
During the first step of the ML project lifecycle, you’ll need to choose an optimal technology stack. That’s when the project strategy decision tree will serve you well.
How a project strategy decision tree helps to create a workable ML solution
A project strategy decision tree is a visual representation or diagram that guides the decision-making process when developing a project strategy. It presents a hierarchical structure of decisions and their potential outcomes, allowing project managers and teams to systematically evaluate and choose the most appropriate strategies based on specific criteria.
Using a project strategy decision tree, you can systematically evaluate different technology stacks, consider relevant criteria, and make informed decisions that align with the specific needs and goals of your ML-driven project.
There are two common approaches to the technical implementation of an ML-powered project:
- If you don’t have in-house ML expertise and you need to handle a common ML use case, use a suitable SaaS solution (such as IBM Watson Studio, Databricks, or DataRobot).
- If you need to quickly set up the required infrastructure, access pre-built tools and frameworks (such as TensorFlow, Keras, PyTorch, and Caffe), obtain computing power and resources, and validate the feasibility of your AI and ML initiatives, use AI and ML infrastructure or managed services (such as AI managed services by Amazon, Microsoft, or Google).
Having viewed the overall approach to implementing an ML-driven project, let’s see how to build an appropriate architecture for an ML solution based on a fictional example.
Designing an optimal architecture for an ML project
Imagine you have a blog with over 500 articles and around 1,000 unique visits per day. The problem is that only 0.5 percent of visitors fill out the contact form. During initial communication with potential clients, most ask for articles showcasing your expertise even though they are already available on the website. While searching for services, potential clients often miss such articles and may think that a company doesn’t have the required experience. To solve this problem, you can benefit from ChatGPT’s capabilities. Follow the steps described below.
1. Create an architectural vision
To develop the solution architecture, you need to create an architectural vision, which is the high-level view and strategic direction of the architecture, outlining its desired future state, goals, and principles. An architectural vision can be created based on the most important architecture drivers: business goals, use cases, and constraints. For this project, they will be as follows:
- Decrease the time for lead conversion by 50 percent.
- Increase the sales team’s efficiency by providing a convenient article knowledge base.
- Prospects can use a chatbot on the website to find relevant articles.
- The sales team can use a chatbot in Slack to find relevant articles by keywords.
- Implementing the project should be cost-effective.
- The system should be developed by one engineer within two months.
2. Consider architectural concerns
You should take into account all factors that might affect the future solution’s quality and feasibility. Here are the architectural concerns for the project we’ve described:
- To ensure that the chat provides accurate information, ChatGPT by OpenAI should be trained with your derived data. Without it, ChatGPT will provide answers that are too generic and may be irrelevant to your company.
- Training data should be verified and fine-tuned by the sales team to ensure that the chatbot broadcasts your corporate tone of voice.
- ChatGPT is known to be bad at finding links to existing pages (consequently, there is a need to validate and regenerate links after ChatGPT).
Suppose you decided to create a proof of concept (PoC) to verify the feasibility of adding training data to ChatGPT and check the accuracy of links it provides to your corporate website. The developed PoC helped you make sure that links generated by ChatGPT lead to 404 pages in most cases, requiring you to validate and regenerate them.
3. Develop the business architecture
A business architecture diagram shows all the systems we have within the project (we will create some of them from scratch and the rest will be ready-made solutions). The diagram should also include all actors working with these systems. The following is an example of what the business architecture for the described project might look like:
4. Resolve the dilemma of adopt or build
Next, you need to decide which existing solutions (paid or open-source) to adopt and which to build yourself. We advise creating custom solutions only if they have the potential of bringing you a competitive advantage or enabling you to get more profit or clients. Otherwise, use ready-made solutions since the less code, the shorter the time to market and the fewer development bottlenecks you’ll face in the future.
For the project described, we would advise you to create the following solutions:
- Website chat UI to ensure strong company branding and provide a better user experience than competitors
- A training data set to ensure the chatbot transmits your corporate tone of voice while communicating with prospects
- An API to connect all adopted solutions, since you are unlikely to find a proper ready-made solution for this purpose
5. Choose the most appropriate solutions
Use a decision log, technology radar, and reliable metrics to make the final decision on the best technology solution, be it a framework or a programming language.
- A decision log is a record of the decisions made during the process of evaluating and choosing the most suitable technology solution for a specific purpose. It includes information about the technologies considered, evaluation criteria, decision rationale, stakeholders involved, and any supporting documentation or references.
- Technology radar is a tool used to track and assess emerging technologies, trends, and practices in the software development industry.
- Other parameters to consider include the number of stars on GitHub, containers in Docker Hub, questions on Stack Overflow, and freelancers on Upwork.
6. Identify all risks (tradeoffs) in your architectural solution
Perform tradeoff analysis to evaluate and make optimal decisions about various architectural options by considering the tradeoffs associated with each choice. This involves assessing the advantages, disadvantages, and impacts of different architectural decisions to identify the optimal solution for a specific software system.
The table below is the result of conducting tradeoff analysis for the project described in this post. Each row of the table is an architectural decision (AD). Each column is an architectural driver (a key requirement or consideration that influences the design and development of an architectural solution) outlined at the architectural vision stage: business goals (BG), use cases (UC), concerns (CN), and constraints (CT).
Marks at the intersection of rows and columns characterize the architectural decisions as follows:
- S (sensitive) — AD is critical to ensure, as the driver won’t be met if it isn’t
- N (non-risk) — AD is optional to ensure, as the driver will be met even if it isn’t
- R-1 (risky) — The sales team has to verify the spreadsheet data, which might be time-consuming
- T-1 (tradeoffs) — Refining links using Google Search API is obligatory but will prevent meeting the project deadline
As you can see, implementing a successful ML-driven project is a complex process that might require years of expertise and accumulated knowledge. If you decide to cooperate with an AI and ML development provider, pay attention to their expertise in ML algorithms and techniques, portfolio of AI-powered projects, and professional recognition. Yalantis has been proclaimed a leading AI development company by C2Creview, a research and IT company review platform. We consult and create quality software utilizing AI, data science, and business intelligence and analytics.
Implement an effective ML-powered solution
We are delighted to announce that we’ve been recently recognized by The Manifest as one of the most reviewed app developers in Lviv, Ukraine. Getting on this list of winners underscores our achievements obtained as the result of our incredible efforts in the software development industry.
This award is a testament to the high quality of our development services and reflects our fruitful client relationships. We would like to extend our gratitude to our wonderful clients and partners for their overwhelming support. To The Manifest and their team, thank you for choosing us for this special award.
The Manifest is a business blog and reviews website that gathers and verifies the hard data, expert insights, and actionable advice that you need to build your brand and grow your business. Consequently, The Manifest strives to provide practical business wisdom that manifests in your success.
Established in 2008, Yalantis has achieved a stable and long-lasting market position. We are an ISO 9001 and ISO/IEC 27001 certified software engineering and IT consulting company with over 500 experts on board. Among our clients are globally recognized brands such as Toyota Tsusho, Bosch, Zillow, KPMG, and LifeWorks.
If you are looking for a development partner with exceptional and polished software development services that is capable of satisfying your business needs, you’re in the right place! Tell us about your project today.
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We are delighted to announce that we’ve been ranked second in a list of the top 10 IoT app development companies for July 2023. This list was compiled by FindBestWebDevelopment, which helps businesses find expert software development providers. In ranking providers, FindBestWebDevelopment examines their ability to deliver innovative and consumer-centered IoT solutions.
Yalantis develops smart home, building automation, industrial, automotive, healthcare, and other IoT solutions. Examples of our complex IoT-related projects include:
- WisDM, a SaaS solution that provides automated management of IoT networks
- a telehealth solution with medical IoT device integration
- ConnectHome, an IoT solution for automated smart home management
- team augmentation for a producer of IoT devices and software for their management
Yalantis clients benefit from our:
- vast expertise gained through multiple successful IoT projects across various domains
- time-saving, cost-efficient, and secure development approach
- innovative product mindset ensured by engaging top talent and modern technology
- commitment to creating software considering clients’ long-term goals
- dedication to delivering scalable and flexible solutions to meet ever-changing business needs
Looking for a trusted IoT development partner?
Learn what we can do to successfully implement your project.
On June 13, 2023, Yalantis hosted a knowledge-sharing event in Warsaw, Poland, to discuss the latest AI and ML technologies. More than 70 attendees listened to four top-notch speakers from Yalantis. Let’s look at a quick overview of each presentation.
How AI and ML disrupt businesses of all sizes
Denys Doronin, our Chief Technology Officer, covered a brief history of AI and ML evolution and how each type of business can benefit from implementing AI and ML. Given a limited number of resources, small businesses can benefit from launching a niche AI solution to win the market. Midsize businesses may already have many interconnected systems and lots of data sets, and they can use AI/ML for scaling. Large enterprises, in turn, can use AI/ML to improve their operations at the departmental level.
Data is the new precious metal of the 21st century. If you, as a business representative, know how to use your data most effectively using ML and AI, then you can get it all: a competitive advantage, automation, increased business efficiency, and much more.
Denys Doronin, Chief Technology Officer
ML use cases
In his interactive presentation, Serhii Zhuravel, our Director of Engineering, explained that ML comprises algorithms fed with structured data to perform tasks that aren’t rule-based with much higher accuracy than humans. Serhii also pointed out that when working on ML projects, we first need to clearly define the problem to solve. Only then can we integrate a suitable ML model, refine it if the initial solution was incorrect, and monitor the model for consistently correct results.
Clients may have data with which they want to teach a neural network to solve certain practical tasks. But it may turn out that the data is labeled incorrectly or is too specific. That’s why we need to carry out a feasibility study to define if the collected data is fitting to implement an ML model.
Serhii Zhuravel, Director of Engineering
Architecture design techniques to build corporate ML chatbots
The next speaker was Maksym Moskvychev, our Head of Architecture Design Office. Maksym’s speech focused on a Yalantis ML project. With the help of ChatGPT, Yalantis built a website chatbot for prospective clients and a Slack chatbot to help the sales team quickly find relevant case studies and articles. Maksym’s team needed to define which components to develop from scratch and which existing solutions could be used. To deliver optimal products, the team used The Open Group Architecture Framework (TOGAF) approach and architectural tradeoff analysis.
To start this project, we developed an architectural vision first, which means transforming the client’s requirements into clear statements to make it easier for us to work on the project.
Maksym Moskvychev, Head of Architecture Design Office
AI and ML advancements and trends in IoT software projects
Denys Hukov, our Program and Delivery Manager, discussed the benefits and opportunities of adopting AI and ML in the IoT domain. Key benefits range from processing large data sets that numerous IoT devices generate in real time to efficiently detecting operational anomalies and enhancing predictive maintenance. Denys also covered a few success stories of IoT companies implementing AI, such as the Polish company Bin-e, which offers smart AI-based waste bins; and Google’s Waymo Via project, which provides self-driving trucks.
What’s next with AI and ML solutions? No one actually knows. Personally, I see the future of IoT in using AI-/ML-enhanced edge computing and visual recognition to support quick and automated decision-making.
Denys Hukov, Program and Delivery Manager
Stay tuned for more upcoming online and offline events from the Yalantis team on technology and business topics. You can learn about new events on our company’s social media pages, including on LinkedIn.
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