Machine learning development
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Develop ML-enhanced software solutions to deliver an exceptional user experience and personalization
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Conduct sentiment analysis among your customers to improve service delivery
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Solve complex business issues with the help of ML in a matter of hours instead of days or months
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Get highly accurate forecasts of business outcomes with carefully trained ML models
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Classify and cluster large amounts of proprietary data to extract business insights
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Ensure stability and performance with advanced monitoring and tracking using ML algorithms
Machine learning development services Yalantis provides
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Machine learning consulting
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Definition of problems and goals
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Planning of efficient machine learning implementation
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Selection of suitable ML approaches and models
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Analysis of ML tools based on domain and business needs
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On-demand ML expert consultation at the support stage of the software project
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Machine learning service
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Integration with ready-made ML solutions to save development costs
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Access to a variety of ML tools for a quick project launch
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Development of highly efficient ML models with Amazon, Microsoft Azure, and Google Cloud Machine Learning services
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Machine learning solutions
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Data preparation and environment setup for ML solutions
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Model selection, training, and continuous evaluation
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Deployment of the trained model and monitoring of its integration into the business setting
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Develop ML-powered software solutions tailored to your needs
Cooperate with a team deeply immersed in ML solutions and experienced in up-to-date AI technologies.
Industry-specific ML use cases Yalantis focuses on
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Manufacturing
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Predictive maintenance
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Supply chain optimization
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Supply and demand forecasting
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Quality control
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Detection of equipment malfunction
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Issue identification in the production flow
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Deep root cause analysis
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IoT
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Monitoring and support of IoT devices
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Aggregation, processing, and analysis of data from IoT sensors
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Personalized insights based on IoT data
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Detection and prediction of operational anomalies
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Energy and resources
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Energy waste reduction
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Smart charging
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Energy consumption optimization
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Smart grid systems
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Effective resource allocation
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Identification of emission patterns
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Energy trading insights
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Benefits of partnering with our machine learning development company
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Parallel model evaluation
Engage several data scientists for efficient machine learning model evaluation to achieve high project speed and accelerate the time to market. Get professional recommendations on data and model improvement.
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Custom ML implementation strategy
Ensure a custom approach to model development that guarantees accurate and business-specific ML model development and implementation.
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Prototype development
Develop a prototype of the machine learning model and test it on synthetic data first. Safely unravel potential issues and prepare the model for more efficient work on real-world business data.
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Enhanced quality control
Improve your project quality by consulting with additional service experts such as a cybersecurity specialist who will ensure your ML solutions align with industry-specific security standards.
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Collaboration efficiency
Develop machine learning models within set deadlines and take an active part in development, deployment, and evaluation processes. Provide key internal and external stakeholders with project results when reaching critical milestones.
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Early-on audit of model performance
Entrust a capable data science team with thorough model testing and evaluation. Validate model performance before the release against initial requirements and promptly overcome any bottlenecks.
Yalantis: A company with a proven portfolio
Increase work efficiency with custom ML solutions
Increase your company’s productivity by implementing advanced machine learning algorithms that speed up business problem-solving.
Video reviews of our clients
What triggered us was their remote collaboration practices as well as their experience in the IoT industry. Their strong technical experience helped us scale our platform and deliver great performance to our customers.
Yalantis has been a great fit for us because of their experience, responsiveness, value, and time to market. From the very start, they’ve been able to staff an effective development team in no time and perform as expected.
Working with Yalantis, you get their breadth of experience building hundreds of projects. Their expertise and knowledge were second to none. And that makes the difference between a good product and a great product.
Established development flows and good communication skills made collaboration with Yalantis very smooth. We appreciate their professionalism and dedication. If you are looking for a solid technical partner and a well-processed software outsourcing company for your project, I’d recommend Yalantis.
Insights into our machine learning development
How to streamline your ML software development project
Read this post to see if ML will be an effective solution for your specific business case, explore multiple machine learning use cases, and learn approaches and strategies for implementing a successful ML-driven project.
Predicting house price using regression algorithm for machine learning
We have prepared a step-by-step guide of using regression algorithms in machine learning, to help you predict house prices for a given area.
BI and advanced analytics solutions for supply chain data analysis
Read a guide on BI and advanced analytics solutions for the supply chain. Learn how to implement each one and read about common tools and techniques for both.
Enhance existing digital capabilities at your organization
Tap into advanced digital solutions and deliver innovative customer service by starting an ML-driven project that matches your current needs.
How does your machine learning development firm choose a suitable ML model for each particular business case?
The Yalantis team performs a thorough feasibility study of different ML models to define their suitability for each unique business case. We take into account business objectives, business size, specific problems to solve, and data management practices. Our machine learning development team also evaluates the data sources a company needs to work with, data quality, and data integrity.
Once we know what problem to solve and the nature of the data we’re working with, we can choose the right ML approach and proceed with building machine learning models that help the business efficiently resolve critical issues or quickly achieve digital goals. But we also consider computational resources the project requires for successful development of the particular machine learning model. This way, we can set realistic deadlines and provide you with accurate project estimates.
How do your machine learning developers evaluate ML models?
Before releasing a machine learning model into production, Yalantis’ machine learning developers need to evaluate the model to make sure it performs as intended and achieves the required goals. Results the model can provide are true positives, true negatives, false positives, and false negatives. True positives and negatives indicate that the model performs correctly, whereas false positives and negatives indicate errors. There are a few common evaluation metrics that help us identify actual model performance. Metrics are selected according to the problem being solved. Here are some of the most common:
- Accuracy reveals the percentage of true positive results among all testing cases.
- Precision indicates the percentage of correct answers the model produces.
- Recall shows how many actual positive results were correctly identified.
- F1 score is a balancing metric that considers both precision and recall.
What are the common steps in a typical machine learning development project?
In general, the typical flow of machine learning development consists of:
- Problem statement or goal definition to start the project on the same page with internal and external stakeholders and validate whether the problem or goal can be solved or achieved using ML (or whether there are other options).
- Data collection, analysis, and cleaning is the next step to take once machine learning developers have identified the problem and seen the feasibility of ML.
- Model development and training is a consecutive step once the relevant data is ready for use.
- Model integration takes place when model performance is tested and the results correspond to the initial requirements.
- Model refining, monitoring, and maintenance are essential to make sure the designed machine learning algorithm is always up to date and meets relevant business needs.
What industries can benefit from ML development services?
Machine learning development services can be beneficial for practically any industry by solving complex business issues, increasing operational efficiency, enhancing service offerings, and improving customer loyalty. For instance, healthcare organizations can use machine learning algorithms for highly accurate medical image analysis, disease outbreak prediction, diagnostic support, and personalized patient treatment as well as clinical research. Financial institutions can benefit from efficient fraud detection, credit scoring, and algorithmic trading. Manufacturing companies can optimize production flow and resource allocation through predictive maintenance and quality control.
Transportation and mobility companies can use ML for real-time route optimization and to ensure high supply chain visibility. Entertainment and media companies can employ ML models for their recommendation engines, content, and sentiment analysis. Thus, nearly every industry can harness machine learning to drive innovation and gain a competitive advantage.
How to prepare for the successful launch of an ML-driven project with a machine learning app development company
To successfully collaborate with a machine learning app development company and launch profitable machine learning projects, it’s necessary to effectively prepare your business environment. You first need to ask yourself a few essential questions that can help you start the project on the same page with your internal stakeholders and external investors. As the next preparation step, you can encourage your employees to learn more about this technology and its benefits for their work. In this manner, you can overcome any anxiety among your in-house team and help them start working with this technology right after the ML solution rollout.
5 critical questions to ask yourself before engaging in an ML project
1. What are the project goals and objectives?
Before initiating any digital project, it’s crucial to have a clear understanding of your business goals and objectives. Define the specific problems you intend to solve with the ML-powered software and outline the qualitative and quantitative outcomes you expect to achieve after the release. This will guide your collaboration with your machine learning development services company and help you form a common business vision among all stakeholders.
2. Do you have access to quality data?
As with any data science solution, efficient machine learning development requires high-quality and correct data. Therefore, you should carefully assess the availability and quality of the data you’ll be using for training and testing the machine learning models. Validate whether your data is clean, relevant, and sufficient. You may need additional machine learning consulting services for proper data validation and to invest in the correct data collection and cleansing process to ensure your models deliver accurate results.
3. What is your budget and timeline?
Setting feasible budget and timeline constraints helps you start machine learning development with the right expectations and compose a capable team from the get-go. If you’re unaware how much typical machine learning projects cost, research this issue, ask competitors who have already launched similar projects, and make your own conclusions. Then, you can discuss these project aspects openly with your chosen machine learning development company to avoid any misunderstandings later in the project. An ML-driven project might involve iterative development and testing, so flexibility in the timeline is important.
4. Have you identified key stakeholders?
Identify key internal stakeholders, external investors, and product owners within or outside your organization who will be involved in your machine learning projects. You should designate responsibilities among all stakeholders to mitigate any issues right away. Effective collaboration among all critical stakeholders and the external machine learning and deep learning development company is vital for the project’s success. Building a trusted and clearly defined working environment even before the project starts is a winning approach, as it will save you much time on decision-making by clearly knowing who impacts which decisions and when each stakeholder needs to be involved.
5. Are your employees familiar with machine learning?
Check the existing machine learning knowledge and skills among your in-house team members. You may discover hidden talent, meaning additional support for your project. Alternatively, if you see a complete lack of understanding among your teams as to why your company needs to implement machine learning solutions, you can use this to your advantage and better define your initial project goals and objectives. You might also consider other data science approaches instead of machine learning such as computer vision, natural language processing, or deep learning development. But you can also think of providing basic training on machine learning concepts and terminology among your in-house team to help them transition to using new technology more smoothly.
Tips on how to prepare your in-house team for implementing machine learning solutions
Educate your team about machine learning
You can consider hosting workshops or training sessions with machine learning experts to introduce your team to fundamental machine learning development concepts and motivate them to adopt machine learning solutions more readily in the future. Explain to your employees how ML-enhanced applications work, their benefits, and their potential business impact. This will help make the technology more down-to-earth and create enthusiasm among employees. You can also encourage a knowledge-sharing environment in your organization to make people more willing to exchange ideas, information, and acquired knowledge on ML, fostering wider machine learning adoption within all of your departments.
Clarify roles and expectations
Clearly define the roles of each team member involved in the project regardless of whether they’re from your company or an external machine learning development company. For example, when working with an external company, you can ask a project manager on your machine learning project to actively involve you in forming the team so that you’re aware of each member’s critical responsibilities. But within your company, you should also outline each member’s responsibilities, prepare team members for the duties they need to fulfill, and handle all of their expectations or objections, emphasizing collaboration and regular updates. This will promote accountability and a sense of ownership, driving the project forward.
Encourage open communication
Foster a working environment with open and honest communication, allowing your employees to feel at ease asking diverse questions, sharing concerns, and offering ideas. Regular meetings and status updates can keep everyone aligned and informed about the project’s progress and the company’s goals with regard to implementing diverse machine learning solutions. If you keep your team members actively engaged in the process of machine learning integration, you help them stay committed to machine learning development for longer and increase their motivation as they contribute to the company’s overall success.
Manifest cross-functional collaboration
ML projects often require collaboration between different departments, such as IT, marketing, and operations, especially in large companies and enterprises. Working with a competent machine learning development services provider can help you easily set up a cross-functional support process for your machine learning project. For instance, Yalantis experts can build a thorough ML implementation roadmap that touches upon your entire organization with clear iterations and the engagement of each department. Thus, you can ensure end-to-end machine learning implementation at your company. By encouraging cross-functional ML implementation, you can ensure your whole company grows in the same direction.
Facilitate continuous learning
Machine learning is a dynamic and rapidly changing field, and staying up to date is essential. Support and create continuous learning initiatives among your employees by providing easily available resources and training materials such as online courses, webinars, workshops, and industry-related articles. You can also create Slack channels or even whole communities for sharing recent changes and news in the realm of machine learning solutions. These initiatives will empower your employees to contribute effectively to the project’s evolution and successful release.
Concluding thoughts
Preparing your business environment for the successful development of an ML-powered software product requires you to invest time and resources into learning why this technology can be beneficial for your particular business and then clearly conveying all of these benefits to your employees. By addressing the key questions mentioned above, you can clearly see where your business stands in digital data management and what steps you can take to prepare for efficient and smooth integration of machine learning solutions. Additionally, focusing on employee onboarding by educating your team about machine learning will help you build an innovative data management approach and create a more digitally-driven culture.
Machine learning technology isn’t the only digital solution you can promote among your team members. You can follow the same principles with any artificial intelligence or data science solutions like natural language processing or deep learning. Advanced digitally-driven data management techniques at the company-wide level are gaining ground in the current business world. By implementing custom machine learning or data science solutions, you can create actionable decision models and extract valuable insights from any dataset to assess your company’s operations and take immediate mitigation actions in case of any critical issues.
As a business owner or the manager of a particular department, you should stay in the loop about the latest changes in the data science field to readily implement solutions into your everyday workflow and stay ahead of the competition.