Customized solutions for your data challenges: Harness the power of machine learning to gain actionable insights, automate processes, and transform your business operations.
Artificial Intelligence and Machine Learning are two of the most critical drivers of innovative business opportunities today. These technologies are accelerating the evolution of processes, products and services in virtually every sector and vertical in the world. And with our help, you can harness their full potential and integrate them seamlessly into your daily operations.
Our ML services
We work with the top 1% of technology talent to deliver the highest quality AI/ML solutions.
AI-powered personalized business solutions
Access the full range of artificial intelligence solutions with our enterprise services suite. From personalized experiences and augmented operations to predictive models and collaborative intelligence, we provide custom AI software designed to accelerate your business based on next-generation technology.
AI-Driven Processes
Create a competitive advantage in your industry by integrating artificial intelligence and machine learning into your business processes. We design powerful systems focused on innovation, readiness and effectiveness of results. With AI-driven processes, your company will run on data-driven initiatives that generate value in a consistent and predictable way.
Data provisioning
The best AI/ML systems have robust and scalable data infrastructures. Implement a comprehensive data culture that covers all processes related to information management, including data collection, data mining, data creation, data aggregation, exploration, linguistic assets, and natural language processing.
Machine Learning Models
Design a custom machine learning model for your business or test and evaluate your current implementation by the industry's best AI and ML engineers. Our testing methodology ensures the reliability and accuracy of your Machine Learning model through model testing, regional validation, testing with real audiences, and performance reporting.
Deep Learning Technologies
Enhance the capabilities of your business, employees, products, services and processes with cutting-edge AI-powered technologies. These include demand forecasting, anomaly detection, fraud detection, medical diagnosis, face detection, object identification, optical character recognition, people tracking, augmented reality, speech recognition, text mining, sentiment analysis and many others.
Human to Machine and Machine to Machine
Automation is the future of business. We design, develop and implement custom human-to-machine and machine-to-machine AI solutions that create interactive, flexible and safe automated processes. State-of-the-art interactive chatbots, digital assistants, voice recognition, intent recognition and programmed decision making.
BairesDev best practices
- Collaborate with the product owner: Understand the context of the problem and the business impact the solution will have to define a clear objective for the machine learning service.
- Determine the most appropriate use case: To meet the defined objective.
- Perform a thorough examination of available data: This includes evaluating the quantity, quality, and sources of data to ensure we have or can obtain the appropriate data needed to accomplish the objective.
- Engage in data preprocessing and feature engineering: We document the feature engineering process to ensure model inputs are as clear as possible.
- Loosely coupled architectural services: By keeping training and prediction services separate, we can more easily isolate errors resulting from code changes.
- Perform sanity checks: Before models are released into production.
- Understand frequency: How the model should be updated and the business impact of update frequency.
- Apply Machine Learning Operations (MLOps): Leveraging continuous integration and continuous delivery to ensure that code changes to the machine learning service will not negatively impact the application.
- Start with a low-complexity machine learning model: This solves the business problem before using a more complex one. Examples of less complex models include using a linear regression or logistic regression model.
- Identify objective metrics: To measure model performance.
- Use experimentation techniques: To test and improve the model.
- Create pipelines: This will orchestrate these tasks.
- Perform feature importance analysis: To make machine learning models explainable and reduce dimensionality when possible.
- Adapt: According to infrastructure changes that may be necessary.
- Limit technical debt: Cleaning up resources we no longer use.
What can your company do with machine learning?
Quite! Here are some of the highlights.
- Natural language processing: Process human language data like never before.
- Intelligent assistants and chatbots: Create efficient and engaging automated interactions.
- Process automation: Use cutting-edge technology to maximize efficiency.
- Smart Segmentation: Automatically identify and track customer segment data.
- Smart supply chain: Optimize and automate supply chain processes.
- Software for Robotics: Transform robots into intelligent robots with artificial intelligence.
- Inventory Forecast: Accurately predict future inventory levels and requirements.
- Computer Vision: Bring together complex sets of data from visual environments.
- Recommendation engines: Predict and simplify your users' search experience.
- Predictive Monitoring: Detect, raise flags and prevent problems with intelligent monitoring.
- Social intelligence: Add human-like behavior to increase AI capabilities.
- Smart IoT: Unleash the power of the Internet of Things with AI-powered datasets.
Our Process
This is how you turn the idea in your head into reality
AI model development
Now for the fun part. The development of the AI model begins with a Proof of Concept developed by our expert AI engineers. Our team will define the project scope, technology stack, implementation methodology, software architecture, tools, and quality assurance requirements.
AI model deployment
The first functional version of the AI model will be deployed following the implementation methodology defined in the previous step. This will be the time to make any stabilization fixes, improvements, and real-world testing.
Integration
After the initial deployment, we can begin to focus on fully integrating the AI model and begin the process of self-learning and self-improvement. We provide continuous support in the models implemented to ensure that your project objectives are met.
Understanding the context
Firstly, we analyze where your company is. The most important part of this step is understanding the project's business and data requirements and using them to list quantifiable goals and their consequent results.
Data Engineering
With a clear picture of the context, we can start collecting all the internal and external data that is relevant to implementing AI. This process entails curating, cleaning, and contextualizing the information collected to build a comprehensive data lake.
AI model development
Now for the fun part. The development of the AI model begins with a Proof of Concept developed by our expert AI engineers. Our team will define the project scope, technology stack, implementation methodology, software architecture, tools, and quality assurance requirements.
AI model deployment
The first functional version of the AI model will be deployed following the implementation methodology defined in the previous step. This will be the time to make any stabilization fixes, improvements, and real-world testing.
Integration
After the initial deployment, we can begin to focus on fully integrating the AI model and begin the process of self-learning and self-improvement. We provide continuous support in the models implemented to ensure that your project objectives are met.
Understanding the context
Firstly, we analyze where your company is. The most important part of this step is understanding the project's business and data requirements and using them to list quantifiable goals and their consequent results.
Data Engineering
With a clear picture of the context, we can start collecting all the internal and external data that is relevant to implementing AI. This process entails curating, cleaning, and contextualizing the information collected to build a comprehensive data lake.
AI model development
Now for the fun part. The development of the AI model begins with a Proof of Concept developed by our expert AI engineers. Our team will define the project scope, technology stack, implementation methodology, software architecture, tools, and quality assurance requirements.
AI model deployment
The first functional version of the AI model will be deployed following the implementation methodology defined in the previous step. This will be the time to make any stabilization fixes, improvements, and real-world testing.
Integration
After the initial deployment, we can begin to focus on fully integrating the AI model and begin the process of self-learning and self-improvement. We provide continuous support in the models implemented to ensure that your project objectives are met.
common questions
Here are some frequently asked questions about artificial intelligence and machine learning
How are machine learning and artificial intelligence related?
Many people confuse these two, so here's the definitive answer: Machine learning is a subset of artificial intelligence. AI involves all developments in technologies created to simulate human behavior. Machine learning is part of this, and what makes it unique is that ML algorithms are designed to automatically learn from past data and perform actions that have not been explicitly programmed.
Which is better, AI or ML?
This depends on what your company is trying to achieve. Both are excellent technologies with unlimited potential and many use cases, but as established in the previous question, machine learning is a subset of artificial intelligence, so any ML project is also an AI project. By this logic, it can be argued that AI is best as it includes a wider range of technologies and implementations.
What are the 3 types of machine learning?
Broadly speaking, machine learning algorithms can be categorized into 3 types: supervised learning, unsupervised learning, and reinforcement learning. Here's a quick look at what each of them means.
- Supervised learning happens when we give the machine a ton of information about a case and its outcome, and we always tell it that its results are correct – so all the work the machine does is supervised.
- Unsupervised learning is the opposite, as there is no help from AI engineers and the computer has to learn on its own. Unsupervised learning is extremely useful for recognizing patterns in data, finding anomalies, grouping problems, and helping us make decisions.
- Reinforcement learning is probably the closest to how we humans learn. In this case, the algorithm or agent continuously learns from its environment by interacting with it and obtains a positive or negative reward based on its action.
Why are AI and machine learning important for businesses?
AI and machine learning are important for companies because they are redefining the way we all do business. Using these technologies has the potential to completely change the way your business operates and how it engages with customers, harnessing the true power of data collection, data processing, automation and all the resulting insights. Right now, the use of AI and other cutting-edge technologies is defining which companies become market leaders and which are playing catch-up.
