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 industry and vertical in the world. And, with our help, you can leverage their full potential and seamlessly integrate them with your day-to-day operations.
BairesDev hires the Top 1% of tech Talent in the region to provide the highest-quality software services in the market. Our goal is to help you understand and seize all of the high-value opportunities that AI and Machine Learning bring to the table. No matter the size or complexity of the project, we deliver custom-built technology solutions designed to exceed your expectations.
Our ML Services
We work with the Top 1% of tech Talent to deliver the highest-quality AI/ML solutions.
Custom AI-Driven Enterprise Solutions
Access the full spectrum of artificial intelligence solutions with our enterprise services package. From personalized experiences and augmented operations to predictive models and collaborative intelligence, we deliver custom-built AI software designed to accelerate your company on the back of next-gen technology.
AI-Driven Processes
Build 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 outcome effectiveness. With AI-Driven Processes, your business will run on data-driven initiatives that generate value consistently and predictably.
Data Provisioning
The best AI/ML systems have robust and scalable data infrastructures behind them. 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 company or have your current implementation tested and assessed by the best AI and ML engineers in the industry. Our testing methodology guarantees the reliability and accuracy of your Machine Learning model via model testing, regional validation, testing with real-world target audiences, and performance reporting.
Deep Learning Technologies
Augment the capabilities of your business, employees, products, services, and processes with cutting-edge technologies driven by AI. These include demand prediction, anomaly detection, fraud detection, medical diagnosis, face detection, object identification, optical character recognition, person tracking, augmented reality, voice recognition, text mining, sentiment analysis, and many others.
Human to Machine & 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 secure automated processes. Interactive next-gen chatbots, digital assistants, voice recognition, intention recognition, and programmed decision making.
BairesDev Best Practices
Artificial intelligence and Machine Learning can be fundamental components of successful software development and delivery. While there are many approaches towards achieving this goal, these are the Best Practices that we follow and recommend at BairesDev and have proven to be successful across many customer engagements. Our AI/ML Circle is the center of excellence that defines and maintains these standards and ensures that knowledge and practices are shared across our organization. Throughout our engagements we:
- Collaborate With the Product Owner: To understand the context of the problem and the business impact the solution will have in order to define a clear objective for the machine learning service.
- Determine the Most Appropriate Use Case: In order to fulfill the defined objective.
- Conduct a Thorough Examination of the Available Data: This includes assessing the quantity, quality, and sources of the data to ensure that we have or can obtain the appropriate data required to fulfill the objective.
- Engage in Data Pre-processing and Feature Engineering: We document the feature engineering process to guarantee that the model inputs are as clear as possible.
- Architect Loosely Coupled Services: By keeping the training and predicting services separately, we can more easily isolate errors resulting from changes in the code.
- Perform Sanity Checks: Before models are released into production.
- Understand the Frequency: With which the model should be updated and the business impact of the update frequency.
- Apply Machine Learning Operations (MLOps): By leveraging continuous integration and continuous delivery to ensure that code changes in the machine learning service will not negatively impact the application.
- Start With a Low-Complexity Machine Learning Model: That addresses 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 the performance of the model.
- Use Experimentation Techniques: To test and improve the model.
- Create Pipelines: That will orchestrate these tasks.
- Perform Feature Importance Analysis: To make machine learning models explainable and reduce dimensionality when possible.
- Adapt: According to the infrastructure changes that may be necessary.
- Limit Technical Debt: By cleaning up features we are no longer using.
At BairesDev, the machine learning engineer participates actively in all stages of the application development to guarantee that the solution will be optimized for each specific case.
What Can Your Company Do With Machine Learning?
A lot! Here are some of the highlights.
- Natural Language Processing: Process data from human language like never before.
- Smart Assistants and Chatbots: Create efficient and engaging automated interactions.
- Process Automation: Use cutting-edge technology to maximize efficiency.
- Smart Segmentation: Identify and track data from customer segments automatically.
- Smart Supply Chain: Optimize and automate supply chain processes.
- Software for Robotics: Turn robots into smart robots with artificial intelligence.
- Inventory Forecasting: Accurately predict future inventory levels and requirements.
- Computer Vision: Gather complex sets of data from visual environments.
- Recommendation Engines: Predict and streamline the search experience of your users.
- Predictive Monitoring: Detect, raise flags, and prevent issues with smart monitoring.
- Social Intelligence: Add human-like behavior to augment AI capabilities.
- Smart IoT: Unleash the power of the Internet of Things with AI-driven datasets.
Our Process
This is how to transform the idea in your head into a reality
AI Model Development
Now, the fun part. The AI model development starts with a Proof of Concept developed by our expert AI engineers. Our team will define the project scope, tech stack, implementation methodology, software architecture, tools, and quality assurance requirements.
AI Model Deployment
The first working 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 corrections, enhancements, and real-world testing.
Integration
After the initial deployment, we can begin to focus on the complete integration of the AI model and start the self-learning and self-improvement process. We provide continuous support on deployed models to guarantee the fulfillment of your project’s goals.
Understanding the Context
First of all, we analyze where your company is standing. The most important part of this stage is to understand the business and data requirements of the project and use them to list quantifiable goals and their consequent outcomes.
Data Engineering
With a clear picture of the context, we can begin collecting all internal and external data that is relevant to the AI implementation. This process implies curating, cleaning, and contextualizing the gathered information to build a comprehensive data lake.
AI Model Development
Now, the fun part. The AI model development starts with a Proof of Concept developed by our expert AI engineers. Our team will define the project scope, tech stack, implementation methodology, software architecture, tools, and quality assurance requirements.
AI Model Deployment
The first working 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 corrections, enhancements, and real-world testing.
Integration
After the initial deployment, we can begin to focus on the complete integration of the AI model and start the self-learning and self-improvement process. We provide continuous support on deployed models to guarantee the fulfillment of your project’s goals.
Understanding the Context
First of all, we analyze where your company is standing. The most important part of this stage is to understand the business and data requirements of the project and use them to list quantifiable goals and their consequent outcomes.
Data Engineering
With a clear picture of the context, we can begin collecting all internal and external data that is relevant to the AI implementation. This process implies curating, cleaning, and contextualizing the gathered information to build a comprehensive data lake.
AI Model Development
Now, the fun part. The AI model development starts with a Proof of Concept developed by our expert AI engineers. Our team will define the project scope, tech stack, implementation methodology, software architecture, tools, and quality assurance requirements.
AI Model Deployment
The first working 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 corrections, enhancements, and real-world testing.
Integration
After the initial deployment, we can begin to focus on the complete integration of the AI model and start the self-learning and self-improvement process. We provide continuous support on deployed models to guarantee the fulfillment of your project’s goals.
Frequently Asked Questions
Here are some FAQs on Artificial Intelligence and Machine Learning
How are machine learning and artificial intelligence related?
A lot of people get these two confused, so here is the definitive answer: machine learning is a subset of artificial intelligence. AI involves all developments of 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 carry out actions that have not been programmed explicitly.
Which is best, AI or ML?
That depends on what your company is trying to achieve. Both are great technologies with unlimited potential and tons of 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 that logic, one could argue 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 these means.
- Supervised learning happens when we provide the machine with a ton of information about a case and its outcome, and we tell it whenever its results are correct—hence, all of the work done by the machine 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 to recognize patterns in data, find anomalies, cluster problems, and help us make decisions.
- Reinforcement learning is probably the closest to how we as humans learn. In this case, the algorithm or the agent learns continually from its environment by interacting with it and gets a positive or a negative reward based on its action.
Why are AI and machine learning important for businesses?
AI and Machine Learning are important to businesses because they are redefining the ways we all do business. Making use of these technologies has the potential to completely change how your company operates and how it relates to the customers, by harnessing the true power of data collection, data processing, automation, and all of the resulting insights. At this time, using AI and other cutting-edge technologies is defining which businesses become market leaders—and which are trying to catch up.
