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Software Engineering vs Software Development Although both may seem very similar, there are actually many
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 IT 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.
We work with the Top 1% of IT Talent to deliver the highest-quality AI/ML 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.
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.
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.
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.
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.
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.
A lot! Here are some of the highlights.
This is how to transform the idea in your head into a reality
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.
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.
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.
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.
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.
Here are some FAQs on Artificial Intelligence and Machine Learning
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.
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.
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.
This technology falls under the blanket of Artificial Intelligence and serves as a data analysis method used to automate analytical model building.
The idea behind Machine Learning is that systems can learn from data, identify patterns, and make decisions without having to rely (too much) on human interaction. One of the most obvious examples of machine learning is the autonomous vehicle. Automotive manufacturers (such as Tesla) are spending billions of dollars to finally achieve the goal of the self-driving car.
But this isn't an easy challenge. Because we're talking about automobiles, these industries must pass vigorous safety inspections and tests, as well as get beyond current restrictions in state, federal, and global laws. On top of that, they'll need to have the infrastructure in place to make self-driving cars happen.
But before those manufacturers can pull that off, they need to actually achieve the goal of the truly autonomous vehicle. On top of that, those manufacturers must be able to answer the question, "Why do we need autonomous vehicles?" One of the biggest answers the industry has is lowered costs. Autonomous delivery vehicles would drastically cut down the price of shipping products.
To achieve this, machine learning must be applied successfully. Machine learning makes it possible for those manufacturers to achieve higher levels of driver assistance, including how the vehicle perceives the world around it. For a vehicle to become autonomous, it must be able to recognize things like obstacles, traffic patterns, speed limits, routes, and even weather patterns. For that, a vehicle employs numerous sensors around the vehicle, which collect data to be interpreted by the onboard system.
This gets problematic if the machine learning algorithm mistakenly interprets a stop sign for a yield sign, which could lead to catastrophe. This is why machine learning is not only crucial but challenging.
Machine learning algorithms find patterns in data that generate specific insight for a particular system. Take our autonomous vehicle example from above. The machine learning algorithm must be able to tell the difference between the various signage found on streets. Without the ability to recognize those signs, a car wouldn't know the difference between a stop sign, a speed limit sign, or a sign indicating a turn.
The issue is way more complicated, mainly because machine learning isn't just applied to self-driving cars, but to medical diagnosis, stock trading, energy forecasting, weather predictions, market trends, speech translation, face recognition, sentiment analysis, social media analysis, fraud detection, and product recommendation, among many other things.
Machine learning uses 2 different techniques for the learning process:
Once you've decided the type of machine learning technique you want to use, the next step is to select the right programming language to build your systems. Keep in mind, though, that not every language is useful for machine learning.
The top 10 most popular programming languages that can be applied to machine learning include:
It's not a matter of if, but when your company will need to add machine learning into your systems. Before you get too far behind this curve, start planning for the eventuality, so you can catch up to the competition. To do that, you're going to need to hire the right people.
A neural network is a simplified model of the human brain. This network is a combination of algorithms that aim to identify patterns in data sets through a process that mimics how our brain operates.
Instead of the standard Bayes theorem:
P(yi | x1,..., xn) =P(yi)P(x1,..., xn | yi)(P(x1,..., xn)
Naive Bayes uses the naive conditional independence assumption that each xiis independent and simplifies this relationship to:
P(xi | yi, x1, ..., xi-1, xi+1, ...., xn) = P(xi | yi)
PCA stands for Principal Component Analysis and is used for dimensional reduction.
The Support Vector Machine algorithm is a supervised machine learning model that is capable of performing linear or non-linear classification, regression, and outlier detection.
Cross-validation is a method of splitting data into 3 parts: training, testing, and validation.
Bias informs there's an inconsistency in data that's usually the result of the algorithm being in favor or against a particular view, perspective, or idea. Basically, bias appears because the learning model uses wrong assumptions that are then replicated in how the algorithm itself operates.
Classification is used to produce discrete results and classify data into specific categories. Regression deals with continuous data.
Collaborative filtering is a type of recommendation system that predicts new content by matching an individual's interests. Content-based filtering is focused on the preferences of a user.
We are looking for an expert in machine learning to help extract value from our data. You will be in charge of this process from collecting, cleaning, and preprocessing data to training models and deploying them to production. The ideal candidate will be passionate about artificial intelligence and stay up to date with the latest developments in the field.
Software Engineering vs Software Development Although both may seem very similar, there are actually many
Outsource IT Services with the Most Talented Engineers in the Industry The business approach to
Learn About Software Engineering Firms Software engineering firms have become a pillar of the modern
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