Machine Learning Development Services
Innovative solutions. Insightful choices driven by data.
In 2 weeks, you can access the top 1% LATAM tech talent. Create machine learning models that perform complex calculations, from fraud detection to predicting human behaviour.
Machine Learning Development Services We Provide
Machine learning is a powerful tool that can be used by businesses in a wide range of industries. From healthcare to manufacturing, we have solutions available. Our ML solutions improve decision-making and operations.
Custom Machine Learning Model Development
Discover business insights. Personalize user experience. Improve prediction accuracy. We build custom machine learning solutions that help you make data driven decisions.
We have data scientists and engineers who develop models that are specific to you. Our ML models are built using Python, R and Java programming languages, as well as machine learning technologies such TensorFlow, PyTorch, and TensorFlow.
Natural Language Processing Services
NLP is at the heart of chatbots and other tools that detect spam. It allows systems and users to communicate more effectively.
We can integrate NLP into our software using tools such as the Python Library Natural Language Toolkit.
Predictive and Real-Time Analytics
Predictive analytics uses machine learning to find patterns, insights and relationships in data.
We collect and process data using tools such as SPSS and Hadoop to create predictive models. Forecasts and business decisions are based on accurate forecasts.
ML Integration
Use machine learning models to improve existing software and systems.
We integrate pre-trained models using APIs and SDKs into applications in order to add functionality such as image recognition and voice-to-text. We can also create custom ML models that we integrate directly into your software.
Computer Vision Services
Many industries, including healthcare, entertainment and agriculture, have started to rely heavily on object detection and scene recognition. These processes are used for security, activity monitoring and other operations. Computer vision is used in all of them.
In this field, computers can derive insights from inputs of visual data and automate the processes associated with human vision. We integrate computer vision systems in applications and devices using techniques such as Convolutional Neuronal Networks (CNNs). We can, for example, use them to automate checkout systems that recognize items and allow fast checkouts without manual intervention.
Deep Learning Services
Have you ever wondered how Netflix or Amazon are so good at suggesting products? Deep learning is the reason. Deep learning is a subset of machine-learning that uses artificial neural networks to solve complex problems.
We use tools such as TensorFlow PyTorch and Keras to design neural networks, configure learning processes, train models, and deliver deep-learning solutions. These systems can be used for everything from medical imaging to shopping recommendation systems.
Case Study
Limeade required software engineering support as well as help with implementing machine-learning algorithms into its core functionality. Our team of engineers specialized in web app development, support for legacy software, and business analytics. They focused on migration of Limeade Classic to Limeade ONE as well as the app’s migration into microservices. The entire Limeade Case Study can be read.
Key Facts About Machine Learning Development
Machine learning has emerged as an important field that enables businesses to accomplish tasks they once believed to be impossible. Organizations across industries are leveraging its immense potential. It’s complex, but it is also very powerful. Outsourcing machine learning services will allow you to take advantage of the expertise of providers who are experts in ML technologies and techniques.
Outsourcing has 5 advantages:
You may not have the necessary ML expertise in your team. You can find ML and data scientists with specialized machine learning knowledge if you look outside your immediate area.
Outsourcing providers are able to complete complex machine learning projects faster than their in-house counterparts, thanks to the established workflows and available resources.
Mittigate risks: Outsourcing companies will share your risk. These companies are also familiar with the regulations governing AI, ML, and data quality.
Scale easily: Adapt quickly as your needs change. You can easily scale up or down with a flexible partner.
Global Talent: Work with a talent pool of global talents and perspectives.
Adopting machine-learning technologies can be crucial for the survival of many businesses in today’s data driven landscape. It can be used to transform business operations and facilitate better decision-making.
Best Practices for Machine Learning Development
As machine learning is always evolving, it’s vital to keep up with the latest tools and techniques. Here are some of the best practices that we use.
Part 1: Investigate the Problem Domain
The ML improvement procedure starts with outlining your necessities and the methods for constructing a model so as to meet them.
Understand the Problem
Define clean objectives, KPIs, and fulfillment criteria to inform your information of the problem area.
Choose the Appropriate Model
Experiment with unique ML algorithms or architectures to find the first-rate suit to address your trouble.
Evaluate the Model
Select evaluation metrics based on the type of hassle.
Obtain and Clean the Data
Source statistics from credible sources, check it for consistency and accuracy, and deal with any missing data or inconsistencies.
Establish Relevant Features
Feature engineering affects model overall performance. Choose features which might be applicable to the model.
Part 2: Hone the Machine Learning Model
Here’s how we construct your ML solution and account for its nuances.
Conduct Data Preprocessing
Data preprocessing involves assessing the great of your raw facts. This process consists of encoding categorical variables and managing lacking values.
Standardize the Data
Improve the steadiness of ML algorithms by using normalizing or standardizing the data.
Evaluate and Improve the Model
Continuously determine and iteratively enhance your ML model, checking out it against new records to reveal its performance and regulate as important.
Perform Exploratory Data Analysis (EDA)
EDA—including visualizing distributions, correlations, and anomalies—allows tell choices about characteristic engineering and model selection.
Account for Scalability
You’ll need to deal with a developing volume of information, so it’s vital to build the version with scalability in mind. Cloud-based totally answers can help with scalability.
Part 3: Test the Model
QA testing is important for ensuring the ML version’s efficacy, security, overall performance, and functionality.
Perform Bias and Fairness Testing
Assess your version for biases. We use fairness metrics and trying out strategies to discover issues in predictions related to elements like gender, race, and age.
Test for Robustness
Evaluate how properly the version handles sudden outputs. Assess stability and carry out exploratory trying out to recognize how the model makes predictions.
Conduct Security Testing
Identify ability vulnerabilities and put in force measures to protect your records.
Why Choose Accel for Machine Learning Development
We use an intensive vetting process to ensure that we hire best the pinnacle 1% of device learning expertise. Our engineers have understanding within the gear and technology in the discipline, as well as understanding of the ultra-modern information and traits.
What do virtual assistants, facial recognition equipment, and health trackers have in common? They’re all powered by system learning. We have revel in operating on custom ML projects like these and lots of others. We’ll collaborate with you to create a unique machine mastering solution.
Machine learning is constantly evolving, and also you want to reach market speedy to preserve up. Our engineers will accelerate your timeline, working swiftly to construct and refine your ML answer. This helps you stay aggressive within the unexpectedly growing AI area.
Our process. Simple, seamless, streamlined.
We begin via discussing the trouble you’re seeking to clear up, determining the ML venture, and figuring out metrics to assess the overall performance of the version. We’ll additionally speak how those goals align with your business goals.
We’ll create a plan to collect and rework statistics to inform your solution. We will paintings collectively to decide which engagement model is maximum suitable for your business: group of workers augmentation, dedicated teams, or quit-to-give up software outsourcing. Then, we’ll pick out the best-fit ML engineers.
Our engineers start running on your ML solution. No AI improvement technique is linear, however commonly, we’ll conduct an Exploratory Data Analysis, choose the model, educate the information, compare, and install the answer. We’ll preserve you informed of our development at every flip.
Frequently Asked Questions (FAQ)
Outsourcing device gaining knowledge of improvement includes contracting an outside corporation to finish ML tasks collaboratively along with your in-residence team or autonomously. We provide 3 unique engagement fashions: staff augmentation, committed groups, and give up-to-end software outsourcing.
There are many varieties of device mastering apps you may build across industries and niches. Examples consist of:
- Spam detection tools
- Recommendation engines
- Virtual assistants
- Chatbots
- Natural language processing (NLP) software such as speech recognition tools
- Fraud detection tools
Machine getting to know is a subfield of synthetic intelligence. While AI is a larger umbrella term encompassing a couple of branches, ML mainly concerns the usage of information technological know-how and algorithms to mirror how human beings study, adapt, and develop as the model accesses greater facts.