Machine learning company

Machine learning
development services

ML solutions you can trust. Outcomes you can measure.

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What is machine
learning?

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Machine learning algorithms are a subset of artificial intelligence that enable systems to learn and improve from experience without being explicitly programmed.

It’s teaching a computer to recognize patterns and make decisions, similar to how humans learn from past experiences. As if when you recognize a friend’s face in a crowd, machine learning algorithms can identify objects in images, predict stock market trends, or recommend tailored products to customers.

Why choose Devico for custom ML development services

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High retention rate

96%

We go beyond the 80% industry norm with reliable, expert support.

Wide expert network

3000

Access to over 3000 engineers and AI experts.

Proven track record

500,000

Over 500,000 man-days successfully delivered.

Support

24/7

Highly experienced management team available around the clock.

Drive your business forward

Businesses that implement AI and machine learning could see at least a 15-20% increase in profitability

How does it work?

01

Start with the right data

Gathering relevant data from various sources.

Start with the right data

02

Make data usable

Cleaning and organizing data to make it suitable for analysis.

Make data usable

03

Teach the machine

Using algorithms to train a model on the prepared data.

Teach the machine

04

Test for excellence

Assessing the model’s accuracy and performance.

Test for excellence

05

Launch into the real world

Integrating the model into business processes for practical use.

Launch into the real world

06

Evolve and improve

Continuously monitoring the model’s performance and updating it as necessary.

Evolve and improve

Industry-specific
ML development

Healthcare

Statistics show that the healthcare sector can save as much as 70% of drug discovery costs with the applications of ML in healthcare.

The areas with the most significant machine learning use potential in healthcare are ML-based diagnosis, early pandemics identification and imaging diagnostics.

Use cases:

  • Disease identification and diagnosis
  • Treatment personalization
  • Remote monitoring and wearable devices
Finance & insurance

With the low data SNR and large volumes of legacy data, machine learning is a right tool for the financial ecosystem.

Almost 70% of financial services companies already use ML, forcing other financial institutions to rethink their traditional approaches to handling financial activities on the market.

Use cases:

  • Algorithmic trading
  • Underwriting and credit scoring
  • Financial monitoring
Retail

Machine learning development allows retailers to increase their sales by 20% and reduce inventory costs by 30%.

Amazon’s recommendation engine, powered by machine learning, accounts for at least 35% of its total sales by suggesting products based on customer behavior.

Use cases:

  • Demand forecasting
  • Inventory management
  • Personalized marketing
Manufacturing

In the manufacturing industry there are 2 critical pillars - quality control and process optimization. With ML applications, manufacturers are set to achieve unparalleled precision and efficiency in these fields.

According to a McKinsey report, companies adopting AI and ML-driven strategies in manufacturing have witnessed a 30% to 50% reduction in machine downtime, 15% to 30% improvement in labor productivity, 10% to 30% increase in throughput, and 10% to 20% decrease in the cost of quality.

Use cases:

  • Predictive maintenance
  • Quality control
  • Supply chain optimization

Save time and money

Reduce customer-processing costs by 45% with ML and demand to speak to an agent by 25%

Advanced machine learning technologies

Criteria

Deep learning

Transfer learning

Neural networks

Definition

A subset of ML with neural networks having multiple layers.

Reusing a pre-trained model on a new but similar task.

Computational models inspired by human brain structure.

Goal

Automatically discover representations from data, particularly for complex tasks.

Improve learning efficiency by leveraging existing knowledge.

Simulate brain functions to recognize patterns and make decisions.

Algorithms in focus

Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs).

Fine-tuning, domain adaptation.

Feedforward Neural Networks, Multilayer Perceptrons (MLPs), Deep Belief Networks (DBNs).

Data requirement

Requires vast amounts of labeled data.

Requires pre-trained models and less data than training from scratch.

Requires structured data, scalability with large datasets.

Advantages

High accuracy for tasks like image and speech recognition.

Reduced training time and improved performance on related tasks.

Flexibility to model complex relationships and interactions.

Applications

Image classification, natural language processing (NLP), autonomous vehicles.

Medical imaging, natural language translation, personalized recommendations.

Pattern recognition, predictive analytics, game AI.

Techniques

Backpropagation, dropout regularization, data augmentation.

Model fine-tuning, transfer learning architectures like BERT and GPT.

Activation functions (ReLU, sigmoid), network architectures (feedforward, recurrent).

Complexity

Moderate.

High.

Very High.

Machine learning algorithms we use

Linear regression

Used for predictive analysis. It models the relationship between a dependent variable (target) and one or more independent variables (features) by fitting a linear equation to the observed data.

For instance, predicting housing prices based on factors like square footage, number of bedrooms, and location involves finding the best-fit line that minimizes the difference between the actual and predicted values.

The simplicity of linear regression makes it a powerful tool for understanding relationships within data, though it assumes a linear relationship that might not always be present.

Logistic regression

Used for binary classification problems, where the outcome is a categorical variable with two possible values, such as spam vs. not spam or disease vs. no disease. Uses a logistic function to model the probability of the default class and outputs values between 0 and 1.

For example, logistic regression can predict whether a customer will buy a product based on their browsing behavior and purchase history. The sigmoid function applied in logistic regression helps in mapping predicted values to probabilities, making it suitable for binary outcome predictions.

Clustering

Unsupervised learning technique that groups data points into clusters based on their similarities. Useful for identifying hidden patterns and segmenting data for further analysis. Widely used in market segmentation, image compression, and anomaly detection.

Another popular method, hierarchical clustering, builds nested clusters by progressively merging or splitting them based on a distance metric.

Decision trees

A versatile Machine learning algorithm capable of performing both classification and regression tasks. They split the data into subsets based on the value of input features, creating a tree-like structure where each node represents a feature, each branch a decision rule, and each leaf a target outcome.

Can be used to classify emails as spam or not spam by evaluating features like the presence of certain keywords. Decision trees are intuitive and easy to interpret, but they can become complex and prone to overfitting, especially with noisy data.

Random forest

Enhance the predictive power and robustness of decision trees: construct multiple decision trees during training and merge their results. Each tree in the forest is trained on a random subset of the data and features, which helps in reducing overfitting and improving generalization.

In predicting loan defaults, a random forest would aggregate predictions from numerous decision trees, resulting in a more accurate and stable prediction model.

Be beyond expectations

65% of companies who are planning to adopt machine learning say the technology helps businesses in decision-making

Differences of AI platforms for Machine learning

MLOps capabilities


  • check_circleAutomated deployment and CI/CD pipelines

    • Examples: AWS SageMaker, Google AI Platform.
  • check_circleModel monitoring and management

    • Tracks model performance, accuracy, and drift in real time.
    • Examples: Azure Machine Learning.
  • check_circleScalability and resource management

    • Dynamically scales resources to handle large datasets and complex models.
    • Examples: Google Kubernetes Engine (GKE), Amazon EKS.

Generative AI capabilities


  • check_circlePre-trained models and customization

    • Access to advanced models like GPT-3 for text generation or GANs for image synthesis.
    • Examples: OpenAI’s GPT-3 API, NVIDIA’s StyleGAN.
  • check_circleIntegration with existing workflows

    • Seamless integration with your current business processes via APIs and SDKs.
    • Examples: IBM Watson.
  • check_circleEthical AI and bias mitigation

    • Tools for auditing and reducing biases in generative outputs.
    • Examples: Microsoft’s Fairlearn, IBM’s AI Fairness 360.

Get in touch

Drop us a line about your project and we will contact you within a business day

Our locations

New York

HQ

521 Fifth Ave, NY 10175

+1 805 491 9331

London

Sales

9 Brighton Terrace, SW9 8DJ

+44 1922 214429

Warsaw

R&D

Towarowa 28, 00-847

info@devico.io

Lviv

R&D

Uhorska str. 14, 79034

info@devico.io

Questions & answers

If you’ve got data, a clear business problem, and internal buy-in – you’re ready. A good machine learning development company can help assess the rest.

Machine learning helps reduce manual decision-making, predicts future trends, automates complex tasks, and unlocks patterns your team can’t see. It’s not magic – it’s ROI.

No. Even with limited data, a machine learning development company can build models that solve targeted, high-impact problems.

It depends on your goal, the kind of data you have, and how fast and accurate your results need to be. We don’t push buzzwords – we pick what fits your needs.

Yes. We specialize in transparent ML – models that don’t just work, but make sense to your stakeholders

Yes, we specialise in integrating ML solutions with existing systems to enhance their functionality and performance.

Access to your data, a clear objective, and someone on your side who understands the domain. We’ll guide you through the rest.

Yes, models aren’t set-and-forget. As your data and goals evolve, retraining keeps things sharp – and we handle that as part of our machine learning development services.

Yes. Most clients do. A pilot helps validate the value before committing to full-scale machine learning development services.

Yes. We work with all data types – text, audio, images, even messy logs. That’s where machine learning gets really powerful.

Pilots can show value in 4–8 weeks. Full deployments depend on scope, but our machine learning development company is known for fast iterations.

We’re used to working with compliance-heavy industries. Our machine learning development services are built around security and privacy best practices.