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Machine Learning Development

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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

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

Our process. Simple, seamless, streamlined.

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:

  1. Spam detection tools
  2. Recommendation engines
  3. Virtual assistants
  4. Chatbots
  5. Natural language processing (NLP) software such as speech recognition tools
  6. 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.