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ML Development Services We Offer

By leveraging the expertise of our ML engineers in working with emerging technologies, we offer powerful software development solutions to help you make enhanced decision-making with comprehensive data-driven insights.

ML consulting and strategy building

ML consulting and strategy building

YTII can help you gain an advantage with innovative ML solutions, attractive user experiences, and streamlined processes. We'll assess your current technical setup, find ML integration opportunities, and create custom strategies to implement ML solutions successfully.

MLOps consulting

MLOps consulting

We facilitate organizations' adoption of best practices of MLOps and streamline workflows to ensure reliable and quick development of ML models. We help businesses choose and configure the tools for testing, version control, monitoring, and deployment while reducing the model transition time from development to production.

Custom ML model development

Custom ML model development

With our ML development expertise, we develop domain-specific ML models by fine-tuning the pre-trained models to meet unique business requirements. Whether it is Llama 2, GPT, or BERY, our development experts ensure that all the models deliver context-aware responses with high precision.

Integration into existing workflows

Integration into existing workflows

Planning to integrate ML solutions into your existing workflows? Hire ML engineers with us to ensure that your ML solutions seamlessly integrate into your existing workflows without any data loss. We also help businesses identify the critical progressive areas where machine learning can deliver the most value.

Marketing automation solutions

Marketing automation solutions

We integrate machine learning with CRM apps and marketing automation to offer personalized content suggestions, run precise marketing campaigns, and improve market segmentation. Hire top-notch ML development experts to drive innovation and automation in your business.

ML-powered solution development

ML-powered solution development

Our team of machine learning engineers designs personalized ML solutions to meet your specific needs. We specialize in creating and implementing predictive models, recommendation systems, and other data-driven applications. These applications improve operations, enhance user experiences, and support data-driven decision-making.

Turn your raw data into intelligent business solutions with ML development services

Partner with YTII to acquire end-to-end machine learning model development and enhance your decision-making today with data-driven insights.

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Why Choose Our ML Development Company?

Being a pioneer AI development company, Your Team In India has successfully delivered excellence in cross-platform applications to accelerate business growth and drive more success.
By following the global industry practices and agile methodology, we have successfully delivered innovative web and mobile app solutions for over 1000+ projects and still counting.
While ensuring strict NDA compliance for our client's privacy, we implement the best security practices to secure your information and application safe from any security breaches. Acquire the industry's best ML development services with us.

Hire the ML Development Team from YTII

Simple & Transparent Pricing | Fully Signed NDA | Code Security | Easy Exit Policy

$20 Hourly (USD)

We’ll provide a fullysigned NDA for your Project’s confidentiality

Get a QuoteFor Fixed Cost Solution

Ensure Timely Delivery
Within Budget

$2800 Monthly (USD)

4+ Years of Exp. Senior Angular Developer 160 hours

Technical Expertise of our Dedicated ML Developers

Languages
Python | R | C++ | JavaScript | Kotlin | Java | Golang | Scala
Frameworks
Tensorflow | Mahout | Caffe | Mxnet | Rasa | Flask | Pytorch
Libraries
Pandas | SpaCy | Scikit-learn | NumPy | Matplotlib | OpenCV | NLTK | Asyncio | SciPy | Ggplot2 | Dash Plotly | Streamlit | Gradio | Spark MLlib | Theano | Keras | Gensim | Seaborn
Algorithms
Regression | Ridge & Lasso Regression | Support Vector Machines | Stochastic Gradient Descent | Ada-Boost | XGBoost | FastLinear Regression | Logistic Regression | Decision Tree | SVM | Naive Bayes | kNN | K-Means | Random Forest
Network Architecture
ResNet | RNN | CNN | Regression models | Categorization models | GAN | NeRF | Clustering algorithms | YoloNet | AlphaPose | Skeleton detection | Pose2Seg | RetinaFace | U-Net | DBSCAN
Data Set
ImageNet | MSCOCO | MNIST | CIFAR-10 | Pascal VOC | Kaggle | Earth Data
OCR
Pytesseract | Amazon Extracts | Google Vision
Communication Tools
AWS | Azure | Google Cloud
Platforms
Google Meet | Zoom | GoToMeeting
Tools and Technologies
Spark | Hadoop | Apache Kafka | IBM Watson | MATLAB | Jax | SAS | IBM SPSS | MySQL | Google Big Query | Power BI | Tableau
PM Tools
Jira | Trello | Slack | Asana | Azure | DevOps | Hubstaff Tasks
Communication Tools
Slack | Hangout | Microsoft Teams

Hiring Models for ML Development Services

We offer top-notch ML development services that deliver an aesthetically pleasing and personalized user experience. Choose the best hiring model that best matches your unique requirements.

Angular team augmentation

ML Team Augmentation

Work with experienced machine learning developers to help your team grow and meet your business scaling needs.

Level up your team

Dedicated angular team

ML Development Outsourcing

Trust skilled developers aligned with your business goals to handle your machine learning development needs and drive your project forward.

Outsource ML development

Angular development outsourcing

Dedicated ML Development Team

Get reliable and customizable machine learning solutions for your project from our team of dedicated ML engineers.

Hire a dedicated ML development team

Drive your business growth with custom machine-learning solutions

Automate your data analysis process and generate actionable insights with YTII ML Engineers.

Our Success Stories

We assist both new and emerging startups in developing MVPs, forming teams, and gaining access to cutting-edge technologies. From application creation to modernization and maintenance, our 360° enterprise solutions cover it all.

Our Customers Say

We have been fortunate enough to build some long-lasting business relationships by delivering excellent services. Here’s what they say about us!

User Guide to ML Development

Machine learning is categorized into three main types, each addressing different challenges and serving distinct purposes. Understanding these types is crucial for choosing the most suitable approach for specific tasks.

1. Unsupervised learning

The unsupervised learning category deals specifically with unlabeled data. In this type of learning, the model learns patterns and relations within the data without any explicit guidance. Unsupervised learning algorithms explore data structures to determine groupings and inherent patterns. This type of learning is commonly used for anomaly detection and clustering.

  • Anomaly detection— It identifies abnormal or rare instances in data. It is mainly used to monitor unusual events, detect faults, and detect fraud.
  • Clustering—The similarity of this group's data points depends on their similarity. It's useful in data exploration, pattern recognition, and customer segmentation.

2. Supervised learning

Supervise the learning process, including data labelling, to train the ML models. The ML models learn from input-output pairs to make accurate predictions on unseen data. Regression and classification are common applications of this learning.

  • Regression— It predicts a consistent output depending on the input data. For instance, the prediction of house prices depends upon features like the number of rooms, location, and size.
  • Classification—This model assigns the input data to predefined classes or categories, such as spam or primary emails.

3. Reinforcement learning

Reinforcement learning involves the agent learning through interaction with the environment and receiving feedback in the form of penalties or rewards. It is commonly applied in situations where actions have an impact on subsequent data.

Data, as the backbone of machine learning, plays a crucial role in the success of your models. The process of data preparation, which involves cleaning, preprocessing, and transforming data, is a key step in making your data suitable for model training. 

Selecting ML frameworks and tools

PyTorch, Scikit-learn, and TensorFlow are popular tools and frameworks, each with its strengths and use cases. The right choice depends on project scalability, complexity, and community support.

  • PyTorch is recognized for its dynamic neural network architecture and flexibility, making it ideal for research-driven applications. 
  • Scikit-learn is a robust solution that addresses general machine-learning tasks and ensures easy implementation and deployment. 
  • TensorFlow has considered ideal for scalable deep learning solutions well-suited for complex projects.

Feature selection involves identifying the most important input features that best predict outcomes in machine learning models. This process is crucial because:

  • Using fewer features reduces overfitting by making the model less complex and less likely to fit the noise in the training data.
  • By removing irrelevant or redundant features, it can improve model accuracy.
  • It reduces training time as the training process uses fewer computational resources and less time by working with fewer features.
  • Having fewer features makes a model easier to understand and explain, improving its interpretability.

Machine learning is incredibly valuable for companies in addressing their challenges. It presents lucrative career opportunities and facilitates data-driven decision-making. Below are some of the advantages of opting for ML development for businesses

Handle Large Data Easily

Machine learning can easily organize large amounts of data and perform this task in any challenging environment.

Wider Approach

You can target a large group of customers and provide a more personalized user experience while focusing on existing customers.

Works on the latest trends and design

Machine Learning Development offers many data services. It helps identify new trends and patterns that are hard for people to see. For example, on an e-commerce website like Amazon, it helps understand what users buy, how they use apps, and what they search for. This makes it easier to give users the right information about their purchases, products, and deals.

Human action is not required

Machine Learning doesn’t require constant monitoring. It comes with built-in algorithms that predict and enhance future data on their own. Machine learning is particularly good at spotting spam in the data.

Continuous advancement

As machine learning processes advance, they continually improve in accuracy and performance. With increased experience, they make more accurate judgments.

RPA can reduce human capital costs, prevent human errors, save money, and free up employees' time for value-added tasks like business development. When talking about why businesses require RPA, the following could be the strong reasons to answer that.

  • RPA automates significant business processes.
  • RPA never takes breaks or gets tired, so it can improve work efficiency by up to 50%.
  • These RPA bots are trained to perform specific tasks without failures or errors, enabling them to execute step-by-step processes with higher accuracy.
  • RPA solutions are more secure, cost-effective, and aligned with business objectives, saving you time and money.
  • With RPA deployment, businesses can generate higher ROI by speeding up their processes and operations. 
  • RPA implementation enhances the employee's performance by executing repetitive tasks.

Machine learning is categorized into three main types, each addressing different challenges and serving distinct purposes. Understanding these types is crucial for choosing the most suitable approach for specific tasks.

1. Unsupervised learning

The unsupervised learning category deals specifically with unlabeled data. In this type of learning, the model learns patterns and relations within the data without any explicit guidance. Unsupervised learning algorithms explore data structures to determine groupings and inherent patterns. This type of learning is commonly used for anomaly detection and clustering.

  • Anomaly detection— It identifies abnormal or rare instances in data. It is mainly used to monitor unusual events, detect faults, and detect fraud.
  • Clustering—The similarity of this group's data points depends on their similarity. It's useful in data exploration, pattern recognition, and customer segmentation.

2. Supervised learning

Supervise the learning process, including data labelling, to train the ML models. The ML models learn from input-output pairs to make accurate predictions on unseen data. Regression and classification are common applications of this learning.

  • Regression— It predicts a consistent output depending on the input data. For instance, the prediction of house prices depends upon features like the number of rooms, location, and size.
  • Classification—This model assigns the input data to predefined classes or categories, such as spam or primary emails.

3. Reinforcement learning

Reinforcement learning involves the agent learning through interaction with the environment and receiving feedback in the form of penalties or rewards. It is commonly applied in situations where actions have an impact on subsequent data.

Data, as the backbone of machine learning, plays a crucial role in the success of your models. The process of data preparation, which involves cleaning, preprocessing, and transforming data, is a key step in making your data suitable for model training. 

Selecting ML frameworks and tools

PyTorch, Scikit-learn, and TensorFlow are popular tools and frameworks, each with its strengths and use cases. The right choice depends on project scalability, complexity, and community support.

  • PyTorch is recognized for its dynamic neural network architecture and flexibility, making it ideal for research-driven applications. 
  • Scikit-learn is a robust solution that addresses general machine-learning tasks and ensures easy implementation and deployment. 
  • TensorFlow has considered ideal for scalable deep learning solutions well-suited for complex projects.

Feature selection involves identifying the most important input features that best predict outcomes in machine learning models. This process is crucial because:

  • Using fewer features reduces overfitting by making the model less complex and less likely to fit the noise in the training data.
  • By removing irrelevant or redundant features, it can improve model accuracy.
  • It reduces training time as the training process uses fewer computational resources and less time by working with fewer features.
  • Having fewer features makes a model easier to understand and explain, improving its interpretability.

Machine learning is incredibly valuable for companies in addressing their challenges. It presents lucrative career opportunities and facilitates data-driven decision-making. Below are some of the advantages of opting for ML development for businesses

Handle Large Data Easily

Machine learning can easily organize large amounts of data and perform this task in any challenging environment.

Wider Approach

You can target a large group of customers and provide a more personalized user experience while focusing on existing customers.

Works on the latest trends and design

Machine Learning Development offers many data services. It helps identify new trends and patterns that are hard for people to see. For example, on an e-commerce website like Amazon, it helps understand what users buy, how they use apps, and what they search for. This makes it easier to give users the right information about their purchases, products, and deals.

Human action is not required

Machine Learning doesn’t require constant monitoring. It comes with built-in algorithms that predict and enhance future data on their own. Machine learning is particularly good at spotting spam in the data.

Continuous advancement

As machine learning processes advance, they continually improve in accuracy and performance. With increased experience, they make more accurate judgments.

RPA can reduce human capital costs, prevent human errors, save money, and free up employees' time for value-added tasks like business development. When talking about why businesses require RPA, the following could be the strong reasons to answer that.

  • RPA automates significant business processes.
  • RPA never takes breaks or gets tired, so it can improve work efficiency by up to 50%.
  • These RPA bots are trained to perform specific tasks without failures or errors, enabling them to execute step-by-step processes with higher accuracy.
  • RPA solutions are more secure, cost-effective, and aligned with business objectives, saving you time and money.
  • With RPA deployment, businesses can generate higher ROI by speeding up their processes and operations. 
  • RPA implementation enhances the employee's performance by executing repetitive tasks.

Our Blog

Read on the topics that matter most to you through our comprehensive research articles & informative blogs.

12 Attributes of a Good RPA Developer You Should Not Miss!

12 Attributes of a Good RPA Developer You Should Not Miss!

Read Full Article
How to Identify Process Automation Opportunities for Business

How to Identify Process Automation Opportunities for Business

Read Full Article
RPA Metrics: A Guide to Measure ROI of Your Process Automation

RPA Metrics: A Guide to Measure ROI of Your Process Automation

Read Full Article

Frequently Asked Questions

FAQ Icon

YTII has a team of ML development experts who are experts in creating tailored machine-learning solutions. Our ML engineers work closely with businesses to understand their unique requirements and implement them in innovative ML solutions to drive innovation and efficiency within organizations. We help companies drive efficiency and acquire innovative ML solutions using analytics tools for forecasting, recommendation systems, natural language processing, and more tailored to business requirements.

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ML development services can typically be easily harnessed by any industry looking to embed ML capabilities into its businesses. However, our services cater to an extensive range of industries, including retail, manufacturing, finance, healthcare, and more.

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Our ML development experts create a project's MVP in 60 days. However, estimating the development time of a complete ML development project can be complicated as it depends upon various factors, including the complexity of the project and the features to be incorporated. Therefore, the timeline depends upon the unique project requirements.

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Absolutely, our ML experts excel at seamlessly integrating the ML solution into your existing systems. Our ML engineers analyze your existing solution and infrastructure, recognize suitable ML models, and create microservices or APIs to perform smooth integrations while ensuring minimal disruptions to business operations.

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We use the best Machine Learning tools, such as TensorFlow, PyTorch, scikit-learn, and Keras. Our team stays abreast of the latest developments in ML to provide advanced solutions.

FAQ Icon Keeping your data safe and private is our top priority. We use strong security measures, such as data encryption and access controls, to meet industry standards. We also sign Non-Disclosure Agreements (NDAs) to protect sensitive information.

FAQ Icon We provide thorough support and maintenance after we launch a product. Our team monitors the product's performance, fixes any problems that arise, and makes updates or improvements as needed to ensure its good performance.

FAQ Icon Hiring a machine learning developer costs $25 an hour. However, the price of machine learning development services varies depending on factors such as the project’s complexity, scope of work, and specific requirements. To get an accurate cost estimate for your machine learning project, connect with our ML experts with your project details.

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