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Machine Learning Development Services

Brew real, useful, and meaningful AI with our machine learning consulting services

At Azilen, we don’t just build AI; we craft it into something simple, useful, and meaningful. Because we believe it should not wow the users. Instead, it should simply solve the problems of end-users. Keeping this philosophy at the center of everything we do, our ML and DL expertise blends sophisticated ML/DL algorithms with real-world insights to deliver AI that’s not only smart but solves the core problems, with responsibility. We’re here to turn your toughest challenges into product success with AI that’s as dynamic as your goals and ever-changing market and user needs. Ready to see AI that makes a real difference while being as real and honest as it can be? Let’s make it happen with our finest machine learning development services.

Revisit the potential area of your software with our ML/DL expertise. Get demos of AI which we have already engineered.  

We help you solve
your pressing challenges.

ML DL Platform Engineering Services

Our Complete Suite of Machine Learning Development Services

Let AI think, ‘Wow! I am being used responsibility for solving real problems!’
Model Training and Hyperparameter Tuning

We leverage sophisticated algorithms and techniques to rigorously train your models, adjusting hyperparameters to maximize performance and accuracy. By employing cross-validation and advanced optimization strategies, we ensure that your models are fine-tuned for peak efficiency and robustness in real-world applications.

  • Data Preparation
  • Feature Engineering
  • Hyperparameter Optimization
  • Cross-validation
Model Evaluation and Validation

We rigorously assess model performance using a variety of metrics and validation techniques to ensure accuracy, generalization, and reliability. Through cross-validation, robustness checks, and performance benchmarking, we validate that your models meet the highest standards and are ready for deployment in real-world scenarios.

  • Test Set Evaluation
  • Confusion Matrix Analysis
  • Bias and Fairness Assessment
  • Sensitivity Analysis
Deployment and Serving

We manage the full lifecycle of model deployment, including containerization, orchestration, and integration with your existing infrastructure. Our solutions ensure high availability, auto-scaling, and efficient model serving to handle real-time data and requests. We also implement monitoring and logging to track performance and detect issues promptly.

  • Model Containerization
  • API Integration
  • Scalability Solutions
  • Real-time Serving
Monitoring and Maintenance

We provide continuous oversight to track model performance, detect anomalies, and ensure stability. Our proactive approach includes routine updates, performance tuning, and issue resolution to adapt to evolving data and requirements. With robust monitoring tools and maintenance strategies, we ensure your AI remains effective, reliable, and aligned with your product goals.

  • Performance Monitoring
  • Drift Detection
  • Model Retraining
  • Error Logging and Analysis

Confused about the resources, timeline & cost for your next ML or DL project? Get tailored estimation within 36 hours.

What’s Trending Now!

The world of AI is changing faster than you can ever imagine. What makes it even more thrilling for product owners is – not all changes are relevant to their products!

  • Explainable AI (XAI) is gaining prominence for transparency and trust. 
  • Enhanced privacy and data security with decentralized model training. 
  • Growing use of ML/DL for real-time processing on edge devices. 
  • Emphasis on reducing the carbon footprint of AI models. 
What's Trending Now!
ML DL Platform Engineering Support

Machine Learning Development Services. Support: From Strategy to Success & Beyond

Our commitment extends beyond the deployment of your ML and DL solutions. We provide comprehensive post-implementation support to ensure your models and systems continue to perform at their best.
  • Provide hands-on support for diagnosing and resolving problems.      
  • Deliver timely fixes and patches for any bugs or vulnerabilities.  
  • Offer training sessions and comprehensive documentation to help your in-house team.  
  • Implement rapid response protocols for addressing critical failures.  

Explore the Possibilities with Machine Learning Development and Consulting Services.

Technologies: The Engine Room

We constantly dig deeper into new technologies and push the boundaries of old technologies, with just one goal - client value realization.
Technologies

Why Azilen is the right choice

We deliver nothing less than excellence.
Achiever
Adaptable
Agile
Ambitious
Analytical
Attentive

Case Studies: Real Transformations, Real Results

Explore how we've turned client challenges into measurable results.

The Spirit Behind Engineering Excellence

We instantly fall in love with your challenges and steal it from you!
  • 400+
    Product Engineers
  • 15+
    Years of Experience
  • 100+
    Delivered Lifecycles
  • 10M+
    Lives Touched
  • 3M+
    Man Hours Invested
  • 10+
    Wellness Programs

Unfiltered Customer Reviews

The essence (in case you don't read it all): We nail it, every time!

Product Engineering is in Our DNA.

And we are not a development outsourcing company!
THE AZILEN Promise
THE AZILEN PromiseUpheld
Product Lifecycle Management
Strategic Innovation and R&D
Cross-Disciplinary Expertise
Product Ownership and Vision
Scalable Architecture Design
Agile and Iterative Development
Long-Term Strategic Partnerships

Frequently Asked Questions (FAQ's)

Get your most common questions around our machine learning development services answered.

Typical components include model development and training environments, scalable deployment infrastructure, data management and preprocessing tools, model monitoring and maintenance systems, and integration frameworks. These components work together to support the entire lifecycle of machine learning and deep learning models.

Machine Learning (ML) refers to algorithms that learn from data to make predictions or decisions, while Deep Learning (DL) is a subset of ML that uses neural networks with many layers to analyze complex patterns in large datasets. Platform engineering for DL often involves more advanced infrastructure due to the computational demands and complexity of DL models compared to traditional ML.

Data management is crucial for ML and DL platform engineering as it involves the collection, storage, preprocessing, and handling of large datasets. Effective data management ensures data quality, accessibility, and integrity, which are essential for training accurate and reliable models.

Model monitoring involves tracking the performance of deployed models to ensure they continue to deliver accurate predictions. Maintenance includes updating models with new data, retraining them as needed, and addressing any performance issues. Monitoring tools provide insights into model behavior, data drift, and operational performance.

ML and DL platforms can be integrated with existing software products through APIs, microservices, or direct embedding of models into the application. Integration involves connecting the platform’s output to the product’s features and ensuring seamless data flow between the platform and the software product.

Key challenges include managing large datasets, ensuring model performance and accuracy, handling computational requirements, and maintaining security and compliance. These challenges are addressed through robust infrastructure, efficient data processing techniques, regular model evaluations, and adherence to best practices in security and compliance.

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