ML Model Engineering Services
With deep expertise in machine learning frameworks and libraries, NLP, deep learning, data visualization/processing tools, and DevOps, we build powerful machine learning models that can drive transformative business outcomes. Whether you want to build a custom ML model from scratch, optimize an existing one, or deploy it in the cloud, we have you covered. Our custom ML models, including generative AI models, multimodal models, transformer models, and recommender systems, are optimized for business-specific use cases.
Trusted By Leading Enterprises
AI Developers
Software Products Delivered
AI Solutions
Total Years of Experience
Our ML Model Engineering Services
Custom Model Development
Our ML model engineering services specialize in custom model development, covering all aspects of the ML lifecycle, such as data collection, data preparation, model training, model testing, deployment, and monitoring. Following the ML lifecycle, we can ensure the model is developed to meet the client’s specific needs and perform as expected.
Model Optimization
Our model optimization services are designed to help you get the most out of your existing machine-learning models. We use advanced techniques such as transfer learning, ensembling, and pruning to fine-tune your models, improving their accuracy and performance to help you achieve desired results.
Model Integration
We excel at deploying machine learning models into production environments, ensuring optimal performance and seamless integration. Whether it’s in the cloud or on-premises, we fine-tune your models to deliver exceptional results. We aim to provide efficient and reliable model integration services, enabling you to maximize the value of your models for your business.
Model Maintenance and Monitoring
We offer comprehensive machine learning model maintenance and monitoring services, ensuring your models are continuously optimized for performance and efficiency. Our team closely monitors the performance and accuracy of the models and re-trains them as needed to maintain optimal performance.
Our ML Model Engineering Expertise
Machine Learning
Our ML engineers demonstrate a deep understanding of statistical analysis and probability theory, along with expertise in Python, R, and Java programming languages, which enables them to develop reliable ML models and evaluate their performance accurately.
Model Fine-tuning
Our ML engineers have expertise in monitoring and maintaining models for optimal performance and accuracy. They fine-tune models as needed, using various techniques such as hyperparameter tuning, transfer learning, ensembling and pruning.
Data Engineering
Our data engineers leverage their knowledge of data processing frameworks, such as Apache Spark and Hadoop and perform tasks such as data cleaning, feature extraction and data visualization to preprocess and analyze the data effectively.
Expertise Across Industries
Our team of ML engineers with experience working across industries such as healthcare, finance and e-commerce, leverage their domain expertise and knowledge to develop custom ML models that meet the unique needs of each business, including regulatory requirements.
Cloud Computing
Our developers’ experience deploying and scaling cutting-edge AI solutions has given them in-depth knowledge of all major cloud computing platforms, including AWS, Google Cloud and Microsoft Azure.
Software Engineering
With a deep knowledge of software engineering principles and practices, including version control, testing, and deployment, our ML engineers ensure that ML models are developed in a controlled environment and tested thoroughly, ensuring the final product meets the highest quality standards.
Why Hire LeewayHertz for ML Model Engineering Services?
Technical Skills
With a strong track record in ML model engineering, involving developing and deploying models in diverse industries, our engineers have the required technical skills, which include expertise in programming languages, machine learning algorithms, data preprocessing and cloud computing.
Data Security
We have robust data security protocols in place to protect your sensitive data throughout the model engineering process. We maintain stringent data security measures such as access control, authorization and authentication that ensure your data is encrypted, backed up and secured, preventing unauthorized access.
End-to-End Solution Development
Our full-cycle solution development services include ideation, development, testing, deployment, and maintenance. We work closely with clients to understand their requirements and business objectives and offer upgrade services to ensure the solution can keep pace with the rapidly evolving technology space to ensure it’s future-ready.
Infrastructure
Our focus on infrastructure as a key component of ML model engineering has enabled us to build a world-class hardware and software infrastructure that features high-performance computing, distributed computing, and large-scale data processing capabilities, making it possible to support even the most complex and demanding ML projects.
AI Models We Have Expertise In
Our ML Model Engineering Tech Stack
Data Storage
SQL databases
NoSQL databases
Data lakes
Amazon S3Data Processing and Preparation
Pandas
NumPy
SciPyML Libraries and Frameworks
Tensorflow
PyTorch
Scikit-learn
kerasDevOps Tools
Git
Jenkins
DockerCloud Infrastructure
AWS
GCP
Microsoft AzureVisualization Tools
Tableau
Matplotlib
PlotlyMonitoring and Tracking Tools
TensorBoard
MLflow
NeptuneOur Artificial Intelligence Portfolio
AI-powered App
LLM-powered Application for Safer Machinery Troubleshooting
LeewayHertz collaborated with a top-tier Fortune 500 manufacturing company to develop an innovative LLM-powered machinery troubleshooting application. This innovative solution streamlines machinery maintenance, elevates safety protocol adherence and mitigates operational risks of the firm. By seamlessly integrating static machinery data and dynamic safety policies, the application provides quick access to relevant information for troubleshooting issues while also enhancing safety with clear and detailed instructions on equipment handling.
Generative AI Application
LLM-powered App for Compliance and Security Access
LeewayHertz has partnered with Scrut to engineer an LLM-powered app designed to streamline access to compliance benchmarks, frameworks, and audit-relevant data for Scrut's clientele. Drawing on Scrut's proprietary data, our team utilized advanced embedding and prompt engineering techniques to seamlessly incorporate an LLM, yielding rapid query responses and upgraded user experiences. This initiative has equipped Scrut's clients with a robust tool, fostering informed decision-making and providing clear insight into industry benchmarks as well as Scrut’s risk monitoring and mitigation strategies and services.
Data Analysis
Geospatial Data Analysis
A US-based geospatial intelligence and analytics firm sought LeewayHertz’s expertise to tackle a complex dataset with limited identifiers, aiming to derive valuable insights, identify patterns, spot unusual movements, and ensure data security. Our solution involved deploying a sophisticated data analysis pipeline, encompassing timestamp conversion, geocoding for contextual enrichment, clustering for pattern recognition, correlation analysis, and anomaly detection. Beyond unveiling intricate location-based patterns, our approach prioritized robust data security, resulting in the delivery of actionable intelligence that precisely met our client's objectives.
Generative AI Solution
GenAI-based Clinical Decision Support System
A GenAI-based clinical decision support system tailored for an esteemed healthcare organization that analyzes diverse patient data using advanced algorithms and NLP to provide healthcare professionals with swift and accurate disease diagnoses. The platform’s user-friendly interface optimizes the diagnostic workflow, offering evidence-driven insights and comprehensive reports that include discussions, testing guidance, therapy recommendations, specialist referrals, and patient education. This solution ultimately elevates patient care through the seamless integration of generative AI, fostering efficiency, precision, and innovation in healthcare delivery.
Big Brands Trust Us
Our Engagement Models
Start a conversation by filling the form
Once you let us know your requirement, our technical expert will schedule a call and discuss your idea in detail post sign of an NDA.
All information will be kept confidential.
All information will be kept confidential.
FAQs
What is ML model engineering?
Machine learning (ML) model engineering is a technical process that involves various steps, including data collection and preprocessing, model selection, training, deployment and monitoring. It aims to develop effective and efficient ML models to solve specific problems and meet diverse business use cases. This process requires a range of technical skills, including expertise in programming languages, data structures, algorithms and ML frameworks.
What industries do you work with?
We work with various industries, including finance, healthcare, retail, and manufacturing. Our team has experience in developing ML models for a variety of use cases, including fraud detection, predictive maintenance, and NLP-based tools.
What are the benefits of hiring professional ML model engineering services?
Hiring a reputed company for ML model engineering services can offer several benefits, such as access to a team of experienced data scientists and engineers, a faster time to market for your ML models and access to the latest tools and technologies in the field.
How do you ensure the security of my data?
We take data security very seriously and have robust protocols in place to protect your sensitive data throughout the ML model engineering process. This includes measures such as data encryption, access controls, and regular security audits.
What is your approach to ML model development?
Our approach to ML model development is based on industry best practices, including iterative development, continuous testing and evaluation, and close collaboration with the client to ensure that the model we develop meets their specific business needs.
How long does it take to develop an ML model?
The timeline for developing an ML model can vary depending on the project’s complexity and data availability. However, we typically follow an agile development process and provide regular updates to our clients throughout the development cycle.
Insights
AI in accounting and auditing: Blazing new trails in financial management
AI has made significant inroads into the fields of accounting and auditing, redefining how financial data is processed, analyzed, and audited.
AI in payment: Key applications, advantages and regulatory considerations
The integration of AI within payment systems has transformed transactions, offering enhanced security, efficiency, and customer satisfaction.
AI agents for insurance: Key components, applications and use cases, benefits, implementation and future trends
AI agents are transforming the insurance industry by automating complex processes, improving decision-making, and enhancing customer experiences.
0.
