MLOps Consulting Services
We optimize your business’s machine learning operations for improved productivity and efficiency by automating ML pipelines and implementing AutoML platforms. Our MLOps expertise ensures improved planning and development, reproducibility in model training and deployment, scalability with hotkey access to necessary tools and resources, and continuity in the entire production flow leading to smooth machine learning operations.
Software Products Delivered
Total Years of Experience
Our MLOps Consulting Services
ML Pipeline Development
We specialize in developing automated ML pipelines designed to take input data and code and process it, enabling you to train machine learning models seamlessly. Our ML pipeline development services ensure that your data is processed accurately and your models are trained to the highest standards.
Model Deployment and Implementation
We have extensive experience in deploying machine-learning models on cloud-native infrastructure that are optimized for ML workloads, such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), ensuring their high availability, scalability and reliability.
Continuous Delivery for Machine Learning
Our CI/CD service enables your data science team to quickly test new ideas and iterate on models by automating pipeline components’ building, testing and deployment to the target environment. By streamlining the development process of your machine learning pipeline, we help you accelerate time-to-market and achieve business growth.
Our observability solutions, such as distributed tracing, log analysis and anomaly detection are designed to provide real-time insights into the performance of your AI systems, enabling you to optimize and fine-tune your models for better accuracy and efficiency.
Why Hire LeewayHertz for MLOps Consulting?
Fastrack Your Workflow
We streamline your infrastructure, workflows, and data preparation with automation and optimization to maintain productivity and efficiency throughout the ML lifecycle, enabling your team to stay productive.
By employing cutting-edge tools and technologies, including advanced algorithms and automation capabilities, our end-to-end development in MLOps service eliminates the need for extensive in-house expertise.
Flexible MLOps Toolkit
We leverage a platform that combines the best of both worlds, combining the power and flexibility of open-source tools with the convenience and reliability of commercial frameworks, along with our own hand-picked selection of favorite notebooks and libraries, providing you a seamless and integrated user experience.
Lower TCO for ML Projects
We understand that flexibility is key to building successful machine learning solutions. That’s why we offer a vendor-agnostic approach that allows you to run your operations in the cloud, on-premises or in a hybrid environment without ever feeling locked in.
By automating routine tasks and facilitating the smooth and efficient flow of experiments, we ensure you can make the most of your valuable time. We neatly store and organize your data sets and create high-performing models for you to achieve desired outcomes.
Security and Compliance
Our air-tight encryption protocols guarantee that your data is safeguarded not just while in use but also in motion and at rest in the cloud. With our rigorous security measures, you can rest assured that your data is in good hands.
Our MLOps Process
Aligning Machine Learning Objectives With Business Goals
- Understanding the business goals and objectives of the organization.
- Defining the problem statement that needs to be solved using machine learning.
- Identifying the data sources and the data required for the machine learning model.
- Developing a plan for the building, testing, deploying, and monitoring of the machine learning model.
Data Preparation and Management
- Developing a program for performing offline extraction or batch fetching from the desired data source.
- Implementing an automated data validation process to ensure data cleanliness and adherence to a predefined schema.
- Utilizing an auto-distribution mechanism to split the validated data into separate training and validation datasets.
- Establishing a feature store as a repository for storing and organizing pre-existing features.
- Choosing a lineup of storage-agnostic version control systems suitable for machine learning workflows.
- Integrating the chosen version control systems into the platform and configuring them appropriately.
- Verifying that metadata generated from new training runs are automatically committed to the appropriate version control system.
- Creating a metadata store to capture relevant information for further analysis.
- Establishing a framework for model monitoring and validation utilizing the selected toolkit.
- Enabling automated capturing of all essential performance data from each model run.
- Recording and storing all relevant details to facilitate easy reproducibility of results.
- Defining specific triggers for launching pre-training when the model performs below expectation.
- Determining the most suitable framework for wrapping the model as an API service.
- Alternatively, selecting and configuring a container service for deployment.
- Establishing a production-ready repository for models.
- Creating a model registry to store all relevant metadata associated with each model.
- Selecting the best-suited agent for real-time model monitoring.
- Configuring the agent to capture anomalies, detect concept drift and monitor model accuracy.
- Incorporating additional measures for estimating model resource consumption.
- Defining re-training triggers and configuring alerts accordingly.
Our MLOps Tech Stack
Our Artificial Intelligence Portfolio
Big Brands Trust Us
Our Engagement Models
Dedicated Development Team
Our blockchain developers are hands-on the cognitive technologies to deliver high-quality services and solutions to clients.
Our team extension model is intended to help clients who want to extend their team with the right expertise required for their project.
Our project-based model and software development specialists are there for customer collaboration and specific client project engagement.
Get Started Today
1. Contact Us
Fill out the contact form protected by NDA, book a calendar and schedule a Zoom Meeting with our experts.
2. Get a Consultation
Get on a call with our team to know the feasibility of your project idea.
3. Get a Cost Estimate
Based on the project requirements, we share a project proposal with budget and timeline estimates.
4. Project Kickoff
Once the project is signed, we bring together a team from a range of disciplines to kick start your project.
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.
What is MLOps?
MLOps is a set of practices and tools that aim to streamline machine learning model development, deployment and monitoring. It is important because it can help businesses to reduce the time and costs associated with building and deploying ML models, improve model performance and increase the reliability and scalability of ML systems.
Why should I opt for MLOps consulting?
- Faster Time-to-market for New Models: MLOps provides a framework that helps organizations streamline the development process for ML models, reducing development time and costs while improving the quality and reliability of the models. With most of the pre-development out of the way and effectively automated, development teams can fully focus on building viable ML models that provide value to the business.
Full Visibility and Reproducibility: MLOps provides full visibility and reproducibility throughout the development lifecycle, making it easier for teams to manage their machine learning models. With a version environment and tools for building, evaluating, and comparing models’ performance, teams can quickly identify what’s working and what’s not, enabling them to optimize their models and ensure that they provide maximum value to the business.
Lower Risk of Production Failure: MLOps provides a framework that enables development teams to lower the risk of production failure by bridging the communication gap between the research and production environments. With a model registry detailing all the model metadata, teams can ensure that models are thoroughly tested and validated before they are deployed to the production environment, minimizing the risk of issues and maximizing the value of the models for the business.
- Accelerate Experimentation Rate: MLOps accelerates the experimentation rate in machine learning development by streamlining the deployment process for viable models and allowing development teams to replicate models quickly. This increased experimentation rate leads to more innovative solutions, enabling development teams to focus on new projects and improve the accuracy and value of their machine-learning models.
- Reducing Time on Data Collection and Preparation: MLOps reduces the time spent on data collection and preparation by creating machine learning pipelines that design and manage reproducible model workflows. MLOps enables development teams to focus on developing more accurate and valuable machine learning models by automating many of the data collection and preparation tasks and delivering consistent model performance.
Scalability of ML Models: MLOps helps with the scalability of ML models by increasing the acceleration, automation, and quality of the ML development process. By automating many of the tasks involved in developing and deploying models, monitoring and managing models at scale, and improving the quality of ML models, MLOps makes it easier to scale the development and deployment of ML models across multiple environments and use cases.
What services do you offer related to MLOps?
We offer a range of MLOps consulting services, including data pipeline design and implementation, model training and deployment, monitoring and performance optimization and team training and development.
How can you help my business implement MLOps?
We can work with you to assess your current ML infrastructure and identify areas for improvement. Based on our assessment, we help you design and implement data pipelines, build and deploy ML models, establish monitoring and alerting systems and develop best practices for MLOps within your organization.
Do you offer customized solutions or pre-packaged MLOps packages?
We offer both customized solutions and pre-packaged MLOps packages depending on your business’s specific needs and requirements. Our team of experts works with you to tailor our services to your unique needs and ensure you get the most value out of them.
How can I get started with your MLOps consulting services?
To get started, fill out our contact form or reach out to our team directly. We will schedule a consultation to discuss your needs and develop a tailored plan to help you achieve your goals with MLOps.
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