MLOps Consulting Services

Optimize Your Machine Learning Operations

At PronexisIT, we enhance your business’s machine learning operations to improve productivity and efficiency by automating ML pipelines and implementing AutoML platforms. Our MLOps expertise ensures effective planning and development, reproducibility in model training and deployment, scalability with quick access to essential tools and resources, and continuity throughout the production flow, leading to seamless machine learning operations.

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Our MLOps Consulting Services

ML Pipeline Development

We specialize in developing automated ML pipelines designed to process input data and code, enabling you to train machine learning models seamlessly. Our pipeline development services ensure accurate data processing and adherence to the highest standards in model training

Model Deployment and Implementation

With extensive experience in deploying machine learning models on cloud-native infrastructures optimized for ML workloads—such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP)—we ensure your models are highly available, scalable, and reliable

Continuous Delivery for Machine Learning

Our Continuous Integration/Continuous Delivery (CI/CD) services enable your data science team to swiftly test new ideas and iterate on models by automating the building, testing, and deployment of pipeline components to the target environment. This streamlining accelerates your machine learning pipeline's development process, helping you achieve business growth.

Model Monitoring

Our observability solutions, including distributed tracing, log analysis, and anomaly detection, provide real-time insights into the performance of your AI systems, enabling you to optimize and fine-tune your models for enhanced accuracy and efficiency

Data Engineering and Management Services

We offer expert guidance on robust data handling practices to ensure dataset integrity and usability for ML models. Our services include advising on data acquisition strategies, implementing efficient data cleaning techniques, and establishing reliable data processing workflows. Our goal is to prepare high-quality datasets that enhance ML effectiveness and minimize errors.

Model Governance and Compliance

We bolster the security and integrity of your ML applications through robust governance strategies, prioritizing data confidentiality, ethics, and strict regulatory compliance. Our comprehensive approach includes oversight mechanisms designed to ensure model fairness, detect biases, and rigorously evaluate performance.

Our MLOps Implementation Process

Aligning Machine Learning Objectives With Business Goals

○ Understand the business goals and objectives of the organization.
○ Define the problem statement to be solved using machine learning.
○ Identify data sources and requirements for the machine learning model.
○ Develop a plan for building, testing, deploying, and monitoring the machine learning model.

Data Preparation and Management

○ Create a program for offline extraction or batch fetching from the desired data source.
○ Implement an automated data validation process to ensure cleanliness and adherence to a predefined schema.
○ Utilize an auto-distribution mechanism to split validated data into separate training and validation datasets.
○ Establish a feature store as a repository for organizing pre-existing features.

Model Training

○ Choose appropriate storage-agnostic version control systems for machine learning workflows.
○ Integrate and configure the selected version control systems within the platform.
○ Ensure metadata generated from new training runs is automatically committed to the appropriate version control system.
○ Create a metadata store to capture relevant information for further analysis.

Model Evaluation

○ Establish a framework for model monitoring and validation using the selected toolkit.
○ Enable automated capturing of essential performance data from each model run.
○ Record and store all relevant details to facilitate easy reproducibility of results.
○ Define triggers for launching pre-training when the model performs below expectations.

Model Serving

○ Determine the most suitable framework for wrapping the model as an API service or select a container service for deployment.
○ Establish a production-ready repository for models.
○ Create a model registry to store all relevant metadata associated with each model.

Model Monitoring

○ Select the most appropriate agent for real-time model monitoring.
○ Configure the agent to capture anomalies, detect concept drift, and monitor model accuracy.
○ Incorporate additional measures for estimating model resource consumption.
○ Define re-training triggers and configure alerts accordingly.

Our Engagement Models

Dedicated Development Team

Our developers leverage cutting-edge cognitive technologies to deliver high-quality services and tailored solutions to our clients.

Team Extension

Our team extension model is designed to assist clients seeking to expand their teams with the precise expertise needed for their projects.

Project-based Model

Our project-oriented approach, supported by our team of software development specialists, is dedicated to fostering client collaboration and achieving specific project objectives.

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.