What are MLOps consulting services?

MLOps consulting services help organizations design, implement, and optimize machine learning operations across the full lifecycle of ML models. This includes infrastructure setup, CI/CD pipelines, model deployment, monitoring, governance, and automation. The goal is to move machine learning models from experimentation to stable production environments while ensuring scalability, reproducibility, and cost control.

What is MLOps service?

An MLOps service focuses on operationalizing machine learning models. It combines data science, DevOps, and data engineering practices to automate workflows, manage model versioning, and ensure reliable model deployment. MLOps services support continuous integration, testing, monitoring, and retraining to keep ML models accurate and production-ready. MLOps enables a small team to manage hundreds or thousands of models simultaneously without a linear increase in operational overhead.

How does MLOps consulting improve machine learning operations?

MLOps consulting improves machine learning operations by introducing automation, standardized pipelines, and governance controls. It reduces manual retraining, prevents deployment bottlenecks, and enables continuous monitoring of model performance. Structured MLOps processes also improve collaboration between data science and engineering teams while lowering operational risks. Automated model training and tuning are necessary to optimize performance and ensure models are updated with fresh data.

When does a company need MLOps services?

A company needs MLOps services when ML models move beyond experimentation and require stable deployment in production environments. Warning signs include manual workflows, inconsistent model performance, slow model deployment, infrastructure scaling issues, and lack of monitoring. MLOps services help scale machine learning systems while maintaining reliability and compliance.

Is CI/CD part of MLOps?

Yes. CI/CD is a core part of machine learning operations. In MLOps, CI/CD pipelines automate code integration, testing, validation, and model deployment. Automated retraining strategies are necessary to detect and mitigate model drift, ensuring consistent and reliable predictions over time. This ensures that machine learning models can be updated, retrained, and released efficiently without disrupting production systems. Continuous integration and delivery reduce errors and accelerate time-to-market.

MLOps Consulting and Development Services

Develop, deploy, scale, and maintain ML projects with unprecedented speed and efficiency. Ensure smooth and round-the-clock ML model operations with our MLOps services.

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SPD Technology
MLOps Consulting and Development Services

Looking to Streamline Your Machine Learning Operations?

Recognizing that no business can rely on unstable machine learning models, we at SPD Technology are committed to ensuring effective management and uninterrupted ML model operations. We combine best practices in machine learning, DevOps, and data engineering to address the complexities of ML models, including version control, resource management, scalability, and monitoring. As a result, our customers benefit from robust and secure infrastructure, automated workflows, and operational cost savings.

SPD Technology provides tailored MLOps solutions leveraging cutting-edge tools to address the unique needs of the business. Our MLOps consulting services help companies address real-world problems by providing innovative solutions that reduce errors and accelerate the deployment of new AI solutions. Increased ROI results from moving more models from pilot to production and capitalizing on AI investments.

Across the projects, our team leveraged and fine-tuned multiple ML models. We delivered 3x faster business processes and 5x cost savings across healthcare, document processing, transportation, construction, and eCommerce. Within these projects, we unlocked opportunities such as complete process automation, elimination of human error, and increased capacity to handle more business operations in a short period. Going beyond these advantages, our MLOps expertise also ensured:

Faster deployment
New market-leading functionalities
Enhanced staff productivity
Driven user engagement
Easily scaled infrastructure
New revenue flows

End-to-End MLOps Consulting Services

From designing a detailed roadmap to developing custom solutions, we help you accelerate time-to-market and streamline ML operations. MLOps can reduce deployment cycles from months to days or even weeks by automating workflows rather than relying on manual processes.

MLOps Consulting and Strategy
We elaborate a unique strategy for your business by examining your existing MLOps stack. MLOps consulting is often tailored to an organization’s maturity level. We find a suitable approach for data versioning, continuous integration, and continuous deployment pipeline management, model performance, data quality, and system health. SPD Technology provides actionable plans to boost operational efficiency and reduce costs.
MLOps Managed Services for Model Training & Deployment
Our data science and MLOps teams take care of model deployment, management, scalability monitoring, and governance. We facilitate the end-to-end lifecycle of machine learning models, starting from structured data preparation, as we did for a US-based payment company. As part of the project, we used ensemble learning techniques to combine multiple weak models into a stronger predictive model. As a result, we achieved over 80% accuracy for the AI system.
Custom MLOps Solutions Development
We craft tailored solutions to scale, automate, reproduce, secure, and support machine learning operations. Whether you need to build automated ML pipelines, enhance model monitoring and management, or improve model governance and compliance, our MLOps experts can design a custom solution for you. MLOps solutions are designed to manage thousands of models at scale, ensuring reproducibility and consistency across diverse environments.
Cloud Migration Services for ML Workloads
Our AI consulting services handle encryption, access controls, and data anonymization to ensure responsible and compliant use of AI technologies. We establish processes for machine learning model development, validation, and deployment by preparing the required documentation. We also have our hands on regular reviews of the AI models to address any concerns along the way.

Success Stories with Global Impact

SPD Technology designs and develops transformative software solutions that drive innovation, new revenue streams, and market leadership.

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How We Helped FSA/HSA Payments Company to Improve Existing Product

Industry: Finance, Payments & Fintech
Country: the USA
Team Size: 2

Less-Than-1-Day Release Cycles: Implementation of the serverless infrastructure and fast CI/CD pipelines that were configured by our team resulted in short release cycles, which is critical for a startup.
80%+ Accuracy of Our AI Solution: Achieved an impressive percentage of success in determining the eligibility of health-related products in our fully automated solution.

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Cutting-Edge AI for Predictive Maintenance in Energy Sector

Industry: Energy Management
Country: the USA
Team Size: 7

Increased Operational Efficiency: With this highly technological solution, our client achieved increased operational efficiency due to timely maintenance alerts and optimized resource allocation, resulting in better overall resource management and significantly reduced downtime.
Less Than 1 Minute Latency: Our solution achieved remarkable data processing capabilities, with an impressively short latency time for this use case. We built a solution that provides quick and accurate insights for close to real-time decision-making.

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High-Load Support AI Chatbot for an Online Fashion Store

Industry: eCommerce & Retail
Country: France
Team Size: 6

Delivered AI-Driven Chatbot for Customer Support: We leveraged a Lanchchain framework as a backbone for orchestrating the usage of LLMs (large language models) and data embeddings for question answering taks.
Process 99% of Queries Under 10 Seconds: The chatbot deployed on the autoscaling infrastructure which makes it capable of processing waves of inquiries and providing quick responses for consistent availability and increased customer satisfaction.

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AI Transforms PDF Data Extraction with 5x Cost Efficiency Boost

Industry: Finance, Payments & Fintech
Country: the USA
Team Size: 3

3x Faster Document Processing: Automating data collection and processing allowed the client’s staff to handle three times more documents within the original timeframe.
5x Cost Reduction: Increased operational speed enabled the business to process more documents, resulting in improved efficiency with the same manpower.

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Our Clients are Happy

To share their experiences

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Alex Samano

Co-Founder & CEO, Mogami App

SPD Technology has done a great job of maintaining the lifeblood of their codes. They’re transparent with pricing models and deliver within budget. Their dedicated teams act as an extension of the partner’s company. Responsibility and a commitment long-term partnership are two hallmarks of their work.

Fabrice Forget

Chief Product Officer, PitchBook

We feel very lucky to have found SPD Technology as our partner. Over the last 10 years, they have totally surpassed our expectations, and day after day we have received incredible value from the team. Here is the secret sauce, just put together your best ideas in your requirements and there is nothing they can’t do!

John Gabbert

Founder and CEO PitchBook Data

Customers are king at PitchBook and SPD Technology shares in this mission. For the last 13 years, SPD Technology has helped us scale product development and continuously deliver the product functionality our clients need to make smarter decisions.

Prasad Sridhar

Senior Engineering Manager E-comerce Solutions Company

SPD Technology’s work has been praised by the client for their consistency and high quality. Their communicative, responsive, and flexible project management ensures a positive collaboration. Ultimately, their professionalism and forward-thinking are impressive.

Shariq Ahmad

Head of Technology, Morningstar, Inc

All of their developers and technology staff are highly talented and very professional. Working with SPD felt like we were working with internal team. They are always accessible any time of the day and very flexible in providing support to our users who are globally distreibuted.

Steve Carner

Founder CEO, Home Hub

The team at SPD Technology exceeds expectations. Their professional communication style makes them stand out, They’re a skilled group of detailed-oriented workers. Customers can expect a team that provides helpful suggestions to better their clients.

Certified

By Independent Organizations

Confirmed by Oracle certification, our company provides top-notch tech expertise in building and delivering cutting-edge database and cloud-based apps.

IIBA Certification signifies our proficiency in business analysis, ensuring a deep understanding of client needs and industry requirements.rn

With AWS Certification, we guarantee top-tier cloud expertise, enabling us to architect robust, scalable, and secure solutions.

Trusting your project development to us, you can rely on our project management excellence, meticulous planning, and efficient resource utilization. rn

Our Scrum Alliance Certification demonstrates our dedication to agile methodologies, fostering collaboration and iterative development.rn

Backed by Scrum.org Certification, our development team leverages the best principles of Scrum to build superior and iterative software solutions.

Transform your ML-driven workflows with our expert MLOps services!

Supporting Businesses Through the Entire MLOps Lifecycle

We support businesses at every stage of the MLOps life cycle, ensuring alignment with regulatory requirements and seamless development, deployment, and management of machine learning models.

Model Development and Training
At SPD Technology, we leverage our ML expertise to build and train models for predictive analytics, personalized recommendations, anomaly detection, and computer vision. Feature engineering is a crucial step in this process, converting raw data into actionable features that significantly improve model accuracy and reliability. Preparation involves automated data ingestion, cleaning, and transformation into high-quality datasets for model training. We trained instance segmentation and deep imbalanced regression models for a veterinary care app.
Model Evaluation and Testing
To maximize ML model performance, we test them using data splitting, establish a baseline, validate performance, and conduct bias detection. MLOps practices ensure the development of high-quality models with advanced algorithmic architectures that deliver better accuracy and performance. Model evaluation and testing help identify and reduce errors and ensure reliable model performance. Model packaging and version control are important for safe deployment and rollback, which allows us to manage multiple model versions and quickly revert to previous states if issues arise.
Model Deployment and Scaling
Our MLOps services focus on deploying ML models that handle increasing data volumes, computational resources, and user demand without sacrificing performance. Infrastructure as Code (IaC) can be used to build reproducible deployment environments with tools such as Terraform and Kubernetes. ML models used for NLP and data extraction can process a greater volume of reports and adapt to evolving client needs, like in an AI/ML-powered automated data extraction tool we built for a B2B intelligence reporting platform.
Model Versioning and Management
We maintain the integrity, reproducibility, and reliability of ML models throughout their lifecycle. Our teams manage model changes to enable continuous improvement across the ML lifecycle. To ensure reproducibility and traceability in machine learning workflows, data versioning can be managed using data version control (DVC). We ensure models can be replicated across environments, so all team members can review, identify, and resolve issues before they are securely deployed.

Trusted Globally by Innovation-Driving Companies

From Fintech industry stalwarts to industry-leading eCommerce providers, we ensure the comprehensive alignment between emerging technologies and established business processes.

An American financial services firm that provides investment research and investment management services
Financial data and software company with offices in London, New York, San Francisco, and Seattle.
All-in-one omni commerce payment solution with contactless, fast, secure, and safe payment processing
One of the most recognizable landmarks, a company that specializes in innovative travel and hospitality services
SaaS XSPN – Next Generation Application & Cloud Security Posture Management
A leading tech-enabled insurance company that provides workers’ comp coverage to small businesses
A UK-based provider of online payment solutions to businesses of all sizes worldwide

Cutting-Edge ML Operations Expertise of SPD Technology

Our data scientists and ML engineers combine machine learning, DevOps, and data engineering expertise to streamline the end-to-end lifecycle of ML models across multiple industries. We explore innovative AI operations strategies to deliver scalable solutions and ensure responsible governance, compliance, and optimization of machine learning workflows.

Artificial Intelligence and Machine Learning

Our MLOps approach is backed by our deep proficiency in AI and ML, which enables us to understand the intricacies of model architecture and algorithm selection, automate key stages of the machine learning lifecycle, such as data preprocessing, and handle large volumes of data. We focus on making AI systems and machine learning workflows accessible and efficient, simplifying complex data processes for our clients. Our team delivers innovative solutions to solve complex business problems and accelerate the deployment of advanced models.
Data Engineering and Management

We maintain data integrity, with our data scientists and machine learning engineers overseeing data collection, storage, and processing. Our team is experienced in working with diverse datasets across industries, ensuring that MLOps consulting services are tailored to specific business scenarios and use cases. Technical flexibility ensures we, as a provider, are proficient in preferred stacks, whether open-source or proprietary tools. We automated invoice data collection and processing for a packaging manufacturer.
DevOps and CI/CD

We enhance machine learning operations through version control, IaC implementation, continuous monitoring, advanced CI/CD practices, and automated testing. We implement CI/CD pipelines for machine learning using tools such as Jenkins, GitLab CI/CD, CircleCI, and GitHub Actions. Our client received an enhanced product that enables expansion into new countries and uninterrupted operations for 35 million global users.
Cloud Development and Migration

At SPD technology, we support ML systems by architecting scalable cloud-native solutions or orchestrating seamless cloud migrations. We build robust, secure, and future-proof applications. We focus on continuous integration and delivery (CI/CD) pipelines to accelerate time-to-market. Our MLOps expertise enables us to provide scalable, efficient, and cost-effective infrastructures for ML operations. Infrastructure optimization encompasses architecting cloud-native or hybrid environments to support elastic scaling for compute-intensive workloads.
ML Model Governance and Data Security

We are dedicated to responsible model deployment and follow the best practices in model development, ensuring data quality and compliance with GDPR, HIPAA, and industry-specific regulations. We ensure regulatory compliance through robust data governance, version control, and data lineage tracking. Real-time monitoring and drift detection are crucial for tracking model performance and ensuring compliance with enterprise policies. MLOps provides robust security measures and ensures compliance with regulatory standards throughout the ML lifecycle.

Our Reliable Approach to MLOps Implementation

We operate within a systematic MLOps framework that accelerates the delivery of your machine learning projects and ensures optimal model performance. Our approach includes continuous monitoring and drift detection to reduce risk and maintain model performance throughout the ML lifecycle. By detecting and remediating model drift and data drift, we ensure your predictions remain consistent and reliable over time.

Strategic Alignment and Business Impact
Our data science and ML engineers conduct a deep analysis of your existing ML solutions, business objectives, and industry trends to identify the areas where MLOps initiatives can deliver the greatest value. We conduct a structured MLOps maturity assessment to evaluate current ML workflows, infrastructure, tooling, and business alignment, helping to identify bottlenecks and define a production-grade roadmap for your MLOps platform. We helped an eCommerce business expand its customer base by optimizing ML models to scrape 300,000 marketplace items in 3 hours, adding over a million products to their store.
Governance and Compliance Assurance
We establish a robust governance framework to ensure that machine learning projects adhere to internal policies, industry regulations, and ethical standards. This framework includes clear guidelines and procedures for data management, model development, deployment, and monitoring. With our expertise in delivering domain-specific AI/ML solutions, we can establish compliance with GDPR, HIPAA, PCI/DSS, and OFAC, and implement data anonymization techniques, access management and controls, and encryption protocols to maintain data security.
Scalability and Change Management
We employ best MLOps practices to design scalable infrastructure that can accommodate the growing demands of machine learning workloads. Our team leverages cloud computing resources, containerization, and orchestration tools to scale operations and improve model performance. We assisted the business with over 20 years in the fund management market in embracing new AI/ML-powered processes for matching asset and wealth managers and scaling them to accommodate growing customer bases. Our ability to scale operations ensures your AI/ML solutions can reliably and efficiently meet business needs as demand increases.

Being a Leading Software Product Development Company

Industry Recognition

Top Software Developer in the UK 2026

SPD Technology earns AWS Select Tier Services Partner status, validating its cloud delivery expertise

SPD Technology joins an exclusive list of the top 1000 global service providers in 2025

EBStar: Employee Recognition 2025
EBStar: Developing Employees 2025

Top Enterprise Software Developer 2025

Top Software Development Company 2025

Top Java Developer Eastern Europe
Top UK Computer Vision Company
Top Legal Software Developer 2025

Best Custom Software Developer

HR PRO Awards

The winner in the category “Reward and Recognition” 2024

HR Brand Ukraine

The winner in the category “Charity project of the year” 2023
The winner in the category “Social and Charitable Projects” 2022

Best Software Development Company
Best Custom Software Engineering Solutions

Experience the next level of machine learning efficiency with our MLOps services

How We Ensure Reliable Delivery

We prioritize a client-centric approach. Therefore, we focus our MLOps services on meeting the specific business requirements and goals.

IP Protection

We sign legal documents to define ownership and rights for both code and ideas. This includes non-disclosure, IP assignment, and license agreements.
Robust Project Management

We adhere to a clear, effective project management approach defined by meticulously established milestones, open communication, and transparent reporting.
Contingency and Risk Mitigation

We conduct thorough risk assessments at the outset to identify potential threats and vulnerabilities, and develop comprehensive mitigation strategies to minimize their impact.
Transparent Cost Structure

We ensure there are no hidden costs with our services. Therefore, we meticulously outline the scope of work and include mechanisms to accommodate changes.

ML Operations Engagement Models

Choose the engagement model that aligns with your technical scope, timeline, and internal MLOps maturity. We structure our ML operations collaboration to match different project complexities, resource availability, and long-term machine learning goals.

Project-Based
We define the scope of work and timeline to fine-tune your ML solutions. If you have specific projects in mind, we can accommodate them with dedicated data scientists and ML professionals. They will guide you through the entire process from requirements collection to delivery of tailored MLOps services.
Time and Material
Our team provides MLOps consulting services on a flexible basis to address specific aspects of your machine learning projects. MLOps consulting services provide the expertise and guidance of experienced MLOps engineers to create MLOps frameworks that streamline machine learning workflows.
Managed Services
We take full responsibility for managing and delivering MLOps services on your behalf. Our team provides a range of MLOps services, including model monitoring, maintenance, performance optimization, and support, so you can focus on core business objectives and operations.

About
SPD Technology

SPD Technology is a global software product development company with over 650 Talents and Delivery Centers across Europe.

From MVPs to advanced solutions in fintech and digital payments, data engineering, AI/ML, and cloud, we take a client-centric approach—aligning our capabilities with real-world market needs and technology challenges.

Founded in 2006, we’ve delivered over 460 custom projects for high-growth startups, SMBs, and enterprises, building strong partnerships with industry leaders such as PitchBook, Morningstar, Blackhawk Network, Poynt, Space Needle and many more. Today, four of the companies we support generate over $4B in annual revenue each—a success we’re proud to contribute to.

Through extended teams and end-to-end software development, we help businesses achieve tangible outcomes: technology modernization and automation, improved scalability and performance, reduced costs, faster time to market and to insights, enhanced security, and more.

Our goal is to become a trusted technology partner for even more businesses worldwide—delivering solutions that shape the future of industries in an ever-evolving digital landscape.

FAQ

Answering common questions on MLOps consulting and solutions.

What are MLOps consulting services?
MLOps consulting services help organizations design, implement, and optimize machine learning operations across the full lifecycle of ML models. This includes infrastructure setup, CI/CD pipelines, model deployment, monitoring, governance, and automation. The goal is to move machine learning models from experimentation to stable production environments while ensuring scalability, reproducibility, and cost control.
What is MLOps service?
An MLOps service focuses on operationalizing machine learning models. It combines data science, DevOps, and data engineering practices to automate workflows, manage model versioning, and ensure reliable model deployment. MLOps services support continuous integration, testing, monitoring, and retraining to keep ML models accurate and production-ready. MLOps enables a small team to manage hundreds or thousands of models simultaneously without a linear increase in operational overhead.
How does MLOps consulting improve machine learning operations?
MLOps consulting improves machine learning operations by introducing automation, standardized pipelines, and governance controls. It reduces manual retraining, prevents deployment bottlenecks, and enables continuous monitoring of model performance. Structured MLOps processes also improve collaboration between data science and engineering teams while lowering operational risks. Automated model training and tuning are necessary to optimize performance and ensure models are updated with fresh data.
When does a company need MLOps services?
A company needs MLOps services when ML models move beyond experimentation and require stable deployment in production environments. Warning signs include manual workflows, inconsistent model performance, slow model deployment, infrastructure scaling issues, and lack of monitoring. MLOps services help scale machine learning systems while maintaining reliability and compliance.
Is CI/CD part of MLOps?
Yes. CI/CD is a core part of machine learning operations. In MLOps, CI/CD pipelines automate code integration, testing, validation, and model deployment. Automated retraining strategies are necessary to detect and mitigate model drift, ensuring consistent and reliable predictions over time. This ensures that machine learning models can be updated, retrained, and released efficiently without disrupting production systems. Continuous integration and delivery reduce errors and accelerate time-to-market.