As businesses increasingly embed machine learning into their core operations, a significant hurdle often appears: moving AI models from promising concepts to successful, large-scale deployment in the real world. This gap between development and effective operationalization represents a major challenge in unlocking the full value of AI investments, and it’s precisely where MLOps – dedicated practices and tools for streamlining AI lifecycles – becomes critical. With many providers now offering MLOps solutions, navigating this landscape can be complex.
To offer some clarity, this article presents our perspective on ten companies that we believe are making notable contributions in the MLOps field. This selection is a result of our in-house analysis and observations from working within the AI domain.
We also want to be transparent from the outset: Addepto is included in this overview, as we are actively involved in developing and delivering MLOps solutions. It’s important to stress that the companies are not listed in any particular ranking or order of preference (from best to worst), as the ‘best’ MLOps partner heavily depends on specific project requirements and business contexts.
Furthermore, the AI landscape is in constant flux, with a huge number of innovative emerging players also doing excellent work and certainly worth considering as you explore your options.
MLOps: Transforming AI initiatives from experiments to business practice
Many organizations follow a similar pattern in their machine learning journey. Initial pilots show promising results in controlled environments, prompting expanded investment in data science talent and infrastructure.
Yet when these organizations attempt to scale from experimental projects to production systems that drive business decisions, they encounter a host of operational challenges:
ML models that performed well in development environments show inconsistent results in production.
Performance degradation goes undetected until it impacts business outcomes.
Deployment cycles stretch from days to weeks or months.
And as models multiply across the organization, maintenance becomes increasingly complex and resource-intensive.
There’s a fundamental difference between building a model that works and building a model that works in production. The latter requires an operational framework that many organizations have yet to develop.
These challenges aren’t unique to any single industry. Financial services companies implementing fraud detection models, retailers deploying demand forecasting systems, and manufacturers optimizing production processes with predictive maintenance – all face similar operational hurdles when moving from AI experimentation to implementation.
MLOps use case: ML Solutions for fraud detection
Consider a financial institution that developed a sophisticated machine learning model to detect fraudulent credit card transactions.
In the development environment, the model showed impressive results: it could identify 95% of fraudulent transactions while generating relatively few false positives.
When the model in production began running, however, several critical issues emerged:
The model’s detection rate dropped to 82%, significantly below expectations. Analysis revealed that the production data differed subtly from the training data, causing the performance degradation. This “data drift” continued as fraudsters adapted their tactics, further reducing effectiveness.
Each model update required a complex, multi-stage manual process that created significant operational friction:
Data scientists needed to extract recent transaction data from production systems, often requiring custom SQL queries and multiple approvals from database administrators due to sensitive financial information.
The extracted data required extensive cleaning and transformation—a process that consumed several days of work as data scientists handled inconsistencies, missing values, and format discrepancies between different transaction sources.
After retraining the model, a validation document detailing performance metrics had to be prepared and reviewed in a meeting with the risk management team, often delayed by scheduling conflicts.
The updated model code and parameters then needed IT department approval, requiring explanation of changes to teams unfamiliar with data science techniques.
Compliance officers had to review model documentation and outputs for regulatory requirements, a process that could take up to two weeks as they verified that the model met fairness and explainability standards.
The deployment itself required coordination between multiple teams: data engineers to prepare the production data pipeline, IT operations to schedule system updates during maintenance windows, and quality assurance to run post-deployment tests.
After deployment, a manual monitoring period followed where fraud analysts would compare the new model’s decisions against established rules, creating additional workload.
When legitimate transactions were incorrectly flagged as fraudulent, customer service representatives couldn’t explain why. The lack of interpretability created customer satisfaction issues and regulatory concerns.
The cost of maintaining the model – including data preparation, retraining, validation, and deployment – consumed nearly 70% of the project’s resources, leaving little bandwidth for improvements or new capabilities.
After months, the promised ROI had failed to materialize despite the model’s technical sophistication. The institution found itself with a theoretically powerful tool that couldn’t deliver consistent business value in practice.
The benefits of MLOps
Organizations that implement the right MLOps practices typically see clear, measurable improvements:
In financial services, MLOps helps stabilize fraud detection systems, making them more accurate and easier to maintain. With streamlined workflows, teams can update models faster and respond more effectively to new fraud patterns. These systems often shift from fragile prototypes to reliable business tools.
In retail, MLOps supports more consistent demand forecasting and inventory planning by enabling continuous model monitoring and updates. This helps businesses adapt to seasonal shifts or changing consumer behavior more effectively.
In healthcare, MLOps ensures that diagnostic support models remain reliable over time. As new medical knowledge emerges, automated monitoring and retraining keep models aligned with current best practices.
In manufacturing, predictive maintenance models benefit from MLOps by becoming more dependable. Early issue detection and consistent deployment pipelines reduce unplanned downtime and improve equipment reliability.
What is MLOps?
Machine Learning Operations (MLOps) brings together the worlds of machine learning, software development, and IT operations into a unified approach for building and maintaining AI systems that actually work in the real world. It’s the bridge that turns promising experiments into reliable business tools.
At its core, MLOps is a set of practices and tools that help organizations move machine learning models from a data scientist’s laptop into production environments where they can deliver business value.
It combines the best aspects of:
Machine learning expertise – the statistical methods and algorithms
DevOps principles – automation, testing, and continuous delivery
Data engineering – reliable data pipelines and processing
The fundamental principles that make MLOps work include:
Automation – replacing manual, error-prone steps with consistent workflows
Reproducibility – ensuring you can recreate results reliably
Frequent updates – safely deploying model improvements when needed
Constant monitoring – knowing when models aren’t performing as expected
Team collaboration – helping technical and business teams work together
Proper governance – maintaining control and compliance
Building bridges across technical disciplines with MLOps
One of the biggest benefits of MLOps is how it connects people who might otherwise struggle to collaborate effectively:
Team Role
Primary Focus Areas
Data Scientists
Model accuracy and statistical methods
Experimental approaches to problem-solving
Specialized tools like Python notebooks
Analytical insights from data
Data Engineers
Data pipeline development and maintenance
Data quality and consistency
ETL processes and data transformations
Data storage and access patterns
Software Engineers
Clean, maintainable code
System architecture and scalability
Integration points with existing applications
Development best practices
MLOps creates shared processes and tools that help these different specialists work together smoothly toward common goals, with each group understanding how their work fits into the bigger picture.
When to implement MLOps
The journey from a single promising model to a portfolio of AI assets that consistently deliver business value isn’t linear. As we’ve seen in the financial institution example, even sophisticated algorithms can falter when deployed without the proper operational foundation.
But this raises an important question that many organizations struggle with: at what point does the investment in MLOps infrastructure become necessary?
Unlike traditional software development, where operational best practices are well-established, the relatively young field of machine learning often leaves organizations navigating uncharted waters. The transition point isn’t always obvious, especially when early successes with smaller projects create the illusion that scaling will be straightforward.
Is your organization ready for MLOps?
You’ve invested in data science talent, developed promising models, and seen the potential of machine learning to transform your business. But now you’re wondering: is it time to get serious about MLOps? This section will help you recognize the signs that your ML initiatives have outgrown ad-hoc approaches and need a more structured operational framework.
Think of MLOps as similar to moving from a small kitchen garden to commercial farming. What works when growing a few tomato plants won’t scale to acres of crops. The same principle applies to machine learning—what works for one experimental model often breaks down when you’re managing multiple models in business-critical applications.
Key triggers for MLOps implementation
The most fundamental trigger occurs when machine learning models transition from experimental environments to production systems that impact business operations or customer experiences. This shift introduces new requirements for reliability, monitoring, and maintainability that typically exceed what manual processes can effectively manage.
Signs that you’ve reached this inflection point include:
Models are informing decisions with financial or operational impact
Predictions are being exposed to customers or integrated into products
Stakeholders are becoming dependent on model outputs for regular workflows
Models need to run at specific intervals with guaranteed availability
As your organization’s machine learning initiatives expand, several scaling challenges emerge that MLOps directly addresses:
Scaling Dimension
Warning Signs
MLOps Solution
Model Quantity
Managing updates for multiple models becomes unwieldy
Inconsistent deployment processes across different models
Difficulty tracking which models are running where
Standardized pipelines and model registries that create consistency across all models
Team Growth
Knowledge silos forming around specific models
Inconsistent practices between different data scientists
New team members struggle to understand existing models
Documented workflows, shared tools, and collaboration frameworks
Requirements for model explainability or fairness assessments
Audit trails for model development and deployment
Data privacy considerations and controls
MLOps provides the structured processes and documentation necessary to demonstrate compliance and manage risk appropriately.
Performance and quality concerns
Operational issues with deployed models often highlight the need for MLOps:
Unpredictable model performance in production
Data drift leading to declining accuracy over time
Inconsistent results between testing and production environments
Difficulties reproducing previous model versions when problems occur
These challenges signal that your ML initiative has outgrown ad hoc approaches and requires more robust operational practices.
Key considerations for MLOps implementation
Successfully putting Machine Learning Operations (MLOps) into practice is about more than just adopting new tools; it’s about creating a robust system where your machine learning models consistently deliver value once they are live. So, what does effective MLOps actually look like when it’s working well in real-world business applications?
1. Model performance
When models start failing in production, teams scramble to understand what changed – was it the data, the model version, or something in the pipeline?
MLflow and DVC handle experiment tracking and model versioning, while Weights & Biases offers more sophisticated visualization for research teams. For production monitoring, tools like Evidently.ai and Arize.ai catch performance drift before customers notice problems. Feature stores like Feast or Tecton ensure the data your model sees during training matches what it gets in production.
2. Data infrastructure
Models are only as good as their data, but still, many organizations spend 80% of their effort on algorithms and 20% on data quality.
Tools like DVC or Pachyderm track not just code changes but data changes too. Your pipelines need validation checks that catch schema drift, missing values, and data quality issues before they poison model performance.
Most importantly, you need comprehensive logging of every transformation. When something breaks, you want to find the root problem immediately instead of spending days debugging.
3. Deployment
Moving models from notebooks to production involves more than just “putting it in the cloud.” Your infrastructure choices here will either enable rapid iteration or create bottlenecks that slow everything down.
Traditional CI/CD doesn’t quite fit machine learning workflows, as it involves not just deploying code but also models, data transformations, and often complex pipelines that span multiple systems. Tools like Kubeflow, MLflow, or cloud-native solutions like SageMaker can automate these workflows, but they require upfront investment in setup and learning.
This is the huge question: build custom pipelines for maximum control, or use managed services? Most teams underestimate how much operational effort custom solutions require, but – on he other hand – ot is true that they often deliver faster time-to-value.
4. Collaboration and documentation
The transition from Jupyter notebooks to production-ready code often becomes a friction point between software engineers and data scientists. Some organizations solve this by having engineers rewrite everything; others invest in tools that bridge the gap. Either approach works, but a clear process would be beneficial, which makes documentation critical issue. Every experiment should capture what was tried, why it was tried, and what was learned. When a data scientist leaves, someone else should be able to understand and continue their work without starting from scratch.
5. Security, compliance, and integration
Machine learning introduces new attack vectors that traditional security teams may not understand. Model inversion attacks, data poisoning, and adversarial examples are real concerns for production systems.
Integration with existing enterprise systems brings its own challenges. Your ML platform needs to respect the same security boundaries, access controls, and audit requirements as other business systems. This often requires close collaboration with security and compliance teams who may be unfamiliar with ML-specific risks.
Financial services and healthcare face particularly strict requirements. Model explainability isn’t just nice to have—it’s often legally required. Plan for this early, because retrofitting explainability onto complex models is much harder than building it in from the start.
Top MLOps Consulting Companies in 2025
As organizations seek to operationalize machine learning at scale, specialized consulting partners have emerged to design and implement effective MLOps strategies. This ranking highlights companies with proven expertise in turning ML experiments into production systems.
1. Addepto
Addepto concentrates on the practical aspects of building and managing the entire lifecycle of your machine learning models, offering specialized MLOps expertise. Our structure allows for greater agility, meaning we can adapt quickly to your specific MLOps needs, provide more personalized solutions, and offer direct access to our experienced team. This often translates into a focused engagement aimed at moving your AI initiatives from development to production smoothly and effectively, without the overhead sometimes associated with much larger service providers.
To ensure we can robustly support even major clients with complex and large-scale MLOps requirements, Addepto has formed a strategic partnership with Grape Up. This collaboration significantly boosts our capacity to deliver, providing enhanced capabilities in enterprise-grade cloud engineering and the ability to scale solutions effectively. This means that while you benefit from Addepto’s dedicated MLOps focus and adaptable service, you also gain the assurance of broader technical depth and delivery power necessary for substantial, enterprise-level projects.
Therefore, if your organization is looking for an MLOps partner that combines specialized know-how and a client-centric, flexible approach with the proven ability, strengthened by our Grape Up alliance, to serve significant enterprise needs, Addepto is a company worth strong consideration to help you successfully operationalize your AI investments.
2. Accenture
Accenture is one of the world’s largest consulting firms, working with enterprise clients across industries such as finance, healthcare, retail, and manufacturing. They have a technical expertise, especially in cloud infrastructure, data engineering, and AI/ML, and they often partner closely with platforms like AWS, Microsoft Azure, and Google Cloud.
Its strength lies in delivering large-scale, enterprise-grade solutions, so their services may be better suited for companies with substantial budgets and complex needs. Smaller businesses or those looking for highly specialized, research-driven AI innovation might find more tailored support elsewhere. Still, for many large organizations aiming to integrate AI into core operations, Accenture’s mix of technical depth and business consulting makes them a strong partner
3. Deloitte
Like Accenture, Deloitte is a large consulting firm that helps big companies tackle both business and technology challenges. Their AI and machine learning services — through groups like Omnia AI and the AI Institute — are part of a broader offering that covers strategy, operations, compliance, and more. They focus on making sure AI projects are practical, secure, and aligned with the way an enterprise actually runs.
Deloitte has a strong presence in industries with strict regulatory requirements, like finance and healthcare, and they’re known for helping clients manage risk while rolling out new tech. Their approach is structured and steady, which works well for large organizations with complex systems and long-term goals. They may not lead in experimental AI, but for enterprise-grade solutions that fit within a larger business context, they’re a dependable partner, much like Accenture.
4. Cognizant
Cognizant is a global consulting and technology company that works with large businesses across different industries. Its AI and Analytics group helps clients make AI a regular part of how they work — from building models to managing them at scale. They use their own tools and frameworks to support this process, focusing on systems that can grow with a company’s needs.
Because Cognizant is built to serve big, complex organizations, they’re well suited for enterprises looking for stable, scalable solutions. That also means they may not be the fastest or most flexible choice for smaller companies or teams focused on niche, cutting-edge AI work. But for large-scale, long-term projects, Cognizant brings the structure and global reach to deliver.
5. Capgemini
Capgemini helps large companies turn their AI ideas into working systems, offering support to launch, monitor, and grow those efforts over time. They use their own AI Engine and MLOps tools to make sure models not only run smoothly but also fit well with a company’s existing tech — whether in the cloud or on local devices. Their strength lies in building reliable, scalable solutions that connect with the broader IT environment.
As a large organization used to handling complex projects, Capgemini is well suited for enterprises that want to integrate AI into their operations and grow it steadily. That also means they might not be the best fit for smaller companies or teams looking for highly specialized, fast-moving AI projects. But for businesses that need structure, scale, and solid tech integration, Capgemini offers a steady hand.
6. Tata Consultancy Services (TCS)
TCS helps big companies consistently use AI across their operations and expand its use safely. They use their own AI system (called Ignio™) and a structured method (their “MLOps Factory”) to do this, with a big emphasis on making sure the AI is reliable and follows all the rules. Because TCS is a large organization itself, good at managing complex projects for other large companies, they are best for businesses that need to roll out AI in a controlled, predictable manner and want to be sure it’s trustworthy and compliant. Smaller companies or those looking for very new, quick AI experiments might find TCS’s methodical, large-scale approach less of a fit for their immediate needs.
7. Infosys
Infosys uses its Cobalt cloud platform along with its artificial intelligence and machine learning tools to help businesses, especially larger ones, build and manage AI systems designed to last. They are adept at making these systems work effectively even when a company’s technology is spread across its own private infrastructure and public cloud services, always emphasizing responsible, ethical AI use and improvements to how daily operations run. This makes their approach particularly well-suited for established organizations that already have complex IT setups and are looking for reliable, sustainable AI solutions to enhance their existing large-scale business functions while adhering to strong ethical guidelines.
However, because Infosys is geared towards providing these comprehensive and robust solutions for larger enterprises, their methods might be more involved than what a smaller company or a startup typically needs. If your main goal is very rapid, experimental AI development, rather than deep integration into extensive existing systems or a primary focus on long-term, structured AI management and ethical frameworks from the outset, their thorough approach might not be the quickest or most direct path for your specific situation.
8. IndataLabs
IndataLabs focuses on creating custom AI and machine learning solutions, and a significant part of their process involves making sure these systems are effectively implemented and managed in real-world operations using MLOps principles. While their core expertise is in data science and developing advanced AI models, including Generative AI, they manage the complete machine learning lifecycle. This means they don’t just design algorithms, but also concentrate on deploying them into practical applications, continuously monitoring their performance, and maintaining them to ensure they deliver lasting value.
Their development of custom AI software and intelligent automation systems includes key MLOps elements such as building reliable deployment procedures, ensuring the solutions can scale, and establishing methods for ongoing model management and updates. So, for businesses looking not only to develop innovative AI but also to have it reliably implemented and sustained in a production environment, IndataLabs offers an approach where MLOps practices are a fundamental part of their end-to-end AI solution development, aimed at delivering AI that works effectively over the long term.
9. Innowise Group
Innowise Group provides machine learning operations (MLOps) consulting for companies in the logistics, healthcare, and finance industries, helping them set up and manage their machine learning projects. Their services focus on automating AI-related processes and designing systems that can scale as a business grows; they also state that their approach includes working with client teams. This positions them as an option for organizations within their targeted sectors that require assistance in building and managing operational AI systems with an emphasis on automation and future scalability.
However, their specialization in MLOps for these particular industries means they are less likely to be suitable for companies operating outside these sectors or those primarily seeking broad AI strategy development rather than operational deployment. Furthermore, businesses looking for minimal, quick-start AI experiments may find Innowise Group’s approach, which is geared towards more complete operational systems, to be more extensive than their immediate needs.
10. LeewayHertz
LeewayHertz concentrates heavily on the practical and technical execution of artificial intelligence, offering specialized services to help businesses in healthcare, finance, and retail design, build, and manage their machine learning systems comprehensively. Their work involves deploying these AI systems using modern cloud-based approaches, with the goal of ensuring these systems operate effectively and reliably within a company’s cloud infrastructure.
This deep focus on the operational aspects of AI and cloud technology means LeewayHertz is primarily set up for enterprises that are prepared to implement or expand their AI capabilities in the cloud and require expert support throughout the entire lifecycle of these machine learning systems. As a result, they might not be the first option for organizations whose AI requirements do not involve cloud environments or for those seeking high-level AI strategy or policy guidance rather than the specific technical development, deployment, and ongoing management of AI.
Final thoughts: How to Choose an MLOps Consulting Company
Know what you need from MLOps First, pinpoint what you expect your MLOps setup to achieve. Define your specific goals, such as better tracking of model versions, automating model retraining, improving how live models are monitored, or meeting specific compliance requirements. Clear technical and operational needs will help you assess if a consulting company’s services match your objectives.
Check for complete MLOps support Look for a company that can assist through the entire machine learning lifecycle. This includes support from preparing your models and deploying them, to maintaining their performance with ongoing monitoring and updates. A suitable partner should be able to advise on MLOps strategy and also handle the technical work of moving your AI models from development to live operation effectively.
Prefer open tools and practices Consider companies that use common open-source MLOps tools and established industry practices. This approach helps prevent you from being locked into a single vendor’s proprietary technology and makes it simpler and more cost-effective to adapt or change your MLOps setup as new tools and techniques emerge.
Review their MLOps work Examine the company’s past MLOps projects. Ask for concrete examples of their work, especially for projects that are similar to yours or within your industry. Proof of successful MLOps implementations indicates their reliability and ability to deliver tangible results.
Confirm integration with your systems Make sure the consulting firm can integrate its MLOps solutions with your existing IT systems and your team’s current ways of working. The aim is to avoid MLOps processes that operate in isolation or require your team to use entirely different toolsets for machine learning compared to your other software systems.
Look for strong model monitoring and data handling Effective MLOps depends on closely monitoring deployed models and managing data properly. Seek firms that demonstrate they prioritize tools and methods for tracking model performance (such as dashboards and alerts), recording details of experiments, and building reliable data pipelines. This is important for the long-term reliability and performance of your AI models.
Assess their knowledge of MLOps tools Evaluate the company’s practical experience with the MLOps platforms and tools relevant to your needs. This might include tools like MLflow, Kubeflow, Airflow, or the MLOps services from major cloud providers. Hands-on experience with these tools is crucial for a successful MLOps implementation.
