What is MLOps?

MLOps stands for Machine Learning Operations. MLOps is a set of principles applied to the lifecycle of machine learning models. It brings together ML, DevOps, and Data Engineering. It focuses on organizing and automating the processes involved in building, deploying, maintaining, and monitoring the ML models effectively in a production environment. 

The core principles of MLOps include Continuous Integration (CI), Continuous Delivery (CD), Continuous Training (CT), and monitoring.

How We Selected the Top MLOps Companies

Top MLOps companies are selected using their comprehensive framework and multi-factor methodology based on market data and best practices. We have used the following criteria to highlight the companies that not only offer technical expertise but also deliver measurable value.

• Years of experience: We have hand-picked companies with longer histories in MLOPs and machine learning operations, which says something about their stability and sustained innovation.
• Services offered: We have evaluated the company’s core business model to determine if they are a platform provider or a service provider. Mainly, we have listed companies with multi-stage MLOps service options (model deployment, monitoring, governance, pipeline automation, and strategic consulting)
• Technical capabilities: A MLOPs company must offer all the key stages of the ML lifecycle. We looked for a deep and mature set of capabilities, including CI/CD for ML, Model governance, monitoring, scalability, and observability.
• Flexibility: The best MLOps companies are the ones which has avoided vendor lock-in. A flexible company can work with a client’s existing data stack, CI/CD tools, and observability platforms. 
• Client review and testimonials: We have looked at the client review, peer rankings, analyst reports, enterprise testimonials, and awards from major platforms like Clutch.
• Pricing models: We have considered the pricing models of each company. We have prioritized vendors with clear, accessible pricing models (usage-based, enterprise, project pricing) and compared them with other MLOps companies for budgeting.

Top MLOps Companies

1. Entrans

Entrans is a leading MLOps Service Provider and AI-first consulting firm that excels in MLOps, automation, and data engineering. It also works on creating efficient data workflows. Because of this, it has a proven track record in complex projects.

Key MLOps services of Entrans

• End-to-End Lifecycle: Entrans helps by designing and implementing production-ready end-to-end MLOps pipelines. This includes setting up automated workflows for data ingestion, model validation, and continuous integration/ continuous delivery (CI/CD) for ML models. 
• Model Deployment and Management: We help clients in setting up automated monitoring to detect issues like model drift and optimizing performance and costs in production environments. We deliver scalable and resilient ML models for deployment.
• Automated Machine Learning (ML) pipelines: Entrans automates pipelines for model training, testing, and deployment. We also ensure that new models and code changes are seamlessly integrated and released to production. We implement systems to track and manage different versions of datasets, models, and code. 
• Governance and compliance: We establish robust governance frameworks for ML solutions to make them compliant with data privacy regulations like GDPR and HIPAA. 
• Consulting and Strategy services: We offer strategic guidance on building and implementing an MLOps roadmap. We help companies to choose the right tools and technologies by ensuring their AI strategy aligns with business value.
• Infrastructure and Cost Optimization: We assist with designing infrastructure-aware solutions, comparing different cloud-native, edge, or hybrid Strategies to find the balance between performance and cost.
• Staff Augmentation: Entrans gives the ability to quickly staff projects. We can assist the clients by providing them with DevOps, cloud, and AI engineers through either onshore or offshore models.

2. Accenture

Accenture is a leading professional services company that provides a comprehensive suite of MLOPs services as part of its data offerings. It is a service provider that gives custom solutions rather than providing an MLOps software product.

Key MLOps services of Accenture

• Accenture designs and builds automated CI/CD pipelines for machine learning models for model training and validation to deployment.
• They also provide strategic MLOps consulting services to help clients use the right tools and technologies.
• Accenture provides services to ensure AI solutions are secure and compliant. They handle model governance, implementing automated checks for model fairness, bias, and regulatory requirements.

3. Cognizant

Cognizant is a leading IT service provider and consulting firm that offers MLOps services as part of its data and AI offerings. 

Key MLOps services of Cognizant

• Cognizant offers a full set of MLOps services, which are wrapped around cloud providers like AWS. Their team of scientists and engineers is responsible for building a complete, production-ready ML lifecycle.
• They also utilize other cloud platforms like AWS SageMaker to deploy the ML models. They also use tools like Docker and Kubernetes to ensure models are portable.
• Cognizant offers ML advisory and Strategy services to help businesses understand their AI maturity. They make sure that the MLOps initiatives are aligned with the core business.

4. Wipro

Wipro is a well-known MLOps service provider that helps clients operationalize their AI and ML initiatives. They are mostly project-based and customized to the specific needs of the customer.

Key MLOps services of Wipro

• The core MLOps service offered by Wipro is end-to-end MLOps engineering. They manage the entire MLOps lifecycle, which includes creating CI/CD pipelines for ML models, managing data, and model versions.
• Wipro has extended its capabilities to LLMOps. They provide services for managing the lifecycle of large language models, including prompt engineering and cost optimization. 

5. IBM

IBM is a leading IT service provider and provides MLOps as a Service through its technology platforms and consulting expertise.

Key MLOps services of IBM

• IBM’s MLOPs service is rendered through their IBM WatsonX platform, which includes services like Watson Machine learning and Watson OpenScale.
• Through their centralized platform, they govern, secure, and monitor ML models. It automates checks for fairness and bias and can simplify compliance with emerging regulations like the EU AI Act.
• They also offer MLOps consulting services by providing guidance, helping clients assess their AI maturity, and developing a custom MLOps roadmap.

6. CGI

CGI is an MLOps service provider that offers both consulting and MLOps services to accelerate its AI journey. It delivers services through a comprehensive approach called CGI AccelerateAI 360.

Key MLOps services of CGI

• CGI specializes in providing complete MLOps pipelines. CGI AcclerateAI360 is a cloud-agnostic platform that provides automation tools to manage the whole ML delivery lifecycle.
• CGI provides services to deploy models, monitor them in production, and perform necessary maintenance. This is one of the main AI Managed services.

7. Capgemini

Capgemini provides MLOPs service as a service provider. It utilizes IT consulting and digital transformation to help clients design, build, and manage their machine learning operations using a variety of tools.

Key MLOps services of Capgemini

• Capgemini provides end-to-end MLOps engineering services, which help teams to build and implement complete MLOps pipelines. They ensure that the ML models perform reliably in production.
• They incorporate their Responsible AI framework into their MLOps services. This helps them to follow regulated standards and stay compliant.

8. Infosys

Infosys is a pioneer MLOps service provider that helps businesses to industrialize their AI and machine learning initiatives by offering a range of services that cover the entire MLOps lifecycle.

Key MLOps services of Infosys

• Through their cobalt platform, they provide predictive analytics, anomaly detection, business-context monitoring, alert noise reduction, etc.
• They also provide continuous monitoring to detect data drift and model drift. They also ensure that the ML models are compliant with regulations and maintain a central model repository. 

9. ZS

ZS offers MLOps as a Service as a component of its larger AI and analytics consulting practice. They specialize in sales and marketing with a strong analytics and AI practice.

Key MLOps services of ZS

• ZS offers MLOps services through the ZAIDYN platform. This allows clients to operationalize their models and analytics from strategy to execution.
• They help clients to establish robust practices for model governance. ZS has deep domain expertise, particularly in life science, healthcare, and pharmaceutical industries.

10. T-Systems

T-Systems is a global IT services and consulting company that offers MLOps services. They give a range of services to help businesses implement and manage machine learning operations.

Key MLOps services of T-Systems

• T-systems designs and builds complete MLOps pipelines. This includes building up data management systems, automating model training, and CI/CD deployment.
• They also provide MLOps consulting services by assessing their current state of model readiness, MLOps strategy, and giving guidance to select the tools and technologies for their specific business goals.
• They mainly focus on building MLOps solutions for specific industries such as automotive, healthcare, and the public sector.

Main Services Offered by MLOps Companies

MLOps companies offer a wide range of services to help businesses organize and automate the entire Machine learning life cycle, starting from development to production. The main services provided by MLOps companies include

• End-to-End Model deployment: MLOps companies automate and manage the overall process of model building and deploying it in production. They establish robust pipelines to automate deployment by ensuring smooth movement to the production environment.
• Model Monitoring and Maintenance: The Main service of MLOps Companies is implementing real-time monitoring of deployed models to track their performance metrics, detect anomalies, and identify issues like data drift.
• Continuous Integration/Continuous Delivery (CI/CD) for ML: MLOps automates the integration and deployment of changes to ML code, data, and models. This will make sure that new and updated models are released quickly and reliably.
• Consulting and Strategy: MLOps company also offers guidance and implementation support to help businesses build custom ML pipelines and integrate MLOps practices into their workflows. They also accelerate their machine learning initiatives, reduce operational costs, version control, documentation, and centralized registry for models and experiments to make the model accurate, reliable, and stay compliant.
• Automated ML pipelines: MLOps companies design and manage pipelines for processing, cleaning, and validation. This step will ensure that machine learning models are trained and produce unbiased results. 
• Model governance and compliance: The MLOps company ensures that all actions are tracked, logged, and auditable to comply with industry standards. This includes managing ethical concerns like bias and fairness.

How to Choose the Right MLOps Company for Your Business

Machine Learning Operations (MLOps) has become a necessity nowadays for organizations that want to scale AI initiatives effectively. Choosing a correct MLOPs company becomes a critical decision that can determine the success or failure of your machine learning initiatives. With so many suppliers in the market, the following criteria will help you find the best fit based on your needs.

• Define your needs: Before choosing an MLOps company, clearly define what you want from MLOps. Decide on whether you need a platform provider or a service provider. If you have a skilled team, then the platform provider is the best choice, and if you have a less experienced team, a consulting ot service provider could be more suitable.
• Proven experience: Look out for a company with a proven track record in your industry. For example, regulated sectors like healthcare, finance, and manufacturing need an MLOps partner who should be compliant with industry standards such as HIPAA, GDPR, etc.
• Experience and expertise: Verify that the company can deliver a full MLOps lifecycle. Starting from data pipeline management, CI/CD for ML models, model monitoring and retraining, governance, explainability, and compliance.
• Tooling and platform flexibility: MLOPc company should not be locked with a single vendor. If it is a platform provider, then there are possibilities of vendor lock-in. The right partner should be well-versed in a variety of tools and platforms, including versioning, orchestration, cloud platforms, containerization, and monitoring.
• Governance and compliance: Consider a MLOps company that has a strong focus on security, data privacy, and the ability to provide audit trails and model explainability.
• Business value: The right MLOps service providers are not limited just ot code; they provide actual business value. This mainly includes aligning model goals with business outcomes, faster time to market, creating dashboards and visualizations, and building retraining policies based on business events.

Future Trends in MLOps

MLOps will see a shift towards automation, specialization, and integration of cutting-edge technologies. 

• Automated machine learning (AutoML): MLOps platforms are increasingly using AutoML for most tasks, such as model selection, hyperparameter tuning, and data preprocessing. AI-powered algorithms will be used to monitor deployed methods for drift bias and performance degradation.
• Self-healing pipelines: MLOps pipelines will be smarter to predict and prevent failures even before they occur. It will itself start addressing issues to ensure continuous operations.
• LLMOps and Generative AI: With the combination of large language models (LLMs) and gen AI, a new subfield is generated called LLMOps (Large Language Model Operations). LLMOps will focus on prompt engineering and management, cost optimization and governance, and safety.
• Standardization of MLOps practices: The Future will see more of standardized frameworks and best practices. Enterprises will push for consistency to improve interoperability and reduce operational complexity.
• AI-powered MLOps: MLOps itself will utilize AI to optimize pipelines. Self-healing models, pipelines, automated scaling, anomaly detection, and predictive maintenance of ML workflows will make operations more resilient.
• Enhanced model governance and Compliance: As AI is involved in critical business decisions, ML models will be developed to automatically generate audit trails, track data and model lineage, and ensure compliance with regulations like the EU’s AI Act. MLOps platforms will use more robust XAI tools so that decisions can be easily explainable.

Why Choose Entrans as Your MLOps Partner?

Without MLOps operation, our machine learning models are becoming obsolete. With the right MLOps partner like Entrans, businesses can deploy faster, easier to scale, and maintain ML systems that truly deliver. With our skilled developers and proven track record, we take care of the operational complexities while your internal team can focus on building innovative solutions. 

Entrans brings along the specialized knowledge in data engineering, model deployment, monitoring, and governance. We are also compliant with governance policies by following versioning, explainability, bias detection, and audit trails.

Want to know more about how we make your machine learning models to stay innovative and responsible? Book a consultation call with us.