Be it Azure data engineering or AWS IaaS solutions, managing the ML model lifecycle is crucial for businesses to derive actionable insights. Here, we’ll discuss the top fifteen ML engineering companies, businesses can partner with in 2025 to optimize their data-driven models.
Machine learning is part of artificial intelligence and involves algorithms that support an application or a model. Businesses that invest in AI also use machine learning, data engineering, cloud solutions, and other relevant technologies to transform their processes digitally and vertically.
The global ML engineering market is expected to be $79.29 billion in 2024 and reach $503.40 billion by 2030 at a CAGR (compound annual growth rate) of 36.08%. According to Fortune Business Insights, the global MLOps market size was $1,064.4 million in 2023 and is expected to reach $13,321.8 million by 2030 at a CAGR of 43.5%. According to the Business Research Company, North America is the largest region driving growth in the MLOps market. Straits Research says that North America is the dominant region in MLOps adoption with a 45.2% market share.
More statistics show that 57% of businesses use machine learning to enhance customer experience, while 49% use it in sales and marketing. Additionally, 48% of businesses worldwide use ML models and technologies in some form.
Like other advanced technologies, machine learning requires expert talent and skills. Enterprises should hire ML engineers, data scientists, data analysts, etc., to build, develop, and maintain machine learning models in their business. Since starting from scratch is cost-intensive, organizations can partner with ML engineering companies to gain access to the required talent and technologies. Working with a certified service provider reduces the risk of losses and increases the success rate.
In this blog, we’ll learn more about MLOps and the top fifteen companies offering this service in 2025.
Machine Learning (ML) Engineering is short for machine learning operations, a practice set that simplifies and automates ML workflows. It is the central function of machine learning engineering and deals with the development, deployment, monitoring, and maintenance of various ML algorithms and models that support business operations. MLOps is not an independent activity but a collaborative practice that includes data science, DevOps, data engineering, data analytics, and more. It is useful in many ways. A few examples of machine learning engineering include demand forecasting, automation, product recommendations, sentiment analysis, measuring customer lifetime value, etc.
In North America (USA), ML engineering is an integral part of data engineering. During the last few years, there has been a 74% annual growth in demand for ML and AI-related roles. The demand will continue and grow by 40% between 2023 and 2027. The average pay of an MLOps engineer is $100K per year, making it a lucrative option for IT professionals. Meanwhile, organizations are actively partnering with experienced service providers to make the most of their data engineering and MLOps services. The BSFI industry has the highest share of MLOps (over 18%) for fraud detection, yield management, preventive maintenance, etc.
Businesses will find it convenient and cost-effective to build, deploy, and maintain the MLOps frameworks on cloud platforms like Azure, AWS, and Google. This also empowers the organization during its digital transformation journey and reduces the pressure of maintaining the expensive IT infrastructure on-premises.
The machine learning lifecycle is complex and includes many stages, starting from data ingestion (feeding data to the algorithm). This requires a team effort from experienced professionals and strict regulations to ensure the models work accurately and provide reliable results. Additionally, the ML models have to be continuously monitored to improve the process and enhance the outcome. Since data is the core of AI and ML models, organizations should hire companies that offer end-to-end data engineering services along with MLOps solutions.
DataToBiz is among the best ML engineering companies offering end-to-end and tailored AI and ML solutions for startups, SMBs, and large enterprises from different parts of the world. The company is a certified partner of Microsoft (Gold), AWS, and Google. It provides customized cloud development and transformation services, along with artificial intelligence consulting, data warehousing, data analytics, etc. With guaranteed NDA and IP protection, the company ensures the client’s confidential data remains safe.
Businesses can achieve flexibility, scalability, and agility in their workflows by partnering with the company. DataToBiz relies on advanced and effective MLOps technologies to streamline, automate, manage, and continuously improve the machine learning models in an enterprise. Businesses can make accurate and proactive data-driven decisions in real-time and achieve success.
Fractal Analytics is on the leading ML engineering companies list of USA-based service providers. It helps clients bridge the gap between machine learning development and enterprise production development by optimizing internal processes. The company manages everything from data collection to model training and deployment, long-term maintenance, and regular upgrades.
By automating the deployment of ML models, the professionals create a streamlined solution that sustains the data-driven models in an enterprise. Since continuous training and continuous monitoring are a part of MLOps, businesses can be assured of developing a reliable machine learning model to analyze large amounts of historical and real-time data. Fractal Analytics offers MLOps services in three ways – building MVP, staff augmentation, and full project.
Tiger Analytics is an AI and analytics service provider that helps businesses solve various challenges hindering their growth. The company uses the best MLOps tools to make sure the AI and ML models deliver accurate and reliable results throughout their lifecycle. Be it faster development cycles, seamless fine-tuning, continuous improvement, or robust maintenance, the company takes care of everything.
It follows the engineering best practices to build, deploy, test, maintain, and monitor the machine learning models for different departments and verticals in an enterprise. Tiger Analytics offers MLOps as a strategy and a service alongside DevOps as a service through public and private cloud platforms. It builds powerful cloud-native apps for businesses to make real-time decisions.
Genpact is a software and IT service provider with a global client base. The company uses MLOps platforms to integrate the industry’s best practices into clients’ processes and converts its AI aspirations into reality. It guides enterprises in making the right decisions about AI and ML technologies to unlock their full potential.
The company mitigates the risk of failure and losses by effectively managing the lifecycle of machine learning models while focusing on continuous innovation. It helps each client overcome the challenges specific to its industry by developing MLOps practices that can be seamlessly implemented and followed in the enterprise. Genpact leads the way with innovation and makes businesses resilient by future-proofing the systems.
MathCo is a global enterprise AI and analytics company offering custom data products through its innovative hybrid model. The company builds the required AI and ML models for the clients and maintains them through MLOps services. It supports every stage of the MLOps pipeline by developing a comprehensive framework to optimize the machine learning models.
The company has helped many businesses in the automotive industry to tackle the challenges by streamlining their processes. The time-consuming task of monitoring and maintaining the ML models is handled by the company to free up the client’s resources and bridge the gap between development and deployment. MathCo also reduces operational costs and accelerates time to market.
Hansa Cequity helps brands build stronger bonds with their customers by providing analytical insights and strategic solutions. The company uses artificial intelligence and machine learning to build and engineer data-driven solutions for clients from four major industries. From predictive analytics to transformative insights, the company provides the necessary support for businesses to achieve their goals.
It also builds and maintains MLOps platforms built using open-source tools and technologies. Hansa Cequity uses state-of-the-art algorithms to decode the input data and provide automated insights that give businesses an edge in the market. The company also works with generative AI and provides innovative solutions to optimize the applications in a business.
SG Analytics is a data analytics company offering diverse services to organizations from varied parts of the world. It provides tailored data engineering services to streamline the business data processes and maximize the results through its tried and tested solutions.
With deep experience in data architecture and engineering, the company assists enterprises in understanding important insights, identifying trends, and adjusting their processes to align with the shifting technological landscape. SG Analytics creates innovative data engineering and MLOps solutions for businesses to effectively manage their data and ML models. This enables proactive data-driven decision-making that puts businesses one step ahead of competitors in this fast-paced world.
Ascendion is among the well-known ML engineering companies enhancing engineering productivity for clients from different parts of the world. It builds and deploys advanced AI models and manages them through MLOps best practices to provide impactful results to businesses. The company uses generative AI to build better and more powerful software applications that align with enterprise requirements in several industries.
It uses AVA as the foundation for its AI and ML applications. The company’s platform solutions are designed to create quick, efficient, and cost-effective models. Ascendion builds its models and manages the machine learning lifecycle on cloud servers. It also offers cloud migration and modernization services and builds new models for the future.
Factspan is an AI analytics company offering fluid intelligence solutions for enterprises to skillfully navigate the current market landscape and make proactive data-driven decisions. It partners with Fortune 500 firms and other organizations to build an analytics center of excellence and generate actionable insights from raw data.
The company builds and optimizes ML models using advanced technologies like deep learning. It is also proficient in using various data analytics tools. From data mining to continuous risk monitoring, the company takes care of all stages of the machine learning lifecycle through its MLOps services. Factspan’s cloud data solutions are designed to seamlessly manage large volumes of business data to run real-time analytics.
StratLytics is a management consulting firm with an analytical focus. It uses machine learning techniques, statistical modeling, and predictive analytics to help clients make proper business decisions through the data-driven approach. The company combines DataOps and MLOps practices to deliver high-quality and relevant data and ML models to business organizations.
It enables enterprises to continuously run the machine learning models from end to end to derive the expected results. StratLytics automates pipeline deployment, manages the configurations, etc. It also provides complete application support to enhance user experience (employees and customers) by keeping the software free of bugs and glitches. The company offers customized Application Support-as-a-managed service for its clients.
Ganit is a data and decision management company that was founded to help businesses streamline their datasets and effectively make data-driven decisions to mitigate risk and maximize opportunities. It is one of the ML engineering companies that designs and deploys purpose-built AI and ML models across various industries.
The company helps enterprises harness the power of data and AI to accelerate business growth and expansion. Ganit’s solutions deliver faster, smarter, and actionable insights to overcome different challenges faced by clients. The company also offers scalable AI and ML services. It provides a range of analytics like forecasting, sentiment analysis, S&OP analytics, CRM analytics, recommendation engines, and so on.
Databricks is one of the leading data intelligence and ML engineering companies in the global market. It not only provides powerful AI and ML platforms like Mosaic AI but also offers supporting services like MLOps to maintain, monitor, and fine-tune the platforms as per each client’s requirements.
The company’s AWS data engineering and cloud development services are customized to suit the varying needs of businesses and align with industry standards and practices. Its data intelligence platform combines DevaOps (git), DataOps (delta lake), and ModelOps (MLflow) to seamlessly build, deploy, and manage several machine learning models. Databricks follows the recommended best practices to offer MLOps services and streamline the ML lifecycle.
Merkle helps the leading brands transform customer experience through its data science solutions. It uses machine learning, big data, and predictive analytics to drive success in digital enterprises and empower them to achieve their objectives.
The company’s range of data solutions, from strategic consulting to visual analytics, covers the necessary elements for a business to adapt to the changing market conditions and make data-driven decisions. It follows a solution-oriented approach to managing the AI and ML models and their lifecycles. Merkle uses the latest MLOps technologies to effectively automate and optimize the various stages of the machine learning lifecycle. It is a partner of Adobe, SAP, salesforce, and Sitecore.
Infocepts is a business transformation company powered by data and AI solutions. It provides data science and machine learning services to elevate data intelligence with precision and unlock business data’s transformative potential. The company’s tailored MLOps practices have been designed to address the unique challenges of each business and help them achieve sustained success in a dynamic digital environment.
Be it specific solutions or full-scale development and long-term maintenance, Infocepts offers a plethora of services to suit the clients’ requirements. It understands the importance of MLOps in supporting data scientists to develop and train powerful ML models to fulfill business expectations and derive the required outcomes.
Agilisium is a data and intelligence company offering data-driven innovative strategies to future-proof a business. It offers end-to-end automation and continuous delivery through DevOps as a service and MLOps solutions to help businesses cut costs and enhance efficiency. The company’s advanced data engineering and analytics services are designed to support businesses stand out from the crowd and gain a competitive edge in the market.
Agilisium understands the current business model of the clients and customizes its solutions to align with their needs and long-term objectives. From data migration to database modernization and automating workflows, the company improves operational efficiency and brings greater flexibility to business processes.
The MLOps process is complex and intricate but effective in increasing the stability and scalability of a model. It reduces risk and results in faster deployment of high-quality ML models. Several machine learning algorithms can be monitored and maintained with ease.
Take time to understand the importance of MLOps and how it can boost business efficiency at multiple levels. Choose the right MLOps company to become a long-term partner of the organization and achieve the business objectives. Gain a competitive edge and stay one step ahead of other businesses.
A data engineering company offering MLOps services will handle the end-to-end requirements of a business to build and manage advanced machine learning models. From risk reduction to better collaboration, acceleration of important processes, pipeline development, workflow automation, and continuous development, the company takes care of various processes in the backend. Enterprises will find it easier to make informed decisions by becoming a part of the global MLOps community.
Check out the below links to learn more about how machine learning and data engineering are beneficial for a business enterprise.
MLOps is in high demand as more businesses from around the world are adopting AI and advanced technologies. MLOps engineers play a crucial role in building, maintaining, and optimizing the machine learning solutions and work in tandem with other experts. An enterprise on its digital transformation journey will need MLOps, data engineering, cloud development, and AI services.
MLflow has been designed for data science teams to effectively manage the machine learning lifecycle. It provides various features that allow data scientists to work on different stages of the ML lifecycle and automate certain processes. Tracking, code version management, annotations, data visualization, etc., are some capabilities of MLflow. Leading companies like Oracle, Bosch, and Apple use MLflow in their processes.
DevOps deals with managing the code and configuration of an application, while MLOps also includes tracking and continuous improvement of the models. DevOps deals with regular software applications and programs. MLOps is specific to machine learning algorithms and models. Depending on the business requirements, MLOps might be a better option, especially when the enterprise uses advanced technologies. Existing DevOps practices can be migrated to MLOps for better results.
DSA stands for data structure and algorithms. Yes, DSA is required for MLOps. Though it is not directly used in machine learning, DSA helps create data structures to ingest data to machine learning algorithms and train the models. An ML engineer with DSA and DevOps knowledge will be an asset to an enterprise.
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