MLOps services for your business
Machine learning operations (MLOps) is a set of best practices for the creation, deployment, maintenance, and management of machine learning solutions. By leveraging MLOps, data science teams can streamline their machine learning workflows and deliver models to production faster and more reliably, ensuring greater scalability at the same time.
These practices can reduce the time and costs associated with building machine learning solutions while improving the accuracy and efficiency of their models, ultimately helping you gain a business advantage.
Machine learning initiatives that support your goal
Benefits of MLOps
Improved scalability of data science efforts
The application of MLOps best practices results in improved communication and closer collaboration among data engineers, data scientists and DevOps engineers and enables you to reliably scale your data science initiatives.
Streamlined research & development
Seamless access to the data they require allows your data scientists to run more experiments, thus increasing the likelihood of successfully solving your business problems. With the ability to validate business cases quickly, you can productionise concepts smoothly and accelerate return on investment.
Reduced re-work and cost of data preparation
Reusable frameworks for data preparation, centralised feature stores, and ML pipeline templates reduce project costs and let you avoid repetitive development tasks.
Adaptation to the changing business environment
By constantly monitoring your model’s accuracy, retraining it whenever needed, and incorporating A/B testing with multiple real-time models, you ensure continuous optimisation and the best possible performance of your solution.
Reduced risk of faulty results
Gain stronger trust in your ML initiatives by evaluating your models on the right datasets. Implement repeatable processes, enable the explainability of your ML solutions, and protect your data with robust security and compliance measures.
What you’ll gain with comprehensive MLOps services
Operational automation of machine learning
Objectivity’s MLOps services are aimed at applying best practices to streamline the development, deployment, management, and monitoring of machine learning models. With the MLOps approach, you’ll be able to set up new ML models faster and improve the quality of final solutions. Additional elements of MLOps, such as real-time monitoring of production solutions, can tell you if your models are still providing up-to-date predictions or require retraining.
With us, you'll choose the best infrastructure for your ML environment (cloud, on-premises, or open source), ensure proper integration and easy access to your data, and provide your data scientists with the tools they need. The final model will be automatically deployed to production with the right level of governance and monitoring and the cooperation among data engineering, data science, and infrastructure teams incorporating CI/CD processes will be seamless across all those domains.
Whether you're looking into evaluating and conceptualising MLOps or building tailored, end-to-end MLOps platforms, our specialists are ready to support you.
- Scalable solutions for data acquisition layers to simplify integration management.
- Reusable feature stores, ML pipeline templates, and CI/CD capabilities for smooth deployments.
- Automated and unified ML processes that serve as a foundation for efficient experimenting and a shorter path to production with business value delivered with a low engineering effort.
Advantages of working with an experienced partner
By leveraging our experience from various fields of development — data science, data engineering, software development, cloud, and infrastructure, we can deliver comprehensive solutions that address various challenges of the machine learning delivery process.
With our vast expertise in developing and setting up cloud-based solutions for various industries, you can be sure that your ML initiatives will have scalable, reliable, and cost-effective foundations.
We offer end-to-end MLOps services to give you the most control and comfort. Starting from consulting and advisory services, through analysis and process design, technical architecture, and development, all the way to hypercare and long-term support.
The solutions we deliver are always tailored to specific business needs and existing IT infrastructure. With our individual, consultative approach and experience in delivering complex IT projects, you’ll be able to efficiently implement your ML initiatives.
Introducing a new MLOps practice is a change management process. Therefore, we offer training and upskilling services to equip all team members with the necessary knowledge to achieve the highest efficiency while working on an MLOps platform.
- Define your goals, and how they fit into your data strategy
- Describe your current ML operational lifecycle
- Assess your environment against an MLOps maturity model
- List the tools you are using
- Determine your capabilities in data science
- Design an end-to-end ML lifecycle process together with your team
- Recommend technologies which fit your needs
- Recognise additional roles that the process requires
- Identify specific actions to lead you to a higher MLOps maturity
- Set up the infrastructure
- Build an MLOps platform:
- Data acquisition layer
- Feature store
- MLOps pipelines
- Models registry and serving services,
- Models monitoring
- DevOps infrastructure
- Augment with additional data roles if needed
- Handover the MLOps platform
- Train and upskill your data team
- Move to hypercare and maintenance
The IRIS PoC proved that a state-of-the-art neural network system is capable of processing difficult data sets, which the traditional approach wasn’t able to handle. Additionally, having implemented the PoC on Azure showed that hosting the application on the cloud makes it significantly faster and increases user capacity without affecting functionality.
Data & AI Guild Master
By implementing our MLOps framework, we can help your organisation scale and productionise its machine learning efforts to make sure they deliver tangible business value.