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Data Science & MLOps

Accelerate and streamline the development workflows for Data Scientists, addressing the gap left by missing critical quality, security and delivery capabilities.


Data Scientists are experts at data cleaning, transformations, analysis, modeling, and more. However, they are often not experts at version control, software dependency management, software packaging, reliability engineering, security, performance profiling/optimization, or other software engineering tasks. These capabilities critical and pillars of modern software delivery. Data science workflows are not exempt from software development challenges, and given the state of the overall ecosystem, they are even more susceptible than ever to the risks these capabilities are built to address.

Bringing an engineering-first mindset to data science teams ensures that ML solutions are not only statistically valid but also robust, scalable, and maintainable in a production environment.

Our MLOps Services

We provide best-of-breed standards and practices that are crucial for developing and maintaining reliable, efficient, and secure ML systems, improving the delivery of Data Science and Machine Learning (ML) services.

Streamlining Model Development Lifecycle

Implement a structured and efficient model development lifecycle that reduces the amount of manual intervention required at each stage. This includes standardizing processes for model design, testing, validation, and deployment.

Automating the CI/CD Pipeline

Enhance the Continuous Integration and Continuous Deployment (CI/CD) pipeline specifically for ML workflows. This means automating the integration of new code, model training, testing, and deployment processes, reducing manual coding and intervention.

Utilizing Modern MLOps Practices

Adopt MLOps (Machine Learning Operations) principles to automate and streamline the ML production process. MLOps focuses on automating the ML pipeline and integrating it with existing DevOps practices to improve efficiency and collaboration.

Implementing Automated Testing & Quality Checks

Develop automated testing frameworks for ML models that include performance metrics, data quality checks, and model behavior validation to ensure the reliability and robustness of the models without manual oversight.

Utilizing Data Science Orchestration Tools

Leverage orchestration tools to manage complex workflows involving multiple ML models and processes. These tools can help automate the sequencing, execution, and monitoring of different tasks, reducing manual coordination efforts.

Improving Version Control Practices for Models & Processes

Implement robust version control mechanisms not just for code, but also for models, datasets, and experiment results. This practice helps in tracking changes, ensuring reproducibility, and reducing errors caused by manual tracking.

Optimizing Resource Allocation

Use tools and practices that automatically manage and optimize the allocation of computational resources. This includes auto-scaling of resources based on workload demands in the ML development and deployment process.

Automating Repetitive Operations Tasks

Foster a culture of automating repetitive tasks and sharing reusable code or templates among team members. This approach reduces the need to write new code for common tasks, thus decreasing manual effort.

Our Other Core Capabilities

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Enterprise Technology Modernization

Accelerate business results and scale your organization with a lean, value-driven approach to software delivery and IT operations.
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Cloud Native Delivery

Empower your teams to build scalable apps in dynamic environments and make high-impact changes frequently and predictably with little toil.
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Modern Platform Engineering

Reliable applications are built on modern self-service platforms that reduce engineering friction.
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Speed of delivery while always staying safe and secure in an automated way. Remove untimely, manual, last gate siloed approvals and validations.
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