Streamline Your AI Journey with MLOps
Spearheaded by industry expert David Pridmore, we offer customised solutions that integrate and streamline large-scale operations, driving increased visibility, optimisation, and improved decision-making.
We believe that the key to successful MLOps lies in fostering a strong collaboration between data scientists, ML engineers, and operations teams. Our services are designed to nurture this collaborative environment, empowering your teams to innovate, integrate, and excel in the MLOps landscape
MLOps Development
Deployment & Monitoring Services
MLOps Consulting & Strategy
Support
Services & Solutions
MLOps
Development
Projects stalling in development? MLOps for Development bridges the gap, streamlining the process to get your models deployed faster and performing better.
Faster Time-to-Value
No more waiting for models to gather dust. See results from your AI initiatives sooner.
Improved ROI
Streamlined deployment means your models deliver value quicker, maximising your return on investment.
Unleashed Potential
Focus on innovation, not development hurdles. MLOps empowers you to unlock the true power of your AI.
MLOps Development Services
Our MLOps development services encompass the entire machine learning lifecycle, from ideation to production, including:
ML Product and Platform Development:
We help you build production-quality reinforcement learning products and platforms tailored to your unique business needs.
Version Control & Experiment Tracking:
We select the right tools and workflows to manage different versions of your models, track experiments, and compare results (e.g., Git integration, MLflow).
Data Versioning & Lineage Management:
Our solutions ensure data used for training is tracked and versioned alongside the models, allowing for easier debugging and reproducibility.
Automated Feature Engineering & Data Pipelines:
We develop pipelines to automate feature engineering tasks and data preparation, ensuring consistency and efficiency.
Model Training Infrastructure Management:
We leverage tools and resources to manage the infrastructure needed for training models, including cloud integration and containerization (e.g., Docker, Kubernetes).
Continuous Integration/Continuous Delivery (CI/CD) for ML:
We implement CI/CD pipelines specifically designed for machine learning models, automating testing, deployment, and rollback processes.
MLOps Services for
Deployment & Monitoring
Ensure your model performs well in the real world! We don't just deploy models; we empower your team with the expertise and tools to achieve sustained AI success.
ROI
See real-world results from your AI initiatives sooner, maximising your return on investment (ROI).
Optimised Model Performance
Continuous monitoring and proactive optimisation ensure your models perform at their best, delivering accurate and reliable results.
Reduced Operational Costs
Automated workflows and proactive issue detection minimise wasted resources and downtime.
Data-Driven Decision Making
Gain valuable insights from your models to make informed decisions that propel your business forward.
Future-Proof Your AI
Our scalable infrastructure ensures your models can handle growing data volumes and evolving business needs.
MLOps Services for Deployment & Monitoring
Developed a groundbreaking AI model? Now what? Don't let deployment roadblocks or performance lags hold you back. Perception IT's MLOps Services for Deployment & Monitoring bridge the gap, ensuring a smooth transition from development to real-world impact.
Model Development Pipeline:
We deploy your models across powerful CPU and GPU machines without managing the underlying infrastructure. Our team provides robust operational support, creating a solid ML infrastructure that aligns with your business objectives.
API Development and Integration:
Our MLOps services include the development and integration of APIs for your machine learning models. This allows for seamless interaction between your models and other software components, enhancing the utility and accessibility of your ML solutions.
Model Monitoring and Management:
We continuously monitor model-performance metrics, detect data drift, and trigger retraining to improve model performance. We also track model version history and lineage for auditability.
Automation and CI/CD Integration:
Our services optimise model training and deployment pipelines, build for continuous integration/continuous delivery (CI/CD) to facilitate retraining, and easily fit machine learning into your existing release processes.
A/B Testing & Feature Flags:
Offer tools for A/B testing different model versions and managing feature flags for controlled rollouts of new models.
Explainable AI (XAI):
Integrate XAI tools to help understand the decision-making process of your models and improve model interpretability.
Automated Model Retraining & Rollback:
Develop automated workflows to retrain models with new data and rollback to previous versions if performance issues arise.
MLOps Consulting
& Strategy
Achieve sustained AI success. Perception IT MLOps Consulting & Strategy services equips you with the tools, knowledge, and strategy to unlock the true potential of your AI investments, leading to greater efficiency, cost savings, and a competitive advantage.
Cost Savings
Identify the right tools and infrastructure for your unique needs, avoiding unnecessary technology overhead.
Boost team collaboration
A well-defined MLOps strategy facilitates better communication and collaboration between data scientists, engineers, and other teams involved in the AI development process.
Differentiate Your Business
Delivering accurate and reliable results from your AI initiatives allows you to stand out from the crowd.
Capture New Market Opportunities
Utilise data-driven insights to make informed decisions and seise new market opportunities.
Future-Proof Your AI Strategy
We plan for growth, ensuring your MLOps practices can adapt and scale with your evolving business needs.
MLOps Consulting & Strategy
Assessment and Roadmap Development
Current State Assessment:
Evaluate your existing development and deployment processes to identify strengths, weaknesses, and opportunities for improvement. This may involve analysing tools, workflows, team structures, and infrastructure.
Business Goals Alignment:
Ensure your MLOps strategy aligns with your overall business objectives. This involves understanding your desired outcomes from AI initiatives and tailoring the MLOps approach to achieve them.
MLOps Roadmap Creation:
Develop a comprehensive roadmap that outlines the steps required to implement a successful MLOps strategy. This roadmap should consider factors like technology adoption, team training, and cultural changes.
Technology Selection and Implementation
MLOps Tool Selection:
Help you identify and implement the right MLOps tools based on your specific needs and infrastructure. This could include tools for version control, CI/CD pipelines, model monitoring, and explainability.
Cloud Integration Strategy:
Develop a strategy for integrating your MLOps practices with your existing cloud infrastructure. This ensures scalability, security, and efficient resource utilization.
Data Management Strategy:
Design a data management strategy that facilitates efficient data flow throughout the MLOps lifecycle. This includes data versioning, lineage tracking, and ensuring data quality.
Interoperability Services
Strong Compatibility:
We ensure that your MLOps solutions are compatible with a wide range of technologies and platforms. Whether you’re using MLflow, Azure Machine Learning, or other tools, we can help you build flexible and secure end-to-end machine learning workflows.
Governance and Scalability Planning
MLOps Governance Framework:
Establish a governance framework that ensures responsible AI practices, model explainability, and regulatory compliance.
Security and Access Controls:
Implement robust security measures to protect your models, data, and infrastructure from unauthorised access.
Scalability Planning:
Plan for future growth and ensure your MLOps infrastructure can handle increasing data volumes and model complexity.
Additional Services
Proof-of-Concept (POC) Development:
Not sure about your strategy? We can Develop and deploy a small-scale MLOps implementation to demonstrate the value and feasibility of your chosen approach.
Change Management and Training:
Provide training and support for your team to ensure they understand and can effectively use MLOps practices.
Ongoing Support:
We offer ongoing support to help you refine your MLOps strategy, troubleshoot issues, and adapt to evolving technologies and business needs.
Support
Perception IT understands that a successful MLOps strategy goes beyond initial deployment. To ensure your AI models continue to deliver value, we offer comprehensive MLOps Support Services
Maximise Model Performance
Continuous monitoring and optimisation ensures your models operate at their best, delivering accurate and reliable results.
Minimise Downtime and Costs
Proactive issue identification and management minimises downtime and associated costs.
Enhanced Data Governance and Security
Robust data management and security practices build trust and mitigate potential risks.
Future-Proofed AI Initiatives
Ongoing support and knowledge sharing ensure your MLOps practices can adapt to evolving technologies and business needs.
MLOps Support Services
Ongoing Monitoring and Optimisation
Model Performance Monitoring:
Our MLOps services focus on monitoring the performance of your models in production, specifically identifying potential issues such as data drift, accuracy degradation, or concept drift.
Alerting and Root Cause Analysis:
If issues arise, we'll promptly notify you and conduct a thorough root cause analysis to identify the source of the problem.
Proactive Model Tuning:
Based on monitoring data, we can recommend and implement adjustments to optimise your models for continued peak performance.
Data Management and Governance
Data Quality Assurance:
We help ensure the quality and consistency of data used by your models, mitigating bias and maintaining model accuracy.
Data Lineage Tracking:
We track the origin and transformation of data throughout the MLOps lifecycle, promoting transparency and facilitating regulatory compliance.
Version Control and Rollback Support:
We maintain a clear version history of your models and data, allowing for easy rollbacks or adjustments if necessary.
Infrastructure Management and Security
Scalability Management:
We monitor your MLOps infrastructure and proactively scale resources to accommodate growing data volumes or model complexity.
Security Patch Management:
We ensure your MLOps infrastructure is up-to-date with the latest security patches, protecting your models and data from vulnerabilities.
Disaster Recovery Planning:
We help develop and test disaster recovery plans to minimise downtime and ensure your AI operations can recover quickly from unexpected events.
Model Explainability and Governance
Explainable AI (XAI) Techniques:
We can implement XAI techniques to explain your model's decision-making process, fostering trust and transparency in your AI initiatives.
Regulatory Compliance Support:
We can assist you in navigating relevant regulations and ensuring your MLOps practices adhere to ethical and legal considerations.
Ongoing Support and Training
Technical Support:
Our MLOps experts are available to answer your questions, troubleshoot issues, and provide ongoing technical support.
Team Training and Knowledge Sharing:
We offer training programs to help your team stay up-to-date on the latest MLOps practices and tools.
Best Practice Sharing:
We keep you informed about emerging trends and best practices in the MLOps landscape, ensuring you stay ahead of the curve.
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