Your partner to establish MLOPs across your enterprise
We are ex-Amazon Machine Learning builders with deep expertise in Machine Learning, Computer Vision, and Natural Language Processing
Challenges: Building MLOps Platform
Data Quality and Availability
Ensuring high-quality data that is both available and reliable is a significant challenge. Many ML projects fail due to poor data quality.
Data Privacy and Compliance
Enterprises often deal with sensitive data. Complying with data privacy regulations (e.g., GDPR) while still utilizing data for ML can be complex.
Model Governance
Managing and governing ML models throughout their lifecycle, including versioning, tracking changes, and ensuring compliance, is challenging.
Infrastructure Scalability
As models become more complex, they require more computational resources. Scalability and cost management can be issues, especially when moving from development to production.
Model Interpretability and Explainability
Understanding why a model makes certain predictions is crucial, especially in regulated industries. Ensuring models are interpretable is a challenge.
Talent and Skill Gap
There's often a shortage of talent with expertise in both ML and DevOps. Bridging this gap through training and hiring can be challenging.
Integration with Existing Systems
Integrating ML into existing software and systems can be complex, requiring significant changes to workflows and processes.
Model Deployment and Serving
Deploying ML models in production at scale and ensuring low-latency serving is challenging.
Continuous Monitoring and Feedback Loop
Building a feedback loop to continuously monitor model performance and retrain models is essential but complex.
Tooling and Technology Stack
Selecting the right tools and technologies for MLOps can be challenging, especially given the rapidly evolving landscape.
Change Management
Cultural and organizational resistance to change can be a significant hurdle. MLOps often requires a shift in mindset and workflows.
Cost Management
ML operations can become expensive, and managing costs while ensuring performance can be tricky.
Security
Ensuring the security of models, data, and the infrastructure they run on is crucial but challenging.
Documentation and Collaboration
Effective documentation and collaboration among cross-functional teams (data scientists, engineers, DevOps) are vital but sometimes overlooked.
Regulatory Challenges
Effective documentation and collaboration among cross-functional teams (data scientists, engineers, DevOps) are vital but sometimes overlooked.Industries such as healthcare and finance have stringent regulations that impact how models are developed and deployed.
Data Quality and Availability
Ensuring high-quality data that is both available and reliable is a significant challenge. Many ML projects fail due to poor data quality.
Data Privacy and Compliance
Enterprises often deal with sensitive data. Complying with data privacy regulations (e.g., GDPR) while still utilizing data for ML can be complex.
Model Governance
Managing and governing ML models throughout their lifecycle, including versioning, tracking changes, and ensuring compliance, is challenging.
Infrastructure Scalability
As models become more complex, they require more computational resources. Scalability and cost management can be issues, especially when moving from development to production.
Model Interpretability and Explainability
Understanding why a model makes certain predictions is crucial, especially in regulated industries. Ensuring models are interpretable is a challenge.
Talent and Skill Gap
There's often a shortage of talent with expertise in both ML and DevOps. Bridging this gap through training and hiring can be challenging.
Integration with Existing Systems
Integrating ML into existing software and systems can be complex, requiring significant changes to workflows and processes.
Model Deployment and Serving
Deploying ML models in production at scale and ensuring low-latency serving is challenging.
Continuous Monitoring and Feedback Loop
Building a feedback loop to continuously monitor model performance and retrain models is essential but complex.
Tooling and Technology Stack
Selecting the right tools and technologies for MLOps can be challenging, especially given the rapidly evolving landscape.
Change Management
Cultural and organizational resistance to change can be a significant hurdle. MLOps often requires a shift in mindset and workflows.
Cost Management
ML operations can become expensive, and managing costs while ensuring performance can be tricky.
Security
Ensuring the security of models, data, and the infrastructure they run on is crucial but challenging.
Documentation and Collaboration
Effective documentation and collaboration among cross-functional teams (data scientists, engineers, DevOps) are vital but sometimes overlooked.
Regulatory Challenges
Effective documentation and collaboration among cross-functional teams (data scientists, engineers, DevOps) are vital but sometimes overlooked.Industries such as healthcare and finance have stringent regulations that impact how models are developed and deployed.
Our Services
MLOps Foundations
MLOps Training
In-depth courses covering MLOps principles, tools, and best practices.
Hands-on labs for version control using Git, model tracking with tools like MLflow, and CI/CD pipeline setups.
Training modules on automating model deployment and monitoring in production.
Learning to address common MLOps challenges and ensuring model reliability.
Data Science Training
Customized training programs for data scientists, data engineers, and analysts.
Workshops on data preprocessing, feature selection, and advanced analytics techniques.
Hands-on experience with popular data science libraries like pandas, scikit-learn, and TensorFlow.
Training in building end-to-end machine learning pipelines from data collection to model deployment.
Workshops and Game Days
Interactive workshops focusing on real-world MLOps scenarios.
Game days simulating MLOps incidents and testing incident response plans.
Machine Learning Hackathons for talent acquisition.
Building cool demo to win for conferences and exhibitions.
Customer Stories
Geo-comm
Challenge
The customer gathers large amounts of digital maps as PDFs, images or Lidar scans and has to use human effort to convert them to digital maps to recommend exit strategies.
Solution
We used Amazon SageMaker to develop a multi-step pipeline to label the data and finetuned Yolo, Custom CNN, and Segment Anything model for object detection and segmentation to solve for edge detection, door detection and room detection problems. The output was converted into GeoJson format to be loaded into ArcGIS Pro for further analysis.
Benefits
The SageMaker MLOps inference pipeline generated digital maps and speed up the map creation process by 70%, resulting in savings.
Largest Insurance in EMEA
Challenge
Processes over 10M+ claims annually and receives many image scans via email and fax in various formats and image quality. They needed a labeling platform to fix OCR errors and improve AI algorithms.
Solution
Karini AI developed an OCR labeling workflow powered by Amazon Textract APIs to detect OCR text, key values, and tables. The labeling workflow integration with SageMaker training and serving provided the human-in-the-loop workflow to improve the model quality continuously.
Benefits
The platform was enabled across 100s of users and estimated to improve the document understanding process by 20%.
Truvian Sciences
Challenge
Truvian Sciences needed an artificial intelligence system to classify blood diseases using hematology images accurately, The system needed continuous learning technique to find out failure scenarios(False Negatives, False Positives). Getting a labeled dataset verified by medical professionals was expensive and needed massive efficiencies.
Solution
Karini AI developed an AI platform using AWS Services to provide easy-to-use bulk classification workflow but built-in dynamic consensus, dataset management to track and understand failure scenarios, and MLOps pipeline using Amazon SageMaker.
Benefits
Continuous learning improved model quality to 85%+ accuracy. Efficient bulk classification workflow was able to save 40% on labeling costs by medical professionals.
Geo-comm
Challenge
The customer gathers large amounts of digital maps as PDFs, images or Lidar scans and has to use human effort to convert them to digital maps to recommend exit strategies.
Solution
We used Amazon SageMaker to develop a multi-step pipeline to label the data and finetuned Yolo, Custom CNN, and Segment Anything model for object detection and segmentation to solve for edge detection, door detection and room detection problems. The output was converted into GeoJson format to be loaded into ArcGIS Pro for further analysis.
Benefits
The SageMaker MLOps inference pipeline generated digital maps and speed up the map creation process by 70%, resulting in savings.
Largest Insurance in EMEA
Challenge
Processes over 10M+ claims annually and receives many image scans via email and fax in various formats and image quality. They needed a labeling platform to fix OCR errors and improve AI algorithms.
Solution
Karini AI developed an OCR labeling workflow powered by Amazon Textract APIs to detect OCR text, key values, and tables. The labeling workflow integration with SageMaker training and serving provided the human-in-the-loop workflow to improve the model quality continuously.
Benefits
The platform was enabled across 100s of users and estimated to improve the document understanding process by 20%.
Truvian Sciences
Challenge
Truvian Sciences needed an artificial intelligence system to classify blood diseases using hematology images accurately, The system needed continuous learning technique to find out failure scenarios(False Negatives, False Positives). Getting a labeled dataset verified by medical professionals was expensive and needed massive efficiencies.
Solution
Karini AI developed an AI platform using AWS Services to provide easy-to-use bulk classification workflow but built-in dynamic consensus, dataset management to track and understand failure scenarios, and MLOps pipeline using Amazon SageMaker.
Benefits
Continuous learning improved model quality to 85%+ accuracy. Efficient bulk classification workflow was able to save 40% on labeling costs by medical professionals.
Our Expertise and Partners
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