Your Data Science platform for MLOps & Data Science needs.

Let your Machine Learning models thrive on quality data with
Insightify’s Data Science Platform

Automated models deployment, monitoring and maintenance
Automated scalable resources for large-scale data processing and model training
Fast to implement & always fixed price

Why bother?

”The biggest hurdle to using AI is lack of mature data capabilities
Julie SweetAccenture’s CEO

Data Quality

Poor Data results in unreliable model predictions and undermines the overall effectiveness of the AI/ML solution

Scalability

Lack of scalability causes significant delays, system crashes, and inability to handle large datasets effectively

Collaboration

Inefficiency results in duplicated work, miscommunication, and challenges in integrating AI/ML solutions

Model Management

Inadequate deployment and monitoring cause real-world model failures due to lack of real-time tracking and updates

OUR SOLUTION

ML Development

Data Management

Feature Engineering

Complete implementation of automated feature engineering processes, ensuring consistency and repeatability across different datasets and projects.

Efficient handling of missing data, feature scaling, and transformation techniques to enhance model performance and reliability.

Continuous integration of domain knowledge and advanced techniques to generate meaningful and high-quality features, driving better model accuracy.

Experimentation & Model Evaluation

Fully automated experimentation pipeline that allows rapid testing and comparison of different model architectures and hyperparameters.

Implementation of robust evaluation metrics and validation techniques to ensure unbiased assessment of model performance.

Comprehensive tracking and documentation of experiments, providing clear insights and facilitating reproducibility and collaboration.

Model Training

Automated and scalable model training infrastructure capable of handling large datasets and complex algorithms without manual intervention.

Implementation of efficient resource management and parallel processing to minimize training time and maximize computational efficiency.

Continuous monitoring and optimization of training processes to ensure optimal model performance and adherence to project timelines.

Feature Store

Centralized repository for storing and managing features, ensuring consistency and reusability across different models and projects.

Implementation of robust access controls and versioning mechanisms to maintain data integrity and facilitate collaboration.

Seamless integration with data pipelines and machine learning workflows, enabling efficient feature retrieval and usage in real-time and batch processing.

Model Registry

Comprehensive system for registering and versioning trained models, ensuring traceability and reproducibility of model deployments.

Automated tracking of model metadata, including performance metrics, training data, and hyperparameters, for easy comparison and selection.

Advanced monitoring and alert systems are implemented, providing real-time information on system status, helping in quick identification and resolution of operational issues.

Implementation of access controls and approval workflows to ensure only validated and approved models are deployed to production.

Model Serving

Scalable and low-latency model serving infrastructure capable of handling high-throughput real-time predictions.

Seamless integration with application interfaces and data pipelines to deliver predictions efficiently and reliably.

Comprehensive monitoring and logging of serving infrastructure to ensure high availability and quick identification and resolution of issues.

Model Deployment

Comprehensive monitoring system to track model performance, data drift, and infrastructure health in real-time.

Implementation of detailed logging mechanisms to capture all relevant events and facilitate quick troubleshooting and root cause analysis.

Automated alerting and reporting systems to ensure timely detection and resolution of issues, minimizing impact on production operations.

Monitoring and Logging

Data visualization tools are fully integrated with analytical platforms, offering dynamic and interactive dashboards.

Visualizations are customized for different user groups, ensuring accessibility and usability for decision-makers at various organizational levels.

Implementation of privacy and security measures in presented data, ensuring that information is displayed only to authorized individuals.

Build yourself

vs

Insightify.io

TIME

~ 3 months to hire resources

TIME

= 6 months to go live

TEAM

Engineers either hired, allocated internally or mixed - usually no single pattern + complementary departments

Let your Machine Learning models thrive on quality data with Insightify’s Data Science Platform

Why bother?

Data Quality

Poor Data results in unreliable model predictions and undermines the overall effectiveness of the AI/ML solution

Scalability

Lack of scalability causes significant delays, system crashes, and inability to handle large datasets effectively

Collaboration

Inefficiency results in duplicated work, miscommunication, and challenges in integrating AI/ML solutions

Model Management

Inadequate deployment and monitoring cause real-world model failures due to lack of real-time tracking and updates

OUR SOLUTION

Complete implementation of automated feature engineering processes, ensuring consistency and repeatability across different datasets and projects.

Efficient handling of missing data, feature scaling, and transformation techniques to enhance model performance and reliability.

Continuous integration of domain knowledge and advanced techniques to generate meaningful and high-quality features, driving better model accuracy.

Fully automated experimentation pipeline that allows rapid testing and comparison of different model architectures and hyperparameters.

Implementation of robust evaluation metrics and validation techniques to ensure unbiased assessment of model performance.

Comprehensive tracking and documentation of experiments, providing clear insights and facilitating reproducibility and collaboration.

Automated and scalable model training infrastructure capable of handling large datasets and complex algorithms without manual intervention.

Implementation of efficient resource management and parallel processing to minimize training time and maximize computational efficiency.

Continuous monitoring and optimization of training processes to ensure optimal model performance and adherence to project timelines.

Centralized repository for storing and managing features, ensuring consistency and reusability across different models and projects.

Implementation of robust access controls and versioning mechanisms to maintain data integrity and facilitate collaboration.

Seamless integration with data pipelines and machine learning workflows, enabling efficient feature retrieval and usage in real-time and batch processing.

Comprehensive system for registering and versioning trained models, ensuring traceability and reproducibility of model deployments.

Automated tracking of model metadata, including performance metrics, training data, and hyperparameters, for easy comparison and selection.

Advanced monitoring and alert systems are implemented, providing real-time information on system status, helping in quick identification and resolution of operational issues.

Implementation of access controls and approval workflows to ensure only validated and approved models are deployed to production.

Scalable and low-latency model serving infrastructure capable of handling high-throughput real-time predictions.

Seamless integration with application interfaces and data pipelines to deliver predictions efficiently and reliably.

Comprehensive monitoring and logging of serving infrastructure to ensure high availability and quick identification and resolution of issues.

Comprehensive monitoring system to track model performance, data drift, and infrastructure health in real-time.

Implementation of detailed logging mechanisms to capture all relevant events and facilitate quick troubleshooting and root cause analysis.

Automated alerting and reporting systems to ensure timely detection and resolution of issues, minimizing impact on production operations.

Data visualization tools are fully integrated with analytical platforms, offering dynamic and interactive dashboards.

Visualizations are customized for different user groups, ensuring accessibility and usability for decision-makers at various organizational levels.

Implementation of privacy and security measures in presented data, ensuring that information is displayed only to authorized individuals.

Build yourself

vs

Insightify.io

TIME

~ 3 months to hire resources

TIME

= 6 months to go live

TEAM

Engineers either hired, allocated internally or mixed - usually no single pattern + complementary departments

TEAM

A team of Senior Engineers with a proven track record of delivering together

PRICE

Difficult to measure, due to many unpredictable factors

PRICE

Fixed price - you pay only for deliverabilities, not for filled in timesheets

Contact Us

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Let your Machine Learning models thrive on quality data with
Insightify’s Data Science Platform