Impetus LeapLogic’s automated path to Amazon SageMaker

Impetus Technologies

By: Sujit Singh, Senior Partner Solutions Architect – AWS
By Dayananda Swamy VT, Partner Solutions Architect – AWS
By Ashish Dahiya, Principal Architect, Impetus Technologies
By Arpan Bhandari, Technical Architect, Impetus Technologies
By Gurvinder Arora, Product Marketing, Impetus Technologies

Legacy on premises machine-learning (ML) environments are often constrained by fixed infrastructure, fragmented tooling, and manual deployment processes. As data volumes grow and models become more complex, these limitations slow experimentation, delay production rollouts, and increase operational overhead.

By migrating these workloads to Amazon SageMaker, enterprises can standardize model development, automate training and deployment, and scale ML workloads on demand without managing underlying infrastructure. SageMaker provides a managed foundation for experimentation, versioning, monitoring, and production grade deployment across the ML lifecycle.

Traditional migration approaches often require months of manual refactoring, custom scripting, and repeated validation cycles, introducing delivery risk and slowing business outcomes. Impetus LeapLogic helps reduce this complexity by automating a significant portion of the migration to SageMaker, minimizing manual effort and accelerating time to production.

LeapLogic, Impetus’s automation led modernization system, helps enterprises transition legacy data and ML workloads into cloud-centric architectures on Amazon Web Services (AWS). Backed by deep experience in data engineering, analytics, and ML modernization, Impetus guides customers through migration from legacy environments to production-ready SageMaker deployments.

Using automated analysis, code and pipeline transformation, and structured validation, LeapLogic enables enterprises to modernize faster while maintaining accuracy, continuity, and operational stability.With Impetus LeapLogic, enterprises can:

• Standardize ML development and deployment on SageMaker
• Automate large portions of legacy pipeline and workload transformation
• Reduce manual refactoring and migration risk
• Coexist with existing AWS services while improving ROI on current investments
• Move ML workloads to production with greater consistency and governance

This blog post describes how SageMaker and Impetus LeapLogic enable a structured, lower risk modernization of legacy workloads on AWS.

AI operationalization with SageMaker: Scaling ML from development to production

SageMaker streamlines the ML journey by combining automation, scalability, and governance into a single managed environment. From intelligent data prep and model selection to distributed training, automatic tuning, collaborative notebooks, and governed model cataloging, SageMaker helps trim the operational clutter and accelerate teams toward production with fast, monitored, versioned deployments and built-in model explainability.

An integrated experience with SageMaker Unified Studio

SageMaker supports the enterprise data management lifecycle, including data ingestion, scalable ML development, secure model deployment, and advanced analytics, enabling continuous improvement from a single, fully managed interface.Let’s see how these services operate cohesively and offer an integrated environment.

Figure 1: SageMaker Unified Studio with complete AWS stack

The architecture shown in the preceding diagram illustrates how enterprise data flows through a unified SageMaker environment, beginning with diverse real-time and batch sources entering AWS through services such as Amazon Kinesis, Amazon Managed Streaming for Apache Kafka (Amazon MSK), and AWS IoT Core. This raw stream is shaped and refined through AWS Glue, AWS Glue Studio, and AWS Glue DataBrew before landing in the curated zones that support analytics engines such as Amazon Redshift, Amazon EMR, Amazon OpenSearch Service, Amazon Quick Sight, and Amazon Athena. Amazon SageMaker Unified Studio, the integrated development environment within SageMaker, is used to orchestrate feature engineering, experimentation, training, and deployment within a single ML workbench, while also drawing on surrounding AI services for added intelligence. The result is a cohesive environment where data movement, transformation, modeling, and insight generation operate as an integrated workflow, giving teams a governed, scalable, and production ready path from ingestion to applied machine learning.

Real world enterprise use cases

SageMaker excels as a centralized interface and integrated environment for data warehousing, extract, transform, and load (ETL) processes, BI reporting, orchestration, advanced analytics, generative AI, automated machine learning (AutoML), explainability, and model monitoring.By seamlessly integrating with a variety of AWS services, SageMaker unlocks powerful use cases, such as the following, that help drive business value, accelerate AI adoption, and optimize operational efficiency.

• Automated data preparation, feature engineering, and predictive modeling
• Unified metadata management
• Interactive ML-driven BI
• Data access controls, usage, monitoring, anomaly detection, and compliance with regulations

Impetus LeapLogic™ – Elevate intelligence

LeapLogic is Impetus’s automation system designed to modernize complex legacy data, analytics, and ML workloads into cloud native AWS architectures. It supports modernization across data warehouses, ETL systems, orchestration tools, analytics layers, and ML pipelines, enabling enterprises to transition to SageMaker with reduced manual effort and predictable outcomes.Trusted by several Fortune 500 enterprises, LeapLogic simplifies data system modernization with a four-step paradigm:

1. Workload assessment offers automated workload analysis with actionable recommendations
2. Business logic transformation translates legacy logic into cloud native vernacular
3. Validation and reconciliation facilitates seamless business continuity
4. Operationalization launches your cloud native future.

Beyond LeapLogic, Impetus, as a premier data and AI system integrator, plays a vital role in rapidly developing enterprise class generative AI solutions. With their generative AI Innovation Labs—the industry’s first strategy, design, and build collaborative service offering—enterprises can create a production ready generative AI prototype in less than six weeks.

LeapLogic-powered fully automated stack mapping

Let’s see how LeapLogic with its new AI enabled conversion capabilities, along with Impetus’s transformative services, helps enable automated transformation of legacy tools and technologies to AI-ready SageMaker and its supporting stack or alternative options such as PySpark. The following shows the usage categories and the LeapLogic process to map to AWS services.

Data sources and storage

• Automates schema, metadata, and data migration into AWS services such as Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Amazon DynamoDB, and Amazon Relational Database Service (Amazon RDS) by parsing legacy structures and generating equivalent cloud native storage models
• Creates AWS Lake Formation blueprints by converting legacy governance rules and access patterns into reusable templates
• Generates validated, optimized AWS workloads by profiling legacy pipelines and producing performance tuned equivalents

Workload modernization

• Translates ETL and extract, load, and transform (ELT) logic, jobs, and orchestration scripts into assets compatible with AWS Glue, Amazon EMR, or Amazon Redshift using automated semantic mapping of transformations
• Reconstructs business logic by interpreting legacy operators, functions, and procedures and generating their AWS service counterparts
• Performs end-to-end functional and row level validation using AWS Glue and Amazon EMR jobs to facilitate accuracy and readiness for production
• Auto provisions the appropriate AWS compute and orchestration services needed to implement in production

Compute and infrastructure

• Maps on-premises execution patterns to cloud compute constructs (such as Amazon Elastic Compute Cloud (Amazon EC2), AWS Lambda, AWS Fargate, or AWS Batch) by analyzing concurrency, scheduling, and resource needs
• Generates infrastructure as code (IaC) deployment templates that automate provisioning, scaling, and operationalization of migrated workloads

Analytics and visualization

• Converts Tableau, Cognos, OBIEE, SAS, and Alterx assets into analytical views backed by Amazon Quick Sight, Athena, and Lake Formation by mapping visual and semantic layers
• Recreates dashboards and workflows using AWS datasets, facilitating functional parity and improved performance
• Suggests conversational analytics enhancements using Amazon Q where applicable
• Provisions Amazon CloudWatch dashboards and alerts to monitor analytical workloads

Security, identity, and compliance

• Translates existing access policies into AWS Identity and Access Management (IAM) and Lake Formation roles, permissions, and fine-grained access controls
• Modernizes identity management by mapping legacy authentication models to identity flows compatible with IAM or Amazon Cognito
• Configures SageMaker built-in encryption, key management (AWS Key Management Service (AWS KMS) or AWS CloudHSM), audit trails, and compliance templates aligned to regulatory standards

AI and ML services

• Establishes an enterprise generative AI foundation through Impetus’s Strategy, Design, and Build Labs that map business use cases to working prototypes in weeks
• Integrates Amazon Bedrock models by generating prompt flows, orchestration logic, and evaluation frameworks
• Automates provisioning of Amazon SageMaker AI components, training pipelines, and inference endpoints needed for scalable AI adoption

Internet of Things (IoT)

• Configures AWS IoT Core, AWS IoT Greengrass, and AWS IoT FreeRTOS integrations by mapping legacy sensor flows to real-time ingestion pipelines
• Builds edge-to-cloud pathways that help transform streaming IoT data into actionable insights using AWS analytics and ML services
• Facilitates SageMaker-ready IoT data pipelines for training, inference, and continuous optimization

Security and compliance

Customers implementing LeapLogic must follow AWS security best practices and the AWS Well-Architected Framework Security Pillar. Deployments should adhere to the principle of least privilege for IAM roles, implement encryption for data at rest and in transit, and use AWS security services such as AWS IAM Access Analyzer to validate generated policies. LeapLogic operates within customer AWS account boundaries, and customers retain full responsibility for their security configuration and compliance requirements under the AWS Shared Responsibility Model.

Conclusion

Modernizing legacy workloads to Amazon SageMaker can be a strategic step for enterprises seeking to scale ML adoption while reducing operational complexity. SageMaker provides a managed foundation for consistent training, deployment, and monitoring of ML models across the enterprise.With Impetus LeapLogic, organizations can approach this transformation with greater confidence. By automating key aspects of workload analysis, transformation, and validation, Impetus helps reduce migration risk and shorten time to value enabling enterprises to operationalize ML on AWS in a controlled, production ready manner.

As an AWS Premier Tier Services Partner with certified Machine Learning and Data & Analytics competencies, Impetus helps enterprises modernize and operationalize ML workloads on SageMaker with speed, accuracy, and operational discipline.

To learn more about how Impetus LeapLogic supports automated modernization of legacy workloads on AWS, contact Impetus.

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Impetus Technologies – AWS Partner Spotlight

Impetus Technologies is an AWS Premier Tier Services Partner who enables the Intelligent Enterprise™ with innovative data engineering, cloud, and enterprise AI services. By helping enterprises modernize workloads and leverage cutting-edge AWS technologies, Impetus empowers businesses to innovate, streamline operations, and unlock new opportunities.

Contact Impetus Technologies | Partner Overview | AWS Marketplace