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Data Reply MLOps Assessment Framework evaluates Skills, Processes, Tools & Technology deployed in the ML Lifecycle against the AWS MLOps best practices and the MLOPs Reference Architecture.
As companies gain more experience and business value from AI and ML, the adoption of AI and ML is gathering pace that leads to new challenges.
The key ones are around the ability to deploy quality models into production at high velocity, to identify the right time to retrain the models and to enable teams with the right mix of skills and tools to work efficiently to ensure that the cost of ML capability is affordable to the business and delivers the right level of ROI.
Companies that see machine learning as strategic are developing their MLOps capability - a set principles, practices, processes and technologies to streamline and automate the ML workflow based on DevOps discipline, to enable the use of AI ML at scale.
In Omdia's comparative review of enterprise MLOps platforms, AWS is recognized as “the outright leader”.
Data Reply, works with customers to help them with ML foundations, its productionisation and operationalisation utilising Amazon SageMaker, other Amazon technologies that provide the most comprehensive set of components, tools and automated services for ML.
Data Reply is a premier AWS partner, offering a broad range of advanced analytics, AI ML and data processing services. Data Reply is an AWS Launch Partner in the MLOps competency and part of the AWS ‘Well Architected programme’. Data Reply MLOps Assessment Framework evaluates customers’ Skills, Processes, Tools & Technology deployed in the ML Lifecycle against the AWS MLOps best practices and the MLOPs Reference Architecture.
- Target MLOps Solution Architecture;
- Documented Recommendations around Target Operation Model based on ‘best practice’ and aligned with business goals and priorities;
- MLOps High level Implementation Roadmap and inputs into a Business Case.
Separately, Data Reply can also assist with the delivery of AI ML projects by augmenting team with the right mix of skills to accelerate time to value and enable knowledge transfer on the project.
Explore a curated selection of MLOps case studies that exemplify the transformative power of machine learning operations in various industries. These examples highlight the strategic implementation of MLOps to drive innovation, enhance operational efficiency, and deliver measurable business outcomes. Each case study provides insights into the challenges faced, solutions implemented, and the tangible benefits realized, offering valuable lessons for organizations looking to leverage MLOps for competitive advantage.
Indicia Worldwide helps global brands improve their customers’ experience using data, machine learning, and AI to fine-tune marketing campaigns, improve retention, and drive new customer growth. It turned to Data Reply to help evolve its data science platform to a more robust, scalable, and efficient solution on AWS. This move cut the time spent building, testing, and deploying new data and machine learning models from months to weeks.
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