Wherever your may find yourself in your journey to machine learning operationalization,
ParallelM can help you get there. Whether you are already running pipelines in production or
are preparing for your first deployment, ParallelM can help you create the right ML operations
infrastructure and shorten your path to Machine Learning success.
Solutions by Role.
Machine learning presents new challenges in production that are not addressed by current IT operations tools. Unlike traditional web and mobile applications, ML technology is characterized by uncertainty, non-reproducibility, heterogeneity of operating environments, and highly frequent change. Operations need solutions and strategies to enable a new practice – machine learning operationalization.
MLOps™ empowers IT operations teams to take charge of machine learning applications in production with confidence without having to know the details of how the models work. Operations teams can focus on making sure that machine learning applications are delivering the intended business value, while allowing for easy communication with data scientists when deeper diagnostics are required.
Monitor ML Health
Monitor ML Health of live machine learning based business processes: The MLOps Center dashboard displays the status of your ML production apps at glance, providing statistics about prediction rates, anomalies, alert conditions, and latest model configurations.
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Curated Diagnostics
Curated diagnostics help operations personnel assess an issue, capture a trace, and share with a data scientist for further diagnostics. In the meantime, models can be quickly rolled back to the last known, working version to ensure ML continuity while awaiting problem resolution
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Sandboxing in production is a safe and efficient way to move a data scientists new model into production. After validating on live data, operations can use MLOps to quickly move new models into production while sharing monitoring views with data scientists. As both teams gain confidence, the influence of the new model can be increased over time.
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Codify Machine Learning
Management Tasks
Codify machine learning management tasks into scalable, automated workflows. MLOps offers step-by-step wizards for packaging your pipelines into a virtual container – the ION – that abstracts away underlying physical infrastructure. Data scientists can prepare their pipelines for delivery and then handoff to IT operations teams to complete deployment all within the same system.
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Snapshots provide data scientists with a complete trace of all the symptoms surrounding a particular issue as it happened. Ops teams can quickly hand over information that is immediately actionable for troubleshooting and resolution.
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Deep Analysis & Diagnostics
Deep analysis and diagnostics workbench. Data scientists can draw from a rich set of charts and analysis tools with which to conduct detailed investigation into ML behavior.
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Business Analysts
Business analysts gain visibility into the performance of ML initiatives and can configure their own dashboard and statistical views. Track prediction rates and usage. Identify which models are providing value and what are the latest developments running in the sandbox prior to deployment. Resource analysis helps manage and optimize cloud spending budgets and make more informed decisions regarding allocation of computing costs. Communicate with both IT operations and data scientists about ML activity and make sure that the line of business perspective is properly represented throughout the process.
Solutions by Industry.
ParrallelM is applied across industries where machine learning is gaining traction.
Financial Institutions deploy machine learning with confidence knowing that ParallelM helps them mitigate the risk of poor decisions in robo-trading applications or advanced credit scoring.

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Fully reliant on making data-driven decisions to optimize value for their

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Often operate in highly distributed environments that require rapid synthesis of data to enable predictive maintenance.

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