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3.2 What data will be processed by the service provider on behalf of the financial institution? 3.3 How do we seek to ad
dependencies, and lessons learned. Identify open questions, action items, and recommended next steps. Deliverables. â¢
Feb 13, 2018 - Paypal: Rapidly innovating and quickly developing and scaling new apps. SaaS. Marketo: Migrating from on-
Deploy a scalable private cloud ... OSP Director. Assign deployment roles. Finalize the operational cloud. 1. 3. 5. 2. 4
Identity Management. Services. Manage the security of and access to cloud assets, supported by Google's own protection o
Cloud Sprint is an intensive hands-on workshop that accelerates a customer's application migration to Google. Cloud Plat
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Build an initial plan for deployment and migration activities given customer's requirements and timelines. As part of th
Cloud Discover: Security helps customers understand security controls and considerations in Google Cloud. Platform (GCP)
Create an inventory of potential workloads targeted for migration to Google Cloud. Document key characteristics of these
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Cloud Deploy: Machine Learning for Minimum Viable Model (MVM) helps customers develop an initial machine learning model
PROFESSIONAL SERVICES
Cloud Deploy: Machine Learning for Minimum Viable Model (MVM) Cloud Deploy: Machine Learning for Minimum Viable Model (MVM) helps customers develop an initial machine learning model running on TensorFlow and Google Cloud Platform to demonstrate proof-of-concept modeling of a specific business use case. Google will guide customers on the iterative process for developing an initial model that demonstrates the feasibility of a solution addressing a business use case.
Key Activities
Deliverables
Data Exploration Guidance Analyze available data sources to assess state of data and potential usefulness in applying in an ML model.
• Preliminary machine learning model through an iterative process
• Analyze data characteristics
Scope and Pricing
• Assess data quality, cleanliness, potential correlation, and patterns
• Up to 32 FTE days engagement (on-site or offsite at Google’s discretion) within a two-month period
• Check for class imbalance • Validate hypothesis relative to data
Algorithm Selection Research modeling strategies to determine appropriate ML selection algorithm to address business problem. • Research existing strategies and whitepapers • Select known algorithms based on hypothesis, type of features, patterns in data • Document decisions related to algorithm usage
Feature Engineering Create ML model features based on raw data analysis and tests. • Use domain knowledge to identify potential features • Advise on transformation of raw data into feature recommendations • Recommend new features, and remove redundant/duplicate features and highly correlated features
• Additional monthly units of service may be required, depending on customer’s business use case or level of data quality • Out of scope examples: building a complete data pipeline, deploying the model into production, converting the model into an API • Pricing will be agreed upon by customer and Google and specified in the applicable Ordering Document
Initial Model Development Develop an initial ML model using the data to solve the business problem, and iterate. • Define the right modeling strategy and choose the right ML algorithms
• Evaluate and visualize model result
• Select data set for training, test set, and validation
• Recommend corrective actions to improve model
• Develop initial model to prove conceptual potential
• Iterate and improve model results
• Determine duration and amount of data for initial experiment
