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Slides from the "Cloud Science" talk at Scale by the Bay, November 2017.
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Assignment help
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To define a measurement of coverage for SQL SELECT queries in database loaded with test data and to present an algorithm that automates the calculation of coverage.
Using an SQL coverage measurement for testing database application
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Our team at Comcast is challenged with operationalizing predictive ML models to improve customer experience. Our goal is to eliminate bottlenecks in the process from model inception to deployment and monitoring. Traditionally CI/CD manages code and infrastructure artifacts like container definitions. We want to extend it to support granular traceability enabling tracking of ML Models from use-case, to feature/attribute selection, development of versioned datasets, model training code, model evaluation artifacts, model prediction deployment containers, and sinks to which the predictions/outcomes are persisted to. Our framework stack enables us to track models from use-case to deployments, manage and evaluate multiple models simultaneously in the live yet dark mode and continue to monitor models in production against real-world outcomes using configurable policies. The technologies/components which drive this vision are: 1. FeatureStore – Enables data scientists to reuse versioned features and review feature metrics by models. Self-Service capabilities allow all teams to onboard their events data into the feature store. 2. ModelRepository – Manages meta-data about models including pre-processing parameters (Ex. Scaling parameters for features), mapping to the features needed to execute the model, model discovery mechanisms, etc. 3. Spark on Alluxio – Alluxio provides the universal data plane on top of various under-stores (Ex. S3, HDFS, RDBMS). Apache Spark with its Data Sources API provides a unified query language which Data Scientist use to consume features to create training/validation/test datasets which are versioned and integrated into the full model pipeline using Ground-Context discussed next. 4. Ground-Context – This open-source vendor-neutral data context service enables full traceability from use-case, models, features, model to features mapping, versioned datasets, model training codebase, model deployment containers and prediction/outcome sinks. It integrates with the Feature-Store, Container Repository and Git to integrate data, code and run-time artifacts for CI/CD integration.
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These are the slides from my presentation on Running R in the Database using Oracle R Enterprise. The second half of the presentation is a live demo of using the Oracle R Enterprise. Unfortunately the demo is not listed in these slides
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Machine learning is being deployed in a growing number of applications which demand real-time, accurate, and robust predictions under heavy query load. However, most machine learning frameworks and systems only address model training and not deployment. In this talk, we present Clipper, a general-purpose low-latency prediction serving system. Interposing between end-user applications and a wide range of machine learning frameworks, Clipper introduces a modular architecture to simplify model deployment across frameworks. Furthermore, by introducing caching, batching, and adaptive model selection techniques, Clipper reduces prediction latency and improves prediction throughput, accuracy, and robustness without modifying the underlying machine learning frameworks. We evaluated Clipper on four common machine learning benchmark datasets and demonstrate its ability to meet the latency, accuracy, and throughput demands of online serving applications. We also compared Clipper to the Tensorflow Serving system and demonstrate comparable prediction throughput and latency on a range of models while enabling new functionality, improved accuracy, and robustness.
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Three things you will take away from the session: • How to run an effective tenant-to-tenant migration • Best practices for before, during, and after migration • Tips for using migration as a springboard to prepare for Copilot in Microsoft 365 Main ideas: Migration Overview: The presentation covers the current reality of cross-tenant migrations, the triggers, phases, best practices, and benefits of a successful tenant migration Considerations: When considering a migration, it is important to consider the migration scope, performance, customization, flexibility, user-friendly interface, automation, monitoring, support, training, scalability, data integrity, data security, cost, and licensing structure Next Wave: The next wave of change includes the launch of Copilot, which requires businesses to be prepared for upcoming changes related to Copilot and the cloud, and to consolidate data and tighten governance ShareGate: ShareGate can help with pre-migration analysis, configurable migration tool, and automated, end-user driven collaborative governance
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
sammart93
This presentations targets students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many developer tools, platforms & APIs? This comprehensive yet still high-level overview outlines the most impactful tools for where to run your code, store & analyze your data. It will also inspire you as to what's possible. This talk is 50 minutes in length.
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
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How to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
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Axa Assurance Maroc - Insurer Innovation Award 2024
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I've been in the field of "Cyber Security" in its many incarnations for about 25 years. In that time I've learned some lessons, some the hard way. Here are my slides presented at BSides New Orleans in April 2024.
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
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TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
How to convert PDF to text with Nanonets
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Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
JSRender
1.
L
H TM ND ER T A ND IP RE S CR JS A G AV IN J US ITH WRAKE D
2.
WHY TEMPLATES?
3.
JSRENDER – JQUERY
VERSION //Define template //Client
4.
RENDERING A TEMPLATE
REQUIRES THREE ASPECTS • Template • Data • Container
5.
JSRENDER – PURE
JAVASCRIPT
6.
RENDERING TEMPLATES -
1
7.
RENDERING TEMPLATES -
2
8.
RENDERING TEMPLATES -
3
9.
JSRENDER FUNDAMENTALS
10.
EXAMPLE
11.
DRILLING INTO HIERARCHICAL
DATA
12.
CONDITIONALS, EXPRESSIONS AND
OPERATORS
13.
USING COUNTERS AND
COMPLEX CONDITIONAL EXPRESSIONS
14.
ITERATE MULTIPLE ARRAYS
TOGETHER
15.
NESTED TEMPLATES
16.
REFERENCE Article http://msdn.microsoft.com/en-us/magazine/hh882454.aspx Github Project https://github.com/BorisMoore/jsrender
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