This document discusses industrializing machine learning pipelines. It proposes a machine learning blueprint to improve collaboration between data scientists and engineers. The blueprint includes technical components like extracting, preprocessing, training, evaluating and predicting data. It defines roles for data scientists and engineers and recommends common tools and code to streamline workflows. The document also uses a case study of catalog categorization to illustrate how the blueprint was applied to classify videos using a machine learning pipeline with various technical components like TensorFlow, Apache Beam and BigQuery.
FlorenceAI: Reinventing Data Science at HumanaDatabricks
Humana strives to help the communities we serve and our individual members achieve their best health – no small task in the past year! We had the opportunity to rethink our existing operations and reimagine what a collaborative ML platform for hundreds of data scientists might look like. The primary goal of our ML Platform, named FlorenceAI, is to automate and accelerate the delivery lifecycle of data science solutions at scale. In this presentation, we will walk through an end-to-end example of how to build a model at scale on FlorenceAI and deploy it to production. Tools highlighted include Azure Databricks, MLFlow, AppInsights, and Azure Data Factory.
We will employ slides, notebooks and code snippets covering problem framing and design, initial feature selection, model design and experimentation, and a framework of centralized production code to streamline implementation. Hundreds of data scientists now use our feature store that has tens of thousands of features refreshed in daily and monthly cadences across several years of historical data. We already have dozens of models in production and also daily provide fresh insights for our Enterprise Clinical Operating Model. Each day, billions of rows of data are generated to give us timely information.
We already have examples of teams operating orders of magnitude faster and at a scale not within reach using fixed on-premise resources. Given rapid adoption from a dozen pilot users to over 100 MAU in the first 5 months, we will also share some anecodotes about key early wins created by the platform. We want FlorenceAI to enable Humana’s data scientists to focus their efforts where they add the most value so we can continue to deliver high-quality solutions that remain fresh, relevant and fair in an ever changing world.
MLTDD : USE OF MACHINE LEARNING TECHNIQUES FOR DIAGNOSIS OF THYROID GLAND DIS...cscpconf
Machine learning algorithms are used to diagnosis for many diseases after very important improvements of classification algorithms as well as having large data sets and high performing computational units. All of these increased the accuracy of these methods. The diagnosis of thyroid gland disorders is one of the application for important classification problem. This study majorly focuses on thyroid gland medical diseases caused by underactive or overactive thyroid glands. The dataset used for the study was taken from UCI repository. Classification of this thyroid disease dataset was a considerable task using decision tree algorithm. The overall
prediction accuracy is 100% for training and in range between 98.7% and 99.8% for testing. In this study, we developed the Machine Learning tool for Thyroid Disease Diagnosis (MLTDD), an Intelligent thyroid gland disease prediction tool in Python, which can effectively help to make the right decision, has been designed using PyDev, which is python IDE for Eclipse.
FlorenceAI: Reinventing Data Science at HumanaDatabricks
Humana strives to help the communities we serve and our individual members achieve their best health – no small task in the past year! We had the opportunity to rethink our existing operations and reimagine what a collaborative ML platform for hundreds of data scientists might look like. The primary goal of our ML Platform, named FlorenceAI, is to automate and accelerate the delivery lifecycle of data science solutions at scale. In this presentation, we will walk through an end-to-end example of how to build a model at scale on FlorenceAI and deploy it to production. Tools highlighted include Azure Databricks, MLFlow, AppInsights, and Azure Data Factory.
We will employ slides, notebooks and code snippets covering problem framing and design, initial feature selection, model design and experimentation, and a framework of centralized production code to streamline implementation. Hundreds of data scientists now use our feature store that has tens of thousands of features refreshed in daily and monthly cadences across several years of historical data. We already have dozens of models in production and also daily provide fresh insights for our Enterprise Clinical Operating Model. Each day, billions of rows of data are generated to give us timely information.
We already have examples of teams operating orders of magnitude faster and at a scale not within reach using fixed on-premise resources. Given rapid adoption from a dozen pilot users to over 100 MAU in the first 5 months, we will also share some anecodotes about key early wins created by the platform. We want FlorenceAI to enable Humana’s data scientists to focus their efforts where they add the most value so we can continue to deliver high-quality solutions that remain fresh, relevant and fair in an ever changing world.
MLTDD : USE OF MACHINE LEARNING TECHNIQUES FOR DIAGNOSIS OF THYROID GLAND DIS...cscpconf
Machine learning algorithms are used to diagnosis for many diseases after very important improvements of classification algorithms as well as having large data sets and high performing computational units. All of these increased the accuracy of these methods. The diagnosis of thyroid gland disorders is one of the application for important classification problem. This study majorly focuses on thyroid gland medical diseases caused by underactive or overactive thyroid glands. The dataset used for the study was taken from UCI repository. Classification of this thyroid disease dataset was a considerable task using decision tree algorithm. The overall
prediction accuracy is 100% for training and in range between 98.7% and 99.8% for testing. In this study, we developed the Machine Learning tool for Thyroid Disease Diagnosis (MLTDD), an Intelligent thyroid gland disease prediction tool in Python, which can effectively help to make the right decision, has been designed using PyDev, which is python IDE for Eclipse.
From an idea to production: building a recommender for BBC SoundsTatiana Al-Chueyr
This presentation was given on the 28th of September 2021 at the first MLOps London Meetup
Event website: https://www.meetup.com/mlopslondon/events/280295841/
Agile Chennai 2021 | Achieving High DevOps Maturity through Platform Engineer...AgileNetwork
Agile Chennai 2021
Achieving High DevOps Maturity through Platform Engineering Practices - by Satish Chandran
Director, DevOps and IT Security, Gain Credit
Seattle Cassandra Users: An OSS Java Abstraction Layer for CassandraJosh Turner
Project Casquatch is a database abstraction layer with code generation designed to streamline Cassandra development. Out of the box it comes pre-tuned with high available policies including load balancing, geo-redundancy, connection pooling, etc., sitting on top of the DataStax driver using native APIs. All of this is abstracted behind the ever prevalent POJO. Instead of writing CQL, we utilize generic programming that allows you to simply pass a generated POJO to a save() method or populate with a getById(). This is the same code reportedly used by T-Mobile for multiple national platforms including the activation of the Apple Watch and Galaxy Watch, T-Mobile Payments, Digits, and many others.
As presented at Seattle Cassandra Users Group on June . 26th, 2019.
Tutorial Expert How-To - Command Line Interface (CLI)PascalDesmarets1
Promote a shared understanding of meaning and context for data structures across business users and technical users, through the synchronization and publication of these data structures to business-facing data catalogs.
This presentation was made on June 9th, 2020.
Video recording of the session can be viewed here: https://youtu.be/OCB9sTUnUug
In this meetup with Sanyam Bhutani, Machine Learning Engineer at H2O.ai, he gives a recap of the eight annual ICLR (International Conference on Learning Representations) 2020 - a niche deep learning conference whose focus is to study how to learn representations of data, which is basically what deep learning does.
Sanyam goes through a few of his favorite selected papers from this year’s ICLR, note this session may not be able to capture the richness of all papers or allow a detailed discussion.
You will be able to find Sanyam in our community slack (https://www.h2o.ai/slack-community/), please feel free to start a discussion with him, if you send a emoji greeting, you’ll find the answers.
Following are the papers we will look into:
U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
Your classifier is secretly an energy based model and you should treat it like one
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Reformer: The Efficient Transformer
Generative Models for Effective ML on Private, Decentralized Datasets
Once for All: Train One Network and Specialize it for Efficient Deployment
Thieves on Sesame Street! Model Extraction of BERT-based APIs
Plug and Play Language Models: A Simple Approach to Controlled Text Generation
BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning
Real or Not Real, that is the Question
Boston Data Engineering: Kedro Python Framework for Data Science: Overview an...Boston Data Engineering
What is Kedro?
Kedro is an open-source Python framework for creating reproducible, maintainable and modular data science code. It borrows concepts from software engineering best practices and applies them to machine-learning code; applied concepts include modularity, separation of concerns and versioning.
Kedro 2-minute Intro Video: https://youtu.be/KEdmJ2ADy_M
Kedro Docs: https://kedro.readthedocs.io
Kedro GitHub repo: https://github.com/quantumblacklabs/kedro
Meetup: https://www.meetup.com/f7324858-b804-4ed8-ba45-580c262189f1/events/280986950/
Velocity NY 2018 "The Cloud Native Developer Workflow"Daniel Bryant
In a productive cloud-native development workflow, individual teams can build and ship software independently from each other. But with a rapidly evolving Cloud Native Landscape, creating an effective developer workflow on Kubernetes can be challenging. We are all creating software to support the delivery of value to our customers and to the business, and therefore, the developer experience from idea generation to running (and observing) in production must be fast, reliable, and provide good feedback.
Production-Ready Kubernetes: It's Not About TechnologyAntoine Craske
Adopting cloud-native technologies can seem like a technological challenge, but it's far from being the only theme.
The organization, culture and people aspects is the main challenge to overcome resistance to change, drive value creation and accelerate the transformation.
This talk shares our journey in adopting a Cloud-native application stack with a Kubernetes PaaS, focusing on the transformation journey.
Blending Supersonic, Subatomic Java with deep learning to perform object detection. Sounds interesting? Because it is! Then watch this session to learn how to create a microservice combining TensorFlow and Quarkus together into one executable using GraalVM native image, JNI, and Protobuf. With this, we detect objects in photos by returning labels, bounding boxes, and confidence scores. Additionally, we will touch on Open Data Hub, an AI/ML solution for OpenShift.
TensorFlow meetup: Keras - Pytorch - TensorFlow.jsStijn Decubber
Slides from the TensorFlow meetup hosted on October 9th at the ML6 offices in Ghent. Join our Meetup group for updates and future sessions: https://www.meetup.com/TensorFlow-Belgium/
(Costless) Software Abstractions for Parallel ArchitecturesJoel Falcou
Performing large, intensive or non-trivial computing on array like data structures is one of the most common task in scientific computing, video game development and other fields. This matter of fact is backed up by the large number of tools, languages and libraries to perform such tasks. If we restrict ourselves to C++ based solutions, more than a dozen such libraries exists from BLAS/LAPACK C++ binding to template meta-programming based Blitz++ or Eigen. If all of these libraries provide good performance or good abstraction, none of them seems to fit the need of so many different user types.
Moreover, as parallel system complexity grows, the need to maintain all those components quickly become unwieldy. This talk explores various software design techniques - like Generative Programming, MetaProgramming and Generic Programming - and their application to the implementation of a parallel computing librariy in such a way that:
- abstraction and expressiveness are maximized - cost over efficiency is minimized
We'll skim over various applications and see how they can benefit from such tools. We will conclude by discussing what lessons were learnt from this kind of implementation and how those lessons can translate into new directions for the language itself.
Software Engineering as the Next Level Up from Programming (Oracle Groundbrea...Lucas Jellema
Software engineering is programming with the added dimension of time: programs that can evolve and scale, be maintained and be operated by multiple people over a longer period of time. What does it take to do software engineering in a professional manner - beyond mere programming? As programmers, our main goal is to make IT work. To translate functional specification into executable code. And sure, that is the least we can do. But we have more responsibility than this. We have to produce software that is robust and will reliably handle expected and unexpected cases. Software that is scalable and can handle expected and somewhat unexpected load gracefully. With minimal operating costs and in the greenest way possible. Software that is observable and manageable and that can be evolved with changing and new functional requirements and with changing technology. Software that will be legacy in the original, positive meaning of the word. That does not depend on the one big brain in our team or on the guy that has been around for three decades. Software that we know is good and can comfortably be modified in a controlled and productive way. We have to grow from excellent programmers to professional software engineers. This session talks about what it takes to create our code with honor. It discusses automation at every level in the build, rollout and monitoring of infrastructure (as code), platform and application, using CI/CD pipelines and DevOps procedures and tools. The session talks about testing – before and during development as well as after each change anywhere in the system and for both functional and non-functional aspects. Test driven development, regression testing and smoke testing are among the concepts discussed. The term ‘clean code’ refers to code that is readable, testable and maintainable. Through code analysis and peer reviews and by performing refactoring we constantly refine our software to be collectively adaptable. The session demonstrates the concepts discussed with code samples in the context of cloud native programming. As software engineers, we have an obligation to society, to our peers and to ourselves to not only write software that does the job, but to create code that is good. Ours is a great and meaningful line of work, especially if we raise our game professionally to code with honor.
Docs as Code: Publishing Processes for API ExperiencesAnne Gentle
When you treat docs like code, you multiply everyone’s efforts and streamline processes through collaboration, automation, and plain old hard work. To create a cohesive API experience, developers, technical marketing engineers, technical writers, and product managers can work together on GitHub to produce web pages and API documentation, including interactive API docs and tutorials. The ways you can leverage developer processes and tools in a docs-as-code system vary widely. Let's walk through some examples including tools, version control, publishing workflows, approvals, source formats, checklists, automated testing, and final approval. Also, let's take some time to share some of the pitfalls and difficulties possible when you work on API and tools documentation for a large and varied product catalog with more than a thousand contributors.
Content Strategy and Developer Engagement for DevPortalsAxway
Slides from Write the Docs Ottawa Meet Up at Shopify HQ in Canada, June 24, 2019
We’ll walk through 5 scenarios and concrete ways of reaching a developer community for frictionless and increased engagement.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
From an idea to production: building a recommender for BBC SoundsTatiana Al-Chueyr
This presentation was given on the 28th of September 2021 at the first MLOps London Meetup
Event website: https://www.meetup.com/mlopslondon/events/280295841/
Agile Chennai 2021 | Achieving High DevOps Maturity through Platform Engineer...AgileNetwork
Agile Chennai 2021
Achieving High DevOps Maturity through Platform Engineering Practices - by Satish Chandran
Director, DevOps and IT Security, Gain Credit
Seattle Cassandra Users: An OSS Java Abstraction Layer for CassandraJosh Turner
Project Casquatch is a database abstraction layer with code generation designed to streamline Cassandra development. Out of the box it comes pre-tuned with high available policies including load balancing, geo-redundancy, connection pooling, etc., sitting on top of the DataStax driver using native APIs. All of this is abstracted behind the ever prevalent POJO. Instead of writing CQL, we utilize generic programming that allows you to simply pass a generated POJO to a save() method or populate with a getById(). This is the same code reportedly used by T-Mobile for multiple national platforms including the activation of the Apple Watch and Galaxy Watch, T-Mobile Payments, Digits, and many others.
As presented at Seattle Cassandra Users Group on June . 26th, 2019.
Tutorial Expert How-To - Command Line Interface (CLI)PascalDesmarets1
Promote a shared understanding of meaning and context for data structures across business users and technical users, through the synchronization and publication of these data structures to business-facing data catalogs.
This presentation was made on June 9th, 2020.
Video recording of the session can be viewed here: https://youtu.be/OCB9sTUnUug
In this meetup with Sanyam Bhutani, Machine Learning Engineer at H2O.ai, he gives a recap of the eight annual ICLR (International Conference on Learning Representations) 2020 - a niche deep learning conference whose focus is to study how to learn representations of data, which is basically what deep learning does.
Sanyam goes through a few of his favorite selected papers from this year’s ICLR, note this session may not be able to capture the richness of all papers or allow a detailed discussion.
You will be able to find Sanyam in our community slack (https://www.h2o.ai/slack-community/), please feel free to start a discussion with him, if you send a emoji greeting, you’ll find the answers.
Following are the papers we will look into:
U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
Your classifier is secretly an energy based model and you should treat it like one
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Reformer: The Efficient Transformer
Generative Models for Effective ML on Private, Decentralized Datasets
Once for All: Train One Network and Specialize it for Efficient Deployment
Thieves on Sesame Street! Model Extraction of BERT-based APIs
Plug and Play Language Models: A Simple Approach to Controlled Text Generation
BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning
Real or Not Real, that is the Question
Boston Data Engineering: Kedro Python Framework for Data Science: Overview an...Boston Data Engineering
What is Kedro?
Kedro is an open-source Python framework for creating reproducible, maintainable and modular data science code. It borrows concepts from software engineering best practices and applies them to machine-learning code; applied concepts include modularity, separation of concerns and versioning.
Kedro 2-minute Intro Video: https://youtu.be/KEdmJ2ADy_M
Kedro Docs: https://kedro.readthedocs.io
Kedro GitHub repo: https://github.com/quantumblacklabs/kedro
Meetup: https://www.meetup.com/f7324858-b804-4ed8-ba45-580c262189f1/events/280986950/
Velocity NY 2018 "The Cloud Native Developer Workflow"Daniel Bryant
In a productive cloud-native development workflow, individual teams can build and ship software independently from each other. But with a rapidly evolving Cloud Native Landscape, creating an effective developer workflow on Kubernetes can be challenging. We are all creating software to support the delivery of value to our customers and to the business, and therefore, the developer experience from idea generation to running (and observing) in production must be fast, reliable, and provide good feedback.
Production-Ready Kubernetes: It's Not About TechnologyAntoine Craske
Adopting cloud-native technologies can seem like a technological challenge, but it's far from being the only theme.
The organization, culture and people aspects is the main challenge to overcome resistance to change, drive value creation and accelerate the transformation.
This talk shares our journey in adopting a Cloud-native application stack with a Kubernetes PaaS, focusing on the transformation journey.
Blending Supersonic, Subatomic Java with deep learning to perform object detection. Sounds interesting? Because it is! Then watch this session to learn how to create a microservice combining TensorFlow and Quarkus together into one executable using GraalVM native image, JNI, and Protobuf. With this, we detect objects in photos by returning labels, bounding boxes, and confidence scores. Additionally, we will touch on Open Data Hub, an AI/ML solution for OpenShift.
TensorFlow meetup: Keras - Pytorch - TensorFlow.jsStijn Decubber
Slides from the TensorFlow meetup hosted on October 9th at the ML6 offices in Ghent. Join our Meetup group for updates and future sessions: https://www.meetup.com/TensorFlow-Belgium/
(Costless) Software Abstractions for Parallel ArchitecturesJoel Falcou
Performing large, intensive or non-trivial computing on array like data structures is one of the most common task in scientific computing, video game development and other fields. This matter of fact is backed up by the large number of tools, languages and libraries to perform such tasks. If we restrict ourselves to C++ based solutions, more than a dozen such libraries exists from BLAS/LAPACK C++ binding to template meta-programming based Blitz++ or Eigen. If all of these libraries provide good performance or good abstraction, none of them seems to fit the need of so many different user types.
Moreover, as parallel system complexity grows, the need to maintain all those components quickly become unwieldy. This talk explores various software design techniques - like Generative Programming, MetaProgramming and Generic Programming - and their application to the implementation of a parallel computing librariy in such a way that:
- abstraction and expressiveness are maximized - cost over efficiency is minimized
We'll skim over various applications and see how they can benefit from such tools. We will conclude by discussing what lessons were learnt from this kind of implementation and how those lessons can translate into new directions for the language itself.
Software Engineering as the Next Level Up from Programming (Oracle Groundbrea...Lucas Jellema
Software engineering is programming with the added dimension of time: programs that can evolve and scale, be maintained and be operated by multiple people over a longer period of time. What does it take to do software engineering in a professional manner - beyond mere programming? As programmers, our main goal is to make IT work. To translate functional specification into executable code. And sure, that is the least we can do. But we have more responsibility than this. We have to produce software that is robust and will reliably handle expected and unexpected cases. Software that is scalable and can handle expected and somewhat unexpected load gracefully. With minimal operating costs and in the greenest way possible. Software that is observable and manageable and that can be evolved with changing and new functional requirements and with changing technology. Software that will be legacy in the original, positive meaning of the word. That does not depend on the one big brain in our team or on the guy that has been around for three decades. Software that we know is good and can comfortably be modified in a controlled and productive way. We have to grow from excellent programmers to professional software engineers. This session talks about what it takes to create our code with honor. It discusses automation at every level in the build, rollout and monitoring of infrastructure (as code), platform and application, using CI/CD pipelines and DevOps procedures and tools. The session talks about testing – before and during development as well as after each change anywhere in the system and for both functional and non-functional aspects. Test driven development, regression testing and smoke testing are among the concepts discussed. The term ‘clean code’ refers to code that is readable, testable and maintainable. Through code analysis and peer reviews and by performing refactoring we constantly refine our software to be collectively adaptable. The session demonstrates the concepts discussed with code samples in the context of cloud native programming. As software engineers, we have an obligation to society, to our peers and to ourselves to not only write software that does the job, but to create code that is good. Ours is a great and meaningful line of work, especially if we raise our game professionally to code with honor.
Docs as Code: Publishing Processes for API ExperiencesAnne Gentle
When you treat docs like code, you multiply everyone’s efforts and streamline processes through collaboration, automation, and plain old hard work. To create a cohesive API experience, developers, technical marketing engineers, technical writers, and product managers can work together on GitHub to produce web pages and API documentation, including interactive API docs and tutorials. The ways you can leverage developer processes and tools in a docs-as-code system vary widely. Let's walk through some examples including tools, version control, publishing workflows, approvals, source formats, checklists, automated testing, and final approval. Also, let's take some time to share some of the pitfalls and difficulties possible when you work on API and tools documentation for a large and varied product catalog with more than a thousand contributors.
Content Strategy and Developer Engagement for DevPortalsAxway
Slides from Write the Docs Ottawa Meet Up at Shopify HQ in Canada, June 24, 2019
We’ll walk through 5 scenarios and concrete ways of reaching a developer community for frictionless and increased engagement.
Similar to Industrializing Machine learning pipelines (20)
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
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TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
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Issues we tackled so far
Onboarding of new engineers
Merging DS and DE work
Knowledges transfer
between features teams
Architecture inconsistency
Introduction
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Talk
overview
1. Machine learning blue print
1.1 Technical components
1.2 Functional usages
1.3 Collaboration data engineer and data
scientist
2. Example : catalog categorization
2.1 Goal
2.2 What data available?
2.3 Machine learning pipeline
Introduction
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Technical
components
Extract Fetch data from data lake
Preprocess Build features
Train Machine learning core
Evaluate Technical metrics / Business KPIs
Predict Score elements
Machine learning blue print
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Functional usages
Do you need to score a new element ?
Serving : extract, preprocess and predict
Should we release the new model?
Evaluation : evaluate old model vs evaluate new model
Can we include this new trend in our model ?
Training : extract , preprocess and train
Machine learning blue print
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Functional
usages
Training:
Update the model with fresh data.
Serving:
Serve predicton in batch or in real time.
Evaluation:
Evaluate model success, interpretability, monitoring.
Machine learning blue print
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Collaboration
Data engineer &
Data scientist
Who does what and how ?
Roles boundaries
Efficient collaboration
Technologies
Common tools
Machine learning blue print
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Why docker ?
• Interface between algo and airflow
• Allow to decorrelate functional ML
code from scheduling
• Handle different python versions
• We use kubernetes to compute our
jobs
• Prod and dev are identical, for DS and
for DE
Machine learning blue print
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Machine learning blue print
Better collaboration Clear roles expectations
Common framework Architecture consistency
Machine learning blue print
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Data scientists can focus on science, are more autonomous
and can bring prod ready code.
Data engineers can focus on scalability, re-usability and
architecture consistency.
This blue print allow efficient release in production
Machine learning blue print
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Serving
JsonRPC : external API
GRPC : internal API
We can have multiple
internal API, with CPU or
GPU without issue, as the
external API will hide it.
Catalog categorization
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Training preprocess:
• Remove html tags (lib BeautifulSoup)
• Tokenize (lib polyglot)
• BagOfWord fit: create vocabulary
• BagOfWord transform: apply vocabulary
Serving preprocess:
• Remove html tags (lib BeautifulSoup)
• Tokenize (lib polyglot)
• BagOfWord fit: create vocabulary
• BagOfWord transform: apply vocabulary
Be sure the code is common in training and serving
Avoiding code duplication
Catalog categorization
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Results
Training updated every week
(wikidata frequency)
API
Serving realtime
à No functional logic duplicated between both
Catalog categorization
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Results
Project went smoothly
Tasks ownership were clear
Less iteration when interfacing
DS and DE work
Some implementation will be directly
reusable in new projects
Conclusion
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Key takeaways
What we had (tech stack):
• We improved our DS/DE collaboration
• We take more time to do a technical
design
• We contribute to a common library used
by all the feature teams
• We have defined a common set of
technologies to used
Conclusion
What we did (methodology):
• Data in one place (BigQuery) &
accessible via SQL
• Containerized application (Docker) to
be use in multiple places
• Environment: dev, stage and prod
• Apache Beam is executed on Dataflow,
no infrastructure to handle
