1. The document discusses architecting data science platforms for a dating product using an event-driven architecture that stores all data as a stream of events.
2. Key aspects of the architecture include an event history repository that stores real-time event streams, a Solr search index for querying events, and using the event stream for both online and offline machine learning.
3. The architecture aims to enable fast experimentation cycles by using the same code and data for production, development, and training machine learning models.
Real-time Recommendations for Retail: Architecture, Algorithms, and DesignJuliet Hougland
Users are constantly searching for new content and to stay competitive organizations must act immediately based on up-to-date data. Outdated recommendations decrease the likelihood of presenting the right offer and make it harder to maintain customer loyalty. In order to provide the most relevant recommendations and increase engagement, organizations must track customer interactions and re-score recommendations on the fly.
Data sources have expanded dramatically to include a wealth of historical data and a constant influx of behavior data. The key to moving from predictive models, applied in batch, to models that provide responses in real time, is to focus on the efficiency of model application. The speed that recommendations can be served is influenced by:
Architecture of the recommendation serving platform
Choice of recommendation algorithm
Datastore access patterns
In this presentation, we’ll discuss how developers can use open source components like HBase and Kiji to develop low-latency recommendation models that can be easily deployed by e-commerce companies. We will give practical advice on how to choose models and design data stores that make use of the architecture and quickly serve new recommendations.
These are slides presented at MLconf in San Francisco, November 14, 2014. I share the approach to real-time machine learning for recommender systems developed at if(we). We achieve rapid iterative cycles by adhering to a strict approach to structuring and accessing our data, as well as to building the online features that comprise our models. These developments support teams of data scientist and data engineers, who work together to solve complex recommendation problems. We also introduce the Antelope Realtime Events framework, an open source demonstration application which derives from our scalable proprietary software stack.
Machine Learning system architecture – Microsoft Translator, a Case Study : ...Vishal Chowdhary
Microsoft Translator currently supports 100+ languages. We constantly improve the translation quality, add new scenarios, all with a constant team size. This session describes a production scale machine learning architecture using MS Translator as a case study. You will learn the mental model to approach your ML problem and concrete Do’s and Don’ts for the various components of the ML system architecture.
ML-Ops: From Proof-of-Concept to Production ApplicationHunter Carlisle
Successfully deploying a working machine learning prototype to a production application is a challenging task, frought with difficulties not experienced in traditional software deployments.
In this talk, you will learn techniques to successfully deploy ML applications in a scalable, maintainable, and automated way.
How to design your ML application to be production ready from the day one
How to switch from notebooks to deployable and maintainable software
How to deploy, serve and monitor prediction pipelines
How to re-train models in production
How to shift machine learning experimentation phase to production
Design Patterns for Machine Learning in Production - Sergei Izrailev, Chief D...Sri Ambati
Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video: https://youtu.be/-rGRHrED94Y.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://twitter.com/h2oai.
- - -
Abstract:
Most machine learning systems enable two essential processes: creating a model and applying the model in a repeatable and controlled fashion. These two processes are interrelated and pose technological and organizational challenges as they evolve from research to prototype to production. This presentation outlines common design patterns for tackling such challenges while implementing machine learning in a production environment.
Sergei's Bio:
Dr. Sergei Izrailev is Chief Data Scientist at BeeswaxIO, where he is responsible for data strategy and building AI applications powering the next generation of real-time bidding technology. Before Beeswax, Sergei led data science teams at Integral Ad Science and Collective, where he focused on architecture, development and scaling of data science based advertising technology products. Prior to advertising, Sergei was a quant/trader and developed trading strategies and portfolio optimization methodologies. Previously, he worked as a senior scientist at Johnson & Johnson, where he developed intelligent tools for structure-based drug discovery. Sergei holds a Ph.D. in Physics and Master of Computer Science degrees from the University of Illinois at Urbana-Champaign.
An Architecture for Agile Machine Learning in Real-Time ApplicationsJohann Schleier-Smith
Presented at KDD, August 11, 2015.
Abstract of the paper:
Machine learning techniques have proved effective in recommender systems and other applications, yet teams working to deploy them lack many of the advantages that those in more established software disciplines today take for granted. The well-known Agile methodology advances projects in a chain of rapid development cycles, with subsequent steps often informed by production experiments. Support for such workflow in machine learning applications remains primitive.
The platform developed at if(we) embodies a specific machine learning approach and a rigorous data architecture constraint, so allowing teams to work in rapid iterative cycles. We require models to consume data from a time-ordered event history, and we focus on facilitating creative feature engineering. We make it practical for data scientists to use the same model code in development and in production deployment, and make it practical for them to collaborate on complex models.
We deliver real-time recommendations at scale, returning top results from among 10,000,000 candidates with sub-second response times and incorporating new updates in just a few seconds. Using the approach and architecture described here, our team can routinely go from ideas for new models to production-validated results within two weeks.
Real-time Recommendations for Retail: Architecture, Algorithms, and DesignJuliet Hougland
Users are constantly searching for new content and to stay competitive organizations must act immediately based on up-to-date data. Outdated recommendations decrease the likelihood of presenting the right offer and make it harder to maintain customer loyalty. In order to provide the most relevant recommendations and increase engagement, organizations must track customer interactions and re-score recommendations on the fly.
Data sources have expanded dramatically to include a wealth of historical data and a constant influx of behavior data. The key to moving from predictive models, applied in batch, to models that provide responses in real time, is to focus on the efficiency of model application. The speed that recommendations can be served is influenced by:
Architecture of the recommendation serving platform
Choice of recommendation algorithm
Datastore access patterns
In this presentation, we’ll discuss how developers can use open source components like HBase and Kiji to develop low-latency recommendation models that can be easily deployed by e-commerce companies. We will give practical advice on how to choose models and design data stores that make use of the architecture and quickly serve new recommendations.
These are slides presented at MLconf in San Francisco, November 14, 2014. I share the approach to real-time machine learning for recommender systems developed at if(we). We achieve rapid iterative cycles by adhering to a strict approach to structuring and accessing our data, as well as to building the online features that comprise our models. These developments support teams of data scientist and data engineers, who work together to solve complex recommendation problems. We also introduce the Antelope Realtime Events framework, an open source demonstration application which derives from our scalable proprietary software stack.
Machine Learning system architecture – Microsoft Translator, a Case Study : ...Vishal Chowdhary
Microsoft Translator currently supports 100+ languages. We constantly improve the translation quality, add new scenarios, all with a constant team size. This session describes a production scale machine learning architecture using MS Translator as a case study. You will learn the mental model to approach your ML problem and concrete Do’s and Don’ts for the various components of the ML system architecture.
ML-Ops: From Proof-of-Concept to Production ApplicationHunter Carlisle
Successfully deploying a working machine learning prototype to a production application is a challenging task, frought with difficulties not experienced in traditional software deployments.
In this talk, you will learn techniques to successfully deploy ML applications in a scalable, maintainable, and automated way.
How to design your ML application to be production ready from the day one
How to switch from notebooks to deployable and maintainable software
How to deploy, serve and monitor prediction pipelines
How to re-train models in production
How to shift machine learning experimentation phase to production
Design Patterns for Machine Learning in Production - Sergei Izrailev, Chief D...Sri Ambati
Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video: https://youtu.be/-rGRHrED94Y.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://twitter.com/h2oai.
- - -
Abstract:
Most machine learning systems enable two essential processes: creating a model and applying the model in a repeatable and controlled fashion. These two processes are interrelated and pose technological and organizational challenges as they evolve from research to prototype to production. This presentation outlines common design patterns for tackling such challenges while implementing machine learning in a production environment.
Sergei's Bio:
Dr. Sergei Izrailev is Chief Data Scientist at BeeswaxIO, where he is responsible for data strategy and building AI applications powering the next generation of real-time bidding technology. Before Beeswax, Sergei led data science teams at Integral Ad Science and Collective, where he focused on architecture, development and scaling of data science based advertising technology products. Prior to advertising, Sergei was a quant/trader and developed trading strategies and portfolio optimization methodologies. Previously, he worked as a senior scientist at Johnson & Johnson, where he developed intelligent tools for structure-based drug discovery. Sergei holds a Ph.D. in Physics and Master of Computer Science degrees from the University of Illinois at Urbana-Champaign.
An Architecture for Agile Machine Learning in Real-Time ApplicationsJohann Schleier-Smith
Presented at KDD, August 11, 2015.
Abstract of the paper:
Machine learning techniques have proved effective in recommender systems and other applications, yet teams working to deploy them lack many of the advantages that those in more established software disciplines today take for granted. The well-known Agile methodology advances projects in a chain of rapid development cycles, with subsequent steps often informed by production experiments. Support for such workflow in machine learning applications remains primitive.
The platform developed at if(we) embodies a specific machine learning approach and a rigorous data architecture constraint, so allowing teams to work in rapid iterative cycles. We require models to consume data from a time-ordered event history, and we focus on facilitating creative feature engineering. We make it practical for data scientists to use the same model code in development and in production deployment, and make it practical for them to collaborate on complex models.
We deliver real-time recommendations at scale, returning top results from among 10,000,000 candidates with sub-second response times and incorporating new updates in just a few seconds. Using the approach and architecture described here, our team can routinely go from ideas for new models to production-validated results within two weeks.
Guiding through a typical Machine Learning PipelineMichael Gerke
Many People are talking about AI and Machine Learning. Here's a quick guideline how to manage ML Projects and what to consider in order to implement machine learning use cases.
Spark Summit EU 2017 - Preventing revenue leakage and monitoring distributed ...Flavio Clesio
Our presentation at Spark Summit EU 2017 - spark-summit.org/eu-2017/events/preventing-revenue-leakage-and-monitoring-distributed-systems-with-machine-learning/
The catalyst for the success of automobiles came not through the invention of the car but rather through the establishment of an innovative assembly line. History shows us that the ability to mass produce and distribute a product is the key to driving adoption of any innovation, and machine learning is no different. MLOps is the assembly line of Machine Learning and in this presentation we will discuss the core capabilities your organization should be focused on to implement a successful MLOps system.
Modern Machine Learning Infrastructure and PracticesWill Gardella
Slides from Curtis Huang's talk at the Couchbase Meetup in Mountain View on August 18th. Curtis is a Senior Software Engineer at Facebook working on Machine Learning, with experience in both ad tech and search.
"AI and machine learning have transformed the technology industry for the last decade, creating a foundation for web search, ranking/recommendation, and object/speech recognition. In this talk, I will discuss a collection of machine learning approaches to effectively analyzing and modeling large-scale data. From a hands-on practitioner's perspective, I will talk about the process of building a ML pipeline from idea to production, the challenges, and lessons learned. As an example, I will describe the infrastructure and components of a modern ML ranking system."
MLflow is an MLOps tool that enables data scientist to quickly productionize their Machine Learning projects. To achieve this, MLFlow has four major components which are Tracking, Projects, Models, and Registry. MLflow lets you train, reuse, and deploy models with any library and package them into reproducible steps. MLflow is designed to work with any machine learning library and require minimal changes to integrate into an existing codebase. In this session, we will cover the common pain points of machine learning developers such as tracking experiments, reproducibility, deployment tool and model versioning. Ready to get your hands dirty by doing quick ML project using mlflow and release to production to understand the ML-Ops lifecycle.
Rsqrd AI: How to Design a Reliable and Reproducible PipelineSanjana Chowdhury
In this talk, David Aronchick, co-founder of Kubeflow and Microsoft's Head of Open Source ML, talks about designing reproducible and reliable ML pipelines. He speaks about the importance and impact of MLOps and use of metadata in pipelines. He also talks about a library he wrote to help with this problem, MLSpecLib.
**These slides are from a talk given at Rsqrd AI. Learn more at rsqrdai.org**
The Machine Learning Workflow with AzureIvo Andreev
Machine learning is not black magic but a discipline that involves data analysis, data science and of course – hard work. From searching patterns in data, applying algorithms to converting to usable predictions, you would need background and appropriate tools. In this session, we will go through major approaches to prepare data, build and deploy ML models in Azure (ML Studio, DataScience VM, Jupyter Notebook). Most importantly – based on some examples from the real world, we will provide you with a workflow of best practices.
MLOps and Data Quality: Deploying Reliable ML Models in ProductionProvectus
Looking to build a robust machine learning infrastructure to streamline MLOps? Learn from Provectus experts how to ensure the success of your MLOps initiative by implementing Data QA components in your ML infrastructure.
For most organizations, the development of multiple machine learning models, their deployment and maintenance in production are relatively new tasks. Join Provectus as we explain how to build an end-to-end infrastructure for machine learning, with a focus on data quality and metadata management, to standardize and streamline machine learning life cycle management (MLOps).
Agenda
- Data Quality and why it matters
- Challenges and solutions of Data Testing
- Challenges and solutions of Model Testing
- MLOps pipelines and why they matter
- How to expand validation pipelines for Data Quality
Intelligent Document Processing in Healthcare. Choosing the Right Solutions.Provectus
Healthcare organizations generate piles of documents and forms in different formats, making it difficult to achieve operational excellence and streamline business processes. Manual entry and OCR are no longer viable, and healthcare entities are looking for new solutions to handle documents.
In this presentation you can learn about:
- Healthcare document types and use cases
- IDP framework: building blocks for document processing solutions
- The document processing market landscape
- Methodology for solution evaluation: comparing apples to apples
Whether you are looking for a ready-made solution or plan to build a custom solution of your own, this webinar will help you find the best fit for your healthcare use cases.
MLOps Bridging the gap between Data Scientists and Ops.Knoldus Inc.
Through this session we're going to introduce the MLOps lifecycle and discuss the hidden loopholes that can affect the MLProject. Then we are going to discuss the ML Model lifecycle and discuss the problem with training. We're going to introduce the MLFlow Tracking module in order to track the experiments.
Why APM Is Not the Same As ML MonitoringDatabricks
Application performance monitoring (APM) has become the cornerstone of software engineering allowing engineering teams to quickly identify and remedy production issues. However, as the world moves to intelligent software applications that are built using machine learning, traditional APM quickly becomes insufficient to identify and remedy production issues encountered in these modern software applications.
As a lead software engineer at NewRelic, my team built high-performance monitoring systems including Insights, Mobile, and SixthSense. As I transitioned to building ML Monitoring software, I found the architectural principles and design choices underlying APM to not be a good fit for this brand new world. In fact, blindly following APM designs led us down paths that would have been better left unexplored.
In this talk, I draw upon my (and my team’s) experience building an ML Monitoring system from the ground up and deploying it on customer workloads running large-scale ML training with Spark as well as real-time inference systems. I will highlight how the key principles and architectural choices of APM don’t apply to ML monitoring. You’ll learn why, understand what ML Monitoring can successfully borrow from APM, and hear what is required to build a scalable, robust ML Monitoring architecture.
FrugalML: Using ML APIs More Accurately and CheaplyDatabricks
Offering prediction APIs for fee is a fast growing industry and is an important aspect of machine learning as a service. While many such services are available, the heterogeneity in their price and performance makes it challenging for users to decide which API or combination of APIs to use for their own data and budget. We take a first step towards addressing this challenge by proposing FrugalML, a principled framework that jointly learns the strength and weakness of each API on different data, and performs an efficient optimization to automatically identify the best sequential strategy to adaptively use the available APIs within a budget constraint. Our theoretical analysis shows that natural sparsity in the formulation can be leveraged to make FrugalML efficient. We conduct systematic experiments using ML APIs from Google, Microsoft, Amazon, IBM, Baidu and other providers for tasks including facial emotion recognition, sentiment analysis and speech recognition. Across various tasks, FrugalML can achieve up to 90% cost reduction while matching the accuracy of the best single API, or up to 5% better accuracy while matching the best API’s cost.
Recent Gartner and Capgemini studies predict only around 25% of data science projects are successful and only around 15% make it to full-scale production. Of these, many degrade in performance and produce disappointing results within months of implementation. How can focusing on the desired business outcomes and business use cases throughout a data science project help overcome the odds?
Data Science as a Service: Intersection of Cloud Computing and Data SciencePouria Amirian
Dr. Pouria Amirian explains data science, steps in a data science workflow and show some experiments in AzureML. He also mentions about big data issues in a data science project and solutions to them.
Importance of ML Reproducibility & Applications with MLfLowDatabricks
With data as a valuable currency and the architecture of reliable, scalable Data Lakes and Lakehouses continuing to mature, it is crucial that machine learning training and deployment techniques keep up to realize value. Reproducibility, efficiency, and governance in training and production environments rest on the shoulders of both point in time snapshots of the data and a governing mechanism to regulate, track, and make best use of associated metadata.
This talk will outline the challenges and importance of building and maintaining reproducible, efficient, and governed machine learning solutions as well as posing solutions built on open source technologies – namely Delta Lake for data versioning and MLflow for efficiency and governance.
ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...DATAVERSITY
Many data scientists are well grounded in creating accomplishment in the enterprise, but many come from outside – from academia, from PhD programs and research. They have the necessary technical skills, but it doesn’t count until their product gets to production and in use. The speaker recently helped a struggling data scientist understand his organization and how to create success in it. That turned into this presentation, because many new data scientists struggle with the complexities of an enterprise.
Which institute is best for data science?DIGITALSAI1
EduXfactor is the top and best data science training institute in hyderabad offers data science training with 100% placement assistance with course certification.
Guiding through a typical Machine Learning PipelineMichael Gerke
Many People are talking about AI and Machine Learning. Here's a quick guideline how to manage ML Projects and what to consider in order to implement machine learning use cases.
Spark Summit EU 2017 - Preventing revenue leakage and monitoring distributed ...Flavio Clesio
Our presentation at Spark Summit EU 2017 - spark-summit.org/eu-2017/events/preventing-revenue-leakage-and-monitoring-distributed-systems-with-machine-learning/
The catalyst for the success of automobiles came not through the invention of the car but rather through the establishment of an innovative assembly line. History shows us that the ability to mass produce and distribute a product is the key to driving adoption of any innovation, and machine learning is no different. MLOps is the assembly line of Machine Learning and in this presentation we will discuss the core capabilities your organization should be focused on to implement a successful MLOps system.
Modern Machine Learning Infrastructure and PracticesWill Gardella
Slides from Curtis Huang's talk at the Couchbase Meetup in Mountain View on August 18th. Curtis is a Senior Software Engineer at Facebook working on Machine Learning, with experience in both ad tech and search.
"AI and machine learning have transformed the technology industry for the last decade, creating a foundation for web search, ranking/recommendation, and object/speech recognition. In this talk, I will discuss a collection of machine learning approaches to effectively analyzing and modeling large-scale data. From a hands-on practitioner's perspective, I will talk about the process of building a ML pipeline from idea to production, the challenges, and lessons learned. As an example, I will describe the infrastructure and components of a modern ML ranking system."
MLflow is an MLOps tool that enables data scientist to quickly productionize their Machine Learning projects. To achieve this, MLFlow has four major components which are Tracking, Projects, Models, and Registry. MLflow lets you train, reuse, and deploy models with any library and package them into reproducible steps. MLflow is designed to work with any machine learning library and require minimal changes to integrate into an existing codebase. In this session, we will cover the common pain points of machine learning developers such as tracking experiments, reproducibility, deployment tool and model versioning. Ready to get your hands dirty by doing quick ML project using mlflow and release to production to understand the ML-Ops lifecycle.
Rsqrd AI: How to Design a Reliable and Reproducible PipelineSanjana Chowdhury
In this talk, David Aronchick, co-founder of Kubeflow and Microsoft's Head of Open Source ML, talks about designing reproducible and reliable ML pipelines. He speaks about the importance and impact of MLOps and use of metadata in pipelines. He also talks about a library he wrote to help with this problem, MLSpecLib.
**These slides are from a talk given at Rsqrd AI. Learn more at rsqrdai.org**
The Machine Learning Workflow with AzureIvo Andreev
Machine learning is not black magic but a discipline that involves data analysis, data science and of course – hard work. From searching patterns in data, applying algorithms to converting to usable predictions, you would need background and appropriate tools. In this session, we will go through major approaches to prepare data, build and deploy ML models in Azure (ML Studio, DataScience VM, Jupyter Notebook). Most importantly – based on some examples from the real world, we will provide you with a workflow of best practices.
MLOps and Data Quality: Deploying Reliable ML Models in ProductionProvectus
Looking to build a robust machine learning infrastructure to streamline MLOps? Learn from Provectus experts how to ensure the success of your MLOps initiative by implementing Data QA components in your ML infrastructure.
For most organizations, the development of multiple machine learning models, their deployment and maintenance in production are relatively new tasks. Join Provectus as we explain how to build an end-to-end infrastructure for machine learning, with a focus on data quality and metadata management, to standardize and streamline machine learning life cycle management (MLOps).
Agenda
- Data Quality and why it matters
- Challenges and solutions of Data Testing
- Challenges and solutions of Model Testing
- MLOps pipelines and why they matter
- How to expand validation pipelines for Data Quality
Intelligent Document Processing in Healthcare. Choosing the Right Solutions.Provectus
Healthcare organizations generate piles of documents and forms in different formats, making it difficult to achieve operational excellence and streamline business processes. Manual entry and OCR are no longer viable, and healthcare entities are looking for new solutions to handle documents.
In this presentation you can learn about:
- Healthcare document types and use cases
- IDP framework: building blocks for document processing solutions
- The document processing market landscape
- Methodology for solution evaluation: comparing apples to apples
Whether you are looking for a ready-made solution or plan to build a custom solution of your own, this webinar will help you find the best fit for your healthcare use cases.
MLOps Bridging the gap between Data Scientists and Ops.Knoldus Inc.
Through this session we're going to introduce the MLOps lifecycle and discuss the hidden loopholes that can affect the MLProject. Then we are going to discuss the ML Model lifecycle and discuss the problem with training. We're going to introduce the MLFlow Tracking module in order to track the experiments.
Why APM Is Not the Same As ML MonitoringDatabricks
Application performance monitoring (APM) has become the cornerstone of software engineering allowing engineering teams to quickly identify and remedy production issues. However, as the world moves to intelligent software applications that are built using machine learning, traditional APM quickly becomes insufficient to identify and remedy production issues encountered in these modern software applications.
As a lead software engineer at NewRelic, my team built high-performance monitoring systems including Insights, Mobile, and SixthSense. As I transitioned to building ML Monitoring software, I found the architectural principles and design choices underlying APM to not be a good fit for this brand new world. In fact, blindly following APM designs led us down paths that would have been better left unexplored.
In this talk, I draw upon my (and my team’s) experience building an ML Monitoring system from the ground up and deploying it on customer workloads running large-scale ML training with Spark as well as real-time inference systems. I will highlight how the key principles and architectural choices of APM don’t apply to ML monitoring. You’ll learn why, understand what ML Monitoring can successfully borrow from APM, and hear what is required to build a scalable, robust ML Monitoring architecture.
FrugalML: Using ML APIs More Accurately and CheaplyDatabricks
Offering prediction APIs for fee is a fast growing industry and is an important aspect of machine learning as a service. While many such services are available, the heterogeneity in their price and performance makes it challenging for users to decide which API or combination of APIs to use for their own data and budget. We take a first step towards addressing this challenge by proposing FrugalML, a principled framework that jointly learns the strength and weakness of each API on different data, and performs an efficient optimization to automatically identify the best sequential strategy to adaptively use the available APIs within a budget constraint. Our theoretical analysis shows that natural sparsity in the formulation can be leveraged to make FrugalML efficient. We conduct systematic experiments using ML APIs from Google, Microsoft, Amazon, IBM, Baidu and other providers for tasks including facial emotion recognition, sentiment analysis and speech recognition. Across various tasks, FrugalML can achieve up to 90% cost reduction while matching the accuracy of the best single API, or up to 5% better accuracy while matching the best API’s cost.
Recent Gartner and Capgemini studies predict only around 25% of data science projects are successful and only around 15% make it to full-scale production. Of these, many degrade in performance and produce disappointing results within months of implementation. How can focusing on the desired business outcomes and business use cases throughout a data science project help overcome the odds?
Data Science as a Service: Intersection of Cloud Computing and Data SciencePouria Amirian
Dr. Pouria Amirian explains data science, steps in a data science workflow and show some experiments in AzureML. He also mentions about big data issues in a data science project and solutions to them.
Importance of ML Reproducibility & Applications with MLfLowDatabricks
With data as a valuable currency and the architecture of reliable, scalable Data Lakes and Lakehouses continuing to mature, it is crucial that machine learning training and deployment techniques keep up to realize value. Reproducibility, efficiency, and governance in training and production environments rest on the shoulders of both point in time snapshots of the data and a governing mechanism to regulate, track, and make best use of associated metadata.
This talk will outline the challenges and importance of building and maintaining reproducible, efficient, and governed machine learning solutions as well as posing solutions built on open source technologies – namely Delta Lake for data versioning and MLflow for efficiency and governance.
ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...DATAVERSITY
Many data scientists are well grounded in creating accomplishment in the enterprise, but many come from outside – from academia, from PhD programs and research. They have the necessary technical skills, but it doesn’t count until their product gets to production and in use. The speaker recently helped a struggling data scientist understand his organization and how to create success in it. That turned into this presentation, because many new data scientists struggle with the complexities of an enterprise.
Which institute is best for data science?DIGITALSAI1
EduXfactor is the top and best data science training institute in hyderabad offers data science training with 100% placement assistance with course certification.
Join us for the Best Selenium certification course at Edux factor and enrich your carrier.
Dream for wonderful carrier we make to achieve your dreams come true Hurry up & enroll now.
<a href="https://eduxfactor.com/selenium-online-training">Best Selenium certification course</a>
Data Science Online Training In HA comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge.hyderabad Data Science Online Training
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Exploring the EduXfactor Data Science Training program, you will learn components of the Data Science lifecycle such as Big Data, Hadoop, Machine Learning, Deep Learning & R programming. Our professional experts will teach you how to adopt a blend of mathematics, statistics, business acumen, tools, algorithms & machine learning techniques. You will learn how to handle a large amount of data information & process it according to any firm business strategy.
A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge.
Eduxfactor is an online data science training institution based in Hyderabad. A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Data science online training in hyderabadVamsiNihal
Exploring the EduXfactor Data Science Training program, you will learn components of the Data Science lifecycle such as Big Data, Hadoop, Machine Learning, Deep Learning & R programming. Our professional experts will teach you how to adopt a blend of mathematics, statistics, business acumen, tools, algorithms & machine learning techniques. You will learn how to handle a large amount of data information & process it according to any firm business strategy.
Overview of Data Science Courses Online
A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge.
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A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge
EduXfactor is the top and best data science training institute in hyderabad offers data science training with 100% placement assistance with course certification.
Data science online training in hyderabadVamsiNihal
Exploring the EduXfactor Data Science Training program, you will learn components of the Data Science lifecycle such as Big Data, Hadoop, Machine Learning, Deep Learning & R programming. Our professional experts will teach you how to adopt a blend of mathematics, statistics, business acumen, tools, algorithms & machine learning techniques. You will learn how to handle a large amount of data information & process it according to any firm business strategy.
data science online training in hyderabadVamsiNihal
A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge. Grasp the key fundamentals of data science, coding, and machine learning. Develop mastery over essential analytic tools like R, Python, SQL, and more.
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Join us for the Best data science training in Hyderabad at Edux factor and enrich your carrier.
Dream for wonderful carrier we make to achieve your dreams come true Hurry up & enroll now.
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The world has witnessed explosive digital growth in the last two decades, which has led to a data deluge. This data may be
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1. Architecting for Data Science
johann@ifwe.co@jssmith github.com/ifwe
Johann Schleier-Smith
CTO, if(we)
O’Reilly Software Architecture Conference
Boston, March 19, 2015
5. Alternative Definitions
extraction of knowledge from data
making discoveries in the world of big data
statistics + machine learning + scalable
computation + visualization + computer science +
business acumen + skilled communication
6. Related and Alternative Language
business intelligence
statistics
data mining
forecasting
business reporting
predictive modeling
analyticsknowledge extraction
10. • >10 million candidates to draw from
• >1000 updates/sec
• Must be responsive to current activity
• Users expect instant query results
Recommendation engine
for dating product
11. • Real-time is challenging
• Human behavior is
complicated, especially in
social context
• Previous interactions are
perhaps our best hope for
predicting future interactions
12. • Human connections
• User engagement ecosystem
• Subscription and other revenues
Value
♥
18. “outgoing and social (heavy messaging ---
especially distant recipients and opposite gender,
many outgoing comments, many friend requests to
distant people), doesn’t play Pets much”
“receives many messages, active user,
views many profiles, doesn't use meet me,
sends many messages to distant people”
Heavy user overall, (pets, meet me, messaging)!
“heavy user overall, (pets, meet me, messaging)”
23. {
“sku” : “1032361”,
“regularPrice” : “19.99”,
“name” : “Need for Speed: Hot Pursuit”,
“description” : “Fasten your seatbelt and
get ready to drive like your life depends
on it...”
...
}
product updates
24. Formats for Data
log files
web services
relational databases
unstructured documents
spreadsheets
xml files
25. Types of Data
technical data
government data
usage records
sensor data
academic data
reference data
yet uncollected data
26. Vasant Dhar. 2013. Data science and prediction. Commun. ACM 56, 12 (December 2013), 64-73.
And International Telecommunication Union (ITU) and United Nations Population Division via www.internetlivestats.com/internet-users/
32. • Created in 1993
• Implementation of S language but also
inherits from Scheme
• Object oriented code is possible but
not encouraged
• Vast high-quality package ecosystem
• Data is vectors and data frames
38. • More of a general purpose language than R
• Arrays and matrices as basic data structures
• Supports data frames through Pandas
• Sophisticated machine learning libraries
• Generally limited to in-memory data sets
39. • Leverages commodity hardware to
store large data sets at low cost
• Vibrant and diverse ecosystem
• Popular but not always best solution
• Probably best viewed as marketing
terminology, as opposed to technology
40. https://hadoopecosystemtable.github.io/
Category
Number of
projects
Distributed Filesystem 7
Distributed Programming 18
NoSQL Database 4
Document Data Model 3
Stream Data Model 1
Key-Value Data Model 4
Graph Data Model 3
NewSQL 9
SQL-On-Hadoop 11
Data Ingestion 11
Service Programming 7
Scheduling 3
Machine Learning 6
Benchmarking 5
Security 3
System Deployment 12
Applications 5
Development Frameworks 2
Categorize Pending 16
130 freely licensed open source
projects listed in the Hadoop
Ecosystem Table
41. Hadoop for Data Scientists
• Pulling data from repository (SQL, Hive)
• MapReduce programming (Java, Scala,
Pig, Python)
• Spark in-memory framework is gaining
adoption rapidly
47. • Profitable startup actively pursuing big
opportunities in social apps
• Millions of users on existing products
• Thousands of social contacts per second
49. 1. Gain understanding of the product usage
2. See opportunity to make the product better
3. Create training data
4. Train predictive models
5. Put models in production
6. See improvements
51. 1. Gain understanding of the product usage
2. See opportunity to make the product better
3. Pull records from relational database to create
interesting features (usually aggregates)
4. Train predictive models
5. Go implement models for production
6. See improvements
52. 1. Gain understanding of the product usage
2. See opportunity to make the product better
3. Pull records from relational database to create
interesting features (usually aggregates)
4. Train predictive models
5. Go implement models for production
6. See improvements
3-6
months
53. 1. Gain understanding of the product usage
2. See opportunity to make the product better
3. Pull records from relational database to create
interesting features (usually aggregates)
4. Train predictive models
5. Go implement models for production
6. See improvements Cool!
Was it worth it?
55. • Data scientist hands model description to
software engineer
• May need to translate features from SQL to Java
• Aggregate features require batch processing
• May need to adjust features and model to achieve
real-time updates
• Fast scoring requires high-performance in-
memory data structures
61. Bob registers
Alice registers
Alice updates profile
Bob opens app
Bob sees Alice in recommendations
Bob swipes yes on Alice
Alice receives push notification
Alice sees Bob swiped yes
Alice swipes yes
Alice sends message to Bob
82. 1. Gain understanding of the product usage
2. See opportunity to make the product better
3. Create training data
4. Train predictive models
5. Put models in production
6. See improvements
Fast cycles!!
84. • Open source implementation derived from if(we)’s
proprietary platform
• Not ready scale or production, but useful for
demonstration purposes
• Seeking collaborators
85. product update events
{
“timestamp” : “2012-05-03 6:43:15”,
“eventType” : “ProductUpdate”,
“eventProperties” : {
“sku” : “1032361”,
“regularPrice” : “19.99”,
“name” : “Need for Speed: Hot Pursuit”,
“description” : “Fasten your seatbelt and
get ready to drive like your life depends
on it...”
...
}
}
109. • Make sure that events are simple facts
• Files are ok for event history, don’t really need a database
• Use an object hierarchy to model events in code
• Use online features that are efficient to update incrementally
• Write efficient implementations before than scaling out
• Functional style makes it easier
• Encourage reactive processing
110.
111.
112. Data Quality
• Matters more than transformations, more than algorithms
• Data that doesn’t make sense often indicates an application bug
• Do assertions, e.g., make sure things aren’t happening out of order
113.
114. • All data in form of events – no exceptions!
• Same feature code in production and development
• Emphasis on creative feature engineering
• Quick cycles between ideas and production
github.com/ifwe/antelope
@jssmith
Try the Antelope Demo:
https://github.com/ifwe/antelope/blob/master/doc/demo.md