From Python to PySpark and Back Again – Unifying Single-host and Distributed Deep Learning with Maggy
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Distributed deep learning offers many benefits – faster training of models using more GPUs, parallelizing hyperparameter tuning over many GPUs, and parallelizing ablation studies to help understand the behaviour and performance of deep neural networks.
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GE is a world leader in the manufacture of commercial jet engines, offering products for many of the best-selling commercial airframes. With more than 33,000 engines in service, GE Aviation has a history of developing analytics for monitoring its commercial engines fleets. In recent years, GE Aviation Digital has developed advanced analytic solutions for engine monitoring, with the target of improving detection and reducing false alerts, when compared to conventional analytic approaches. The advanced analytics are implemented in a real-time monitoring system which notifies GE’s Fleet Support team on a per flight basis. These analytics are developed and validated using large, historical datasets.
Analytic tools such as SQL Server and MATLAB were used until recently, when GE’s data was moved to an Apache Spark environment. Consequently, our advanced analytics are now being migrated to Spark, where there should also be performance gains with bigger data sets. In this talk we will share experiences of converting our advanced algorithms to custom Spark ML pipelines, as well as outlining various case studies.
With Honor Powrie and Peter Knight
Introduction to MLflow
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure. In this talk, I present MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
Chris Fregly, Research Scientist, PipelineIO at MLconf ATL 2016
Comparing TensorFlow NLP Options: word2Vec, gloVe, RNN/LSTM, SyntaxNet, and Penn Treebank: Through code samples and demos, we’ll compare the architectures and algorithms of the various TensorFlow NLP options. We’ll explore both feed-forward and recurrent neural networks such as word2vec, gloVe, RNN/LSTM, SyntaxNet, and Penn Treebank using the latest TensorFlow libraries.
Oleksii Moskalenko "Continuous Delivery of ML Pipelines to Production"
Here in DS team in WIX we want to help to create stunning sites by applying recent achievement of AI research to production. Since Data Science engineering practices are still not fully shaped we found out that it is crucial to bring the best practices from software engineering - give Data Scientist ability to deliver models fast without loss in quality and computation efficiency to stay competitive in this overhyped market. To achieve this we are developing our own infrastructure for creating pipelines and deploying them to production with minimum (to none) engineer involvement.
This talk will cover initial motivation, solved technical issues and lessons learned while building such ML delivery system.
Website: https://fwdays.com/en/event/data-science-fwdays-2019/review/continuous-delivery-of-ml-pipelines-to-production
Ibis: Seamless Transition Between Pandas and Apache Spark
Pandas is the de facto standard (single-node) Data Frame implementation in Python. However, as data grows larger, pandas no longer works very well due to performance reasons.
Navigating the ML Pipeline Jungle with MLflow: Notes from the Field with Thun...
Plumbing has been a key focus of modern software engineering, with our API/services/containers/devops driven landscape so it may come as a surprise that plumbing is where AI projects tend to fail. But it is precisely because our modern software development focuses on decoupled plumbing that we have struggled to handle the rise of AI.
Specifically, companies are able to use AI effectively when they are able to create end-to-end AI model factories that explicitly account for coupling between data, models, and code.
In this talk, I will be walking through what a model factory is and how MLFlow’s design supports the creation of end-to-end model factories as well as sharing best practices I’ve observed helping customers from startups to Fortune 50s create, productionize, and scale end-to-end ML pipelines, and watching those pipelines produce serious, game changing business impact.
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This talk tells the story of implementation and optimization of a sparse logistic regression algorithm in spark. I would like to share the lessons I learned and the steps I had to take to improve the speed of execution and convergence of my initial naive implementation. The message isn’t to convince the audience that logistic regression is great and my implementation is awesome, rather it will give details about how it works under the hood, and general tips for implementing an iterative parallel machine learning algorithm in spark. The talk is structured as a sequence of “lessons learned” that are shown in form of code examples building on the initial naive implementation. The performance impact of each “lesson” on execution time and speed of convergence is measured on benchmark datasets. You will see how to formulate logistic regression in a parallel setting, how to avoid data shuffles, when to use a custom partitioner, how to use the ‘aggregate’ and ‘treeAggregate’ functions, how momentum can accelerate the convergence of gradient descent, and much more. I will assume basic understanding of machine learning and some prior knowledge of spark. The code examples are written in scala, and the code will be made available for each step in the walkthrough.
Flock: Data Science Platform @ CISL
In this talk, we will present the basic features and functionality of Flock, an end-to-end research platform that we are developing at CISL which simplifies and automates the integration of machine learning solutions in data engines. Flock makes use of MLflow for model and experiment tracking but extends and complements it by providing automatic logging, model optimizations and support for the ONNX model format.
We will showcase Flock's features through a demo using Microsoft's Azure Data Studio and SQL Server.
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Data Science and Machine Learning Using Python and Scikit-learn
Data Science and Machine Learning Using Python and Scikit-learn
Hadoop France meetup Feb2016 : recommendations with spark
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Migrating Apache Spark ML Jobs to Spark + Tensorflow on Kubeflow
Migrating Apache Spark ML Jobs to Spark + Tensorflow on Kubeflow
ROCm and Distributed Deep Learning on Spark and TensorFlow
ROCm and Distributed Deep Learning on Spark and TensorFlow
Ge aviation spark application experience porting analytics into py spark ml p...
Ge aviation spark application experience porting analytics into py spark ml p...
Chris Fregly, Research Scientist, PipelineIO at MLconf ATL 2016
Chris Fregly, Research Scientist, PipelineIO at MLconf ATL 2016
Oleksii Moskalenko "Continuous Delivery of ML Pipelines to Production"
Oleksii Moskalenko "Continuous Delivery of ML Pipelines to Production"
Ibis: Seamless Transition Between Pandas and Apache Spark
Ibis: Seamless Transition Between Pandas and Apache Spark
Navigating the ML Pipeline Jungle with MLflow: Notes from the Field with Thun...
Navigating the ML Pipeline Jungle with MLflow: Notes from the Field with Thun...
Lessons Learned while Implementing a Sparse Logistic Regression Algorithm in ...
Lessons Learned while Implementing a Sparse Logistic Regression Algorithm in ...
Similar to From Python to PySpark and Back Again – Unifying Single-host and Distributed Deep Learning with Maggy
Parallel Ablation Studies for Machine Learning with Maggy on Apache Spark
Ablation studies have become best practice in machine learning research as they provide insights into the relative contribution of the different architectural and regularization components to the performance of models. An ablation study consists of several trials, where one trial could be, e.g., removing the last convolutional layer of a CNN model, retraining the model, and observing the resulting performance. However, as machine learning architectures become ever deeper and data sizes keep growing, there is an explosion in the number of different architecture combinations that need to be evaluated to understand their relative performance. In this talk, we introduce a new Spark-based framework for design and parallel execution of ablation studies on machine learning frameworks.
A Tale of Three Deep Learning Frameworks: TensorFlow, Keras, & PyTorch with B...
We all know what they say – the bigger the data, the better. But when the data gets really big, how do you mine it and what deep learning framework to use? This talk will survey, with a developer’s perspective, three of the most popular deep learning frameworks—TensorFlow, Keras, and PyTorch—as well as when to use their distributed implementations.
We’ll compare code samples from each framework and discuss their integration with distributed computing engines such as Apache Spark (which can handle massive amounts of data) as well as help you answer questions such as:
As a developer how do I pick the right deep learning framework?
Do I want to develop my own model or should I employ an existing one?
How do I strike a trade-off between productivity and control through low-level APIs?
What language should I choose?
In this session, we will explore how to build a deep learning application with Tensorflow, Keras, or PyTorch in under 30 minutes. After this session, you will walk away with the confidence to evaluate which framework is best for you.
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This document provides an overview of a meetup on language translation with deep learning using TensorFlow on FloydHub. It will cover the language translation challenge, introducing key concepts like deep learning, RNNs, NLP, TensorFlow and FloydHub. It will then describe the solution approach to the translation task, including a demo and code walkthrough. Potential next steps and references for further learning are also mentioned.
Towards Safe Automated Refactoring of Imperative Deep Learning Programs to Gr...
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Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged at the expense of run-time performance. Though hybrid approaches aim for the “best of both worlds,” using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution. We present our ongoing work on automated refactoring that assists developers in specifying whether and how their otherwise eagerly-executed imperative DL code could be reliably and efficiently executed as graphs while preserving semantics. The approach, based on a novel imperative tensor analysis, will automatically determine when it is safe and potentially advantageous to migrate imperative DL code to graph execution and modify decorator parameters or eagerly executing code already running as graphs. The approach is being implemented as a PyDev Eclipse IDE plug-in and uses the WALA Ariadne analysis framework. We discuss our ongoing work towards optimizing imperative DL code to its full potential.Natural language processing open seminar For Tensorflow usage
This is presentation for Natural Language Processing open seminar in Kookmin University.
The open seminar reference : https://cafe.naver.com/nlpk
My presentation about how to use tensorflow for NLP open seminar for newbies for tensorflow.
(Py)testing the Limits of Machine Learning
Despite the hype cycle, each day machine learning becomes a little less magic and a little more real. Predictions increasingly drive our everyday lives, embedded into more of our everyday applications. To support this creative surge, development teams are evolving, integrating novel open source software and state-of-the-art GPU hardware, and bringing on essential new teammates like data ethicists and machine learning engineers. Software teams are also now challenged to build and maintain codebases that are intentionally not fully deterministic.
This nondeterminism can manifest in a number of surprising and oftentimes very stressful ways! Successive runs of model training may produce slight but meaningful variations. Data wrangling pipelines turn out to be extremely sensitive to the order in which transformations are applied, and require thoughtful orchestration to avoid leakage. Model hyperparameters that can be tuned independently may have mutually exclusive conditions. Models can also degrade over time, producing increasingly unreliable predictions. Moreover, open source libraries are living, dynamic things; the latest release of your team's favorite library might cause your code to suddenly behave in unexpected ways.
Put simply, as ML becomes more of an expectation than an exception in our industry, testing has never been more important! Fortunately, we are lucky to have a rich open source ecosystem to support us in our journey to build the next generation of apps in a safe, stable way. In this talk we'll share some hard-won lessons, favorite open source packages, and reusable techniques for testing ML software components.
Certification Study Group - NLP & Recommendation Systems on GCP Session 5
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Continuous Delivery of Deep Transformer-Based NLP Models Using MLflow and AWS...
Transformer-based pretrained language models such as BERT, XLNet, Roberta and Albert significantly advance the state-of-the-art of NLP and open doors for solving practical business problems with high performance transfer learning. However, operationalizing these models with production-quality continuous integration/ delivery (CI/CD) end-to-end pipelines that cover the full machine learning life cycle stages of train, test, deploy and serve while managing associated data and code repositories is still a challenging task.
Continuous Delivery of Deep Transformer-Based NLP Models Using MLflow and AWS...
Transformer-based pre-trained language models such as BERT, XLNet, Roberta and Albert significantly advance the state-of-the-art of NLP and open doors for solving practical business problems with high-performance transfer learning. However, operationalizing these models with production-quality continuous integration/ delivery (CI/CD) end-to-end pipelines that cover the full machine learning life cycle stages of train, test, deploy and serve while managing associated data and code repositories is still a challenging task. In this presentation, we will demonstrate how we use MLflow and AWS Sagemaker to productionize deep transformer-based NLP models for guided sales engagement scenarios at the leading sales engagement platform, Outreach.io.
On the Semantics of Real-Time Domain Specific Modeling Languages
This document summarizes Jose E. Rivera's PhD thesis on providing formal semantics for real-time domain specific modeling languages. The thesis defines a framework for specifying the timed behavior of domain specific modeling languages using rewriting logic. It extends in-place model transformations to include time-dependent behavior. Models are formally specified in Maude, enabling simulation and analysis of real-time properties. The work contributes tools and techniques for formally specifying, simulating, and analyzing real-time domain specific modeling languages.
Machine Learning with TensorFlow 2
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- Key concepts include tensors (multi-dimensional arrays), flow graphs representing operations as nodes and tensors as edges, and sessions for executing graphs. TensorFlow 2.0 fully integrates the Keras API.
- TensorFlow is used for tasks like computer vision, natural language processing, speech recognition through neural networks and deep learning models. Popular datasets include MNIST for images and IMDB for text.
Tensorflow Ecosystem
The document summarizes the TensorFlow ecosystem. It discusses TensorFlow's data processing, model building, training, deployment, and tooling capabilities. It highlights improvements in TensorFlow 2.x like eager execution by default, tight Keras integration, and support for distributed training. The document also discusses how TensorFlow empowers responsible AI through initiatives like privacy research, model cards, and collaborative tools to improve model performance and transparency.
Multi-Task Learning and Web Search Ranking
The document discusses using multi-task learning approaches for web search ranking. It describes using each query, language, or query class as a separate task. Methods include adaptive target value transformation where each query has its own linear transformation, multi-language MLR where each language is a separate task, and multi-query class MLR where different query types are separate tasks. Experiments show gains from these multi-task learning methods.
Overview of TensorFlow For Natural Language Processing
TensorFlow open sourced recently by Google is one of the key frameworks that support development of deep learning architectures. In this slideset, part 1, we get started with a few basic primitives of TensorFlow. We will also discuss when and when not to use TensorFlow.
Training at AI Frontiers 2018 - Lukasz Kaiser: Sequence to Sequence Learning ...
This document discusses sequence to sequence learning with Tensor2Tensor (T2T) and sequence models. It provides an overview of T2T, which is a library for deep learning models and datasets. It discusses basics of sequence models including recurrent neural networks (RNNs), convolutional models, and the Transformer model based on attention. It encourages experimenting with different sequence models and datasets in T2T.
Transfer Learning for Improving Model Predictions in Robotic Systems
Modern software systems are now being built to be used in dynamic environments utilizing configuration capabilities to adapt to changes and external uncertainties. In a self-adaptation context, we are often interested in reasoning about the performance of the systems under different configurations. Usually, we learn a black-box model based on real measurements to predict the performance of the system given a specific configuration. However, as modern systems become more complex, there are many configuration parameters that may interact and, therefore, we end up learning an exponentially large configuration space. Naturally, this does not scale when relying on real measurements in the actual changing environment. We propose a different solution: Instead of taking the measurements from the real system, we learn the model using samples from other sources, such as simulators that approximate performance of the real system at low cost.
Deep Learning 모델의 효과적인 분산 트레이닝과 모델 최적화 방법 - 김무현 데이터 사이언티스트, AWS :: AWS Summit...
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Deep Learning 모델의 효과적인 분산 트레이닝과 모델 최적화 방법
김무현 데이터 사이언티스트, AWS
갈수록 증가하는 데이터의 규모와 복잡도를 효율적으로 제어하면서 모델의 성능을 향상시키기 위해서, AWS Deep Learnign AMI, SageMaker, SageMaker Pipemode, Horovod, Tensor Core 등을 어떻게 활용할 수 있는지 알아봅니다. 또한, 다양한 디바이스에서 모델 inference 수행 속도와 이식성을 높여주는 머신러닝 모델 최적화 툴 Neo-AI도 소개합니다.deepnet-lourentzou.ppt
This document provides an outline for a presentation on machine learning and deep learning. It begins with an introduction to machine learning basics and types of learning. It then discusses what deep learning is and why it is useful. The main components and hyperparameters of deep learning models are explained, including activation functions, optimizers, cost functions, regularization methods, and tuning. Basic deep neural network architectures like convolutional and recurrent networks are described. An example application of relation extraction is provided. The document concludes by listing additional deep learning topics.
Automate ml workflow_transmogrif_ai-_chetan_khatri_berlin-scala
TransmogrifAI is an open source library for automating machine learning workflows built on Scala and Spark. It helps automate tasks like feature engineering, selection, model selection, and hyperparameter tuning. This reduces machine learning development time from months to hours. TransmogrifAI enforces type safety and modularity to build reusable, production-ready models. It was created by Salesforce to make machine learning more accessible to developers without a PhD in machine learning.
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Certification Study Group - NLP & Recommendation Systems on GCP Session 5
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The document discusses migrating a data warehouse to the Databricks Lakehouse Platform. It outlines why legacy data warehouses are struggling, how the Databricks Platform addresses these issues, and key considerations for modern analytics and data warehousing. The document then provides an overview of the migration methodology, approach, strategies, and key takeaways for moving to a lakehouse on Databricks.
Data Lakehouse Symposium | Day 1 | Part 1
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Data Lakehouse Symposium | Day 1 | Part 2
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Data Lakehouse Symposium | Day 2
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
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In this session, learn how to quickly supplement your on-premises Hadoop environment with a simple, open, and collaborative cloud architecture that enables you to generate greater value with scaled application of analytics and AI on all your data. You will also learn five critical steps for a successful migration to the Databricks Lakehouse Platform along with the resources available to help you begin to re-skill your data teams.
Democratizing Data Quality Through a Centralized Platform
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Dynamically generating pipelines that can be abstracted away from users
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In this talk, I would like to introduce an open-source tool built by our team that simplifies the data conversion from Apache Spark to deep learning frameworks.
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The existing data conversion process can be tedious. For example, to convert an Apache Spark DataFrame to a TensorFlow Dataset file format, you need to either save the Apache Spark DataFrame on a distributed filesystem in parquet format and load the converted data with third-party tools such as Petastorm, or save it directly in TFRecord files with spark-tensorflow-connector and load it back using TFRecordDataset. Both approaches take more than 20 lines of code to manage the intermediate data files, rely on different parsing syntax, and require extra attention for handling vector columns in the Spark DataFrames. In short, all these engineering frictions greatly reduced the data scientists’ productivity.
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Scaling your Data Pipelines with Apache Spark on Kubernetes
There is no doubt Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark has evolved to run both Machine Learning and large scale analytics workloads. There is growing interest in running Apache Spark natively on Kubernetes. By combining the flexibility of Kubernetes and scalable data processing with Apache Spark, you can run any data and machine pipelines on this infrastructure while effectively utilizing resources at disposal.
In this talk, Rajesh Thallam and Sougata Biswas will share how to effectively run your Apache Spark applications on Google Kubernetes Engine (GKE) and Google Cloud Dataproc, orchestrate the data and machine learning pipelines with managed Apache Airflow on GKE (Google Cloud Composer). Following topics will be covered: – Understanding key traits of Apache Spark on Kubernetes- Things to know when running Apache Spark on Kubernetes such as autoscaling- Demonstrate running analytics pipelines on Apache Spark orchestrated with Apache Airflow on Kubernetes cluster.
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
Pipelines have become ubiquitous, as the need for stringing multiple functions to compose applications has gained adoption and popularity. Common pipeline abstractions such as “fit” and “transform” are even shared across divergent platforms such as Python Scikit-Learn and Apache Spark.
Scaling pipelines at the level of simple functions is desirable for many AI applications, however is not directly supported by Ray’s parallelism primitives. In this talk, Raghu will describe a pipeline abstraction that takes advantage of Ray’s compute model to efficiently scale arbitrarily complex pipeline workflows. He will demonstrate how this abstraction cleanly unifies pipeline workflows across multiple platforms such as Scikit-Learn and Spark, and achieves nearly optimal scale-out parallelism on pipelined computations.
Attendees will learn how pipelined workflows can be mapped to Ray’s compute model and how they can both unify and accelerate their pipelines with Ray.
Sawtooth Windows for Feature Aggregations
In this talk about zipline, we will introduce a new type of windowing construct called a sawtooth window. We will describe various properties about sawtooth windows that we utilize to achieve online-offline consistency, while still maintaining high-throughput, low-read latency and tunable write latency for serving machine learning features.We will also talk about a simple deployment strategy for correcting feature drift – due operations that are not “abelian groups”, that operate over change data.
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
We want to present multiple anti patterns utilizing Redis in unconventional ways to get the maximum out of Apache Spark.All examples presented are tried and tested in production at Scale at Adobe. The most common integration is spark-redis which interfaces with Redis as a Dataframe backing Store or as an upstream for Structured Streaming. We deviate from the common use cases to explore where Redis can plug gaps while scaling out high throughput applications in Spark.
Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue
· Why?
o Custom queries on top a table; We load the data once and query N times
· Why not Structured Streaming
· Working Solution using Redis
Niche 2 : Distributed Counters
· Problems with Spark Accumulators
· Utilize Redis Hashes as distributed counters
· Precautions for retries and speculative execution
· Pipelining to improve performance
Re-imagine Data Monitoring with whylogs and Spark
In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will, affect the bottom line. As a result, ensuring data quality across the entire ML pipeline is both costly, and cumbersome while data monitoring is often fragmented and performed ad hoc. To address these challenges, we built whylogs, an open source standard for data logging. It is a lightweight data profiling library that enables end-to-end data profiling across the entire software stack. The library implements a language and platform agnostic approach to data quality and data monitoring. It can work with different modes of data operations, including streaming, batch and IoT data.
In this talk, we will provide an overview of the whylogs architecture, including its lightweight statistical data collection approach and various integrations. We will demonstrate how the whylogs integration with Apache Spark achieves large scale data profiling, and we will show how users can apply this integration into existing data and ML pipelines.
Raven: End-to-end Optimization of ML Prediction Queries
Machine learning (ML) models are typically part of prediction queries that consist of a data processing part (e.g., for joining, filtering, cleaning, featurization) and an ML part invoking one or more trained models. In this presentation, we identify significant and unexplored opportunities for optimization. To the best of our knowledge, this is the first effort to look at prediction queries holistically, optimizing across both the ML and SQL components.
We will present Raven, an end-to-end optimizer for prediction queries. Raven relies on a unified intermediate representation that captures both data processing and ML operators in a single graph structure.
This allows us to introduce optimization rules that
(i) reduce unnecessary computations by passing information between the data processing and ML operators
(ii) leverage operator transformations (e.g., turning a decision tree to a SQL expression or an equivalent neural network) to map operators to the right execution engine, and
(iii) integrate compiler techniques to take advantage of the most efficient hardware backend (e.g., CPU, GPU) for each operator.
We have implemented Raven as an extension to Spark’s Catalyst optimizer to enable the optimization of SparkSQL prediction queries. Our implementation also allows the optimization of prediction queries in SQL Server. As we will show, Raven is capable of improving prediction query performance on Apache Spark and SQL Server by up to 13.1x and 330x, respectively. For complex models, where GPU acceleration is beneficial, Raven provides up to 8x speedup compared to state-of-the-art systems. As part of the presentation, we will also give a demo showcasing Raven in action.
Processing Large Datasets for ADAS Applications using Apache Spark
Semantic segmentation is the classification of every pixel in an image/video. The segmentation partitions a digital image into multiple objects to simplify/change the representation of the image into something that is more meaningful and easier to analyze [1][2]. The technique has a wide variety of applications ranging from perception in autonomous driving scenarios to cancer cell segmentation for medical diagnosis.
Exponential growth in the datasets that require such segmentation is driven by improvements in the accuracy and quality of the sensors generating the data extending to 3D point cloud data. This growth is further compounded by exponential advances in cloud technologies enabling the storage and compute available for such applications. The need for semantically segmented datasets is a key requirement to improve the accuracy of inference engines that are built upon them.
Streamlining the accuracy and efficiency of these systems directly affects the value of the business outcome for organizations that are developing such functionalities as a part of their AI strategy.
This presentation details workflows for labeling, preprocessing, modeling, and evaluating performance/accuracy. Scientists and engineers leverage domain-specific features/tools that support the entire workflow from labeling the ground truth, handling data from a wide variety of sources/formats, developing models and finally deploying these models. Users can scale their deployments optimally on GPU-based cloud infrastructure to build accelerated training and inference pipelines while working with big datasets. These environments are optimized for engineers to develop such functionality with ease and then scale against large datasets with Spark-based clusters on the cloud.
Massive Data Processing in Adobe Using Delta Lake
At Adobe Experience Platform, we ingest TBs of data every day and manage PBs of data for our customers as part of the Unified Profile Offering. At the heart of this is a bunch of complex ingestion of a mix of normalized and denormalized data with various linkage scenarios power by a central Identity Linking Graph. This helps power various marketing scenarios that are activated in multiple platforms and channels like email, advertisements etc. We will go over how we built a cost effective and scalable data pipeline using Apache Spark and Delta Lake and share our experiences.
What are we storing?
Multi Source – Multi Channel Problem
Data Representation and Nested Schema Evolution
Performance Trade Offs with Various formats
Go over anti-patterns used
(String FTW)
Data Manipulation using UDFs
Writer Worries and How to Wipe them Away
Staging Tables FTW
Datalake Replication Lag Tracking
Performance Time!
More from Databricks (20)
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
Democratizing Data Quality Through a Centralized Platform
Democratizing Data Quality Through a Centralized Platform
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
Stage Level Scheduling Improving Big Data and AI Integration
Stage Level Scheduling Improving Big Data and AI Integration
Simplify Data Conversion from Spark to TensorFlow and PyTorch
Simplify Data Conversion from Spark to TensorFlow and PyTorch
Scaling your Data Pipelines with Apache Spark on Kubernetes
Scaling your Data Pipelines with Apache Spark on Kubernetes
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Raven: End-to-end Optimization of ML Prediction Queries
Raven: End-to-end Optimization of ML Prediction Queries
Processing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache Spark
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原版一模一样【微信：741003700 】【加拿大麦吉尔大学毕业证（mcgill毕业证书）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理加拿大麦吉尔大学毕业证（mcgill毕业证书）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理加拿大麦吉尔大学毕业证（mcgill毕业证书）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理加拿大麦吉尔大学毕业证（mcgill毕业证书）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理加拿大麦吉尔大学毕业证（mcgill毕业证书）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
一比一原版南昆士兰大学毕业证如何办理
原版一模一样【微信：741003700 】【南昆士兰大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理南昆士兰大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理南昆士兰大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理南昆士兰大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理南昆士兰大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
一比一原版(Sheffield毕业证书)谢菲尔德大学毕业证如何办理
原版制作【微信:41543339】【(Sheffield毕业证书)谢菲尔德大学毕业证】【微信:41543339】《成绩单、外壳、雅思、offer、留信学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器一制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
REUSE-SCHOOL-DATA-INTEGRATED-SYSTEMS.pptx
Enhanced data collection methods can help uncover the true extent of child abuse and neglect. This includes Integrated Data Systems from various sources (e.g., schools, healthcare providers, social services) to identify patterns and potential cases of abuse and neglect.
DSSML24_tspann_CodelessGenerativeAIPipelines
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
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Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
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This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
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This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
Experts live - Improving user adoption with AI
Bekijk de slides van onze sessie Enhancing Modern Workplace Efficiency op Experts Live 2024.
Cell The Unit of Life for NEET Multiple Choice Questions.docx
celltheunitoflife
ribosomes
thegolgi
nuclearmembrane
cilia
phragmoplast
原版一比一爱尔兰都柏林大学毕业证(UCD毕业证书)如何办理
原版制作【微信:41543339】【爱尔兰都柏林大学毕业证(UCD毕业证书)】【微信:41543339】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版爱尔兰都柏林大学毕业证(本硕）ucd学位证书如何办理
原版一模一样【微信：741003700 】【爱尔兰都柏林大学毕业证(本硕）ucd成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理爱尔兰都柏林大学毕业证(本硕）ucd学位证书【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理爱尔兰都柏林大学毕业证(本硕）ucd学位证书【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理爱尔兰都柏林大学毕业证(本硕）ucd学位证书【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理爱尔兰都柏林大学毕业证(本硕）ucd学位证书【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
一比一原版多伦多大学毕业证(UofT毕业证书)学历如何办理
办理【微信号:176555708】【办理(UofT毕业证书)】【微信号:176555708】《成绩单、外壳、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号:176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号:176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(曼大毕业证书)曼尼托巴大学毕业证如何办理
原版一模一样【微信：741003700 】【(曼大毕业证书)曼尼托巴大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(曼大毕业证书)曼尼托巴大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(曼大毕业证书)曼尼托巴大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(曼大毕业证书)曼尼托巴大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(曼大毕业证书)曼尼托巴大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
Jio cinema Retention & Engagement Strategy.pdf
Introduction to Jio Cinema**:
- Brief overview of Jio Cinema as a streaming platform.
- Its significance in the Indian market.
- Introduction to retention and engagement strategies in the streaming industry.
2. **Understanding Retention and Engagement**:
- Define retention and engagement in the context of streaming platforms.
- Importance of retaining users in a competitive market.
- Key metrics used to measure retention and engagement.
3. **Jio Cinema's Content Strategy**:
- Analysis of the content library offered by Jio Cinema.
- Focus on exclusive content, originals, and partnerships.
- Catering to diverse audience preferences (regional, genre-specific, etc.).
- User-generated content and interactive features.
4. **Personalization and Recommendation Algorithms**:
- How Jio Cinema leverages user data for personalized recommendations.
- Algorithmic strategies for suggesting content based on user preferences, viewing history, and behavior.
- Dynamic content curation to keep users engaged.
5. **User Experience and Interface Design**:
- Evaluation of Jio Cinema's user interface (UI) and user experience (UX).
- Accessibility features and device compatibility.
- Seamless navigation and search functionality.
- Integration with other Jio services.
6. **Community Building and Social Features**:
- Strategies for fostering a sense of community among users.
- User reviews, ratings, and comments.
- Social sharing and engagement features.
- Interactive events and campaigns.
7. **Retention through Loyalty Programs and Incentives**:
- Overview of loyalty programs and rewards offered by Jio Cinema.
- Subscription plans and benefits.
- Promotional offers, discounts, and partnerships.
- Gamification elements to encourage continued usage.
8. **Customer Support and Feedback Mechanisms**:
- Analysis of Jio Cinema's customer support infrastructure.
- Channels for user feedback and suggestions.
- Handling of user complaints and queries.
- Continuous improvement based on user feedback.
9. **Multichannel Engagement Strategies**:
- Utilization of multiple channels for user engagement (email, push notifications, SMS, etc.).
- Targeted marketing campaigns and promotions.
- Cross-promotion with other Jio services and partnerships.
- Integration with social media platforms.
10. **Data Analytics and Iterative Improvement**:
- Role of data analytics in understanding user behavior and preferences.
- A/B testing and experimentation to optimize engagement strategies.
- Iterative improvement based on data-driven insights.
一比一原版兰加拉学院毕业证(Langara毕业证书)学历如何办理
原版办【微信号:BYZS866】【兰加拉学院毕业证(Langara毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样
学校原件一模一样【微信：741003700 】《(unimelb毕业证书)墨尔本大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
社内勉強会資料_Hallucination of LLMs .
社内エンジニア・リサーチャー勉強会の発表資料「Hallucination of LLMs」を公開しました！
大規模言語モデル(LLM)に起こるハルシネーションのメカニズムと、それらを軽減する方法について紹介しています。
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From Python to PySpark and Back Again – Unifying Single-host and Distributed Deep Learning with Maggy
- 2. From Python to PySpark and Back Again - Unifying Single-host and Distributed Machine Learning with Maggy Moritz Meister, @morimeister Software Engineer, Logical Clocks Jim Dowling, @jim_dowling Associate Professor, KTH Royal Institute of Technology
- 3. ML Model Development A simplified view Exploration Experimentation Model Training Explainability and Validation Serving Feature Pipelines
- 4. ML Model Development Explore and Design Experimentation: Tune and Search Model Training (Distributed) Explainability and Ablation Studies It’s simple - only four steps
- 5. Artifacts and Non DRY Code Explore and Design Experimentation: Tune and Search Model Training (Distributed) Explainability and Ablation Studies
- 6. What It’s Really Like … not linear but iterative
- 7. What It’s Really Really Like … not linear but iterative
- 8. Root Cause: Iterative Development of ML Models Explore and Design Experimentation: Tune and Search Model Training (Distributed) Explainability and Ablation Studies
- 9. Ablation StudiesEDA HParam Tuning Training (Dist) Iterative Development Is a Pain, We Need DRY Code! Each step requires different implementations of the training code
- 10. OBLIVIOUS TRAINING FUNCTION # RUNS ON THE WORKERS def train(): def input_fn(): # return dataset model = … optimizer = … model.compile(…) rc = tf.estimator.RunConfig( ‘CollectiveAllReduceStrate gy’) keras_estimator = tf.keras.estimator. model_to_estimator(….) tf.estimator.train_and_evaluate( keras_estimator, input_fn) Ablation StudiesEDA HParam Tuning Training (Dist) The Oblivious Training Function
- 11. Challenge: Obtrusive Framework Artifacts ▪ TF_CONFIG ▪ Distribution Strategy ▪ Dataset (Sharding, DFS) ▪ Integration in Python - hard from inside a notebook ▪ Keras vs. Estimator vs. Custom Training Loop Example: TensorFlow
- 12. Where is Deep Learning headed?
- 13. Productive High-Level APIs Or why data scientists love Keras and PyTorch Idea Experiment Results Infrastructure Framework Tracking Visualization Francois Chollet, “Keras: The Next 5 Years”
- 14. Productive High-Level APIs Or why data scientists love Keras and PyTorch Idea Experiment Results Infrastructure Framework Tracking Visualization Francois Chollet, “Keras: The Next 5 Years” ? Hopsworks (Open Source) Databricks Apache Spark Cloud Providers
- 15. How do we keep our high-level APIs transparent and productive?
- 16. What Is Transparent Code? def dataset(batch_size): (x_train, y_train) = load_data() x_train = x_train / np.float32(255) y_train = y_train.astype(np.int64) train_dataset = tf.data.Dataset.from_tensor_slices( (x_train,y_train)).shuffle(60000) .repeat().batch(batch_size) return train_dataset def build_and_compile_cnn_model(lr): model = tf.keras.Sequential([ tf.keras.Input(shape=(28, 28)), tf.keras.layers.Conv2D(32, 3, activation='relu'), tf.keras.layers.Flatten(), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dense(10) ]) model.compile( loss=SparseCategoricalCrossentropy(from_logits=True), optimizer=SGD(learning_rate=lr)) return model def dataset(batch_size): (x_train, y_train) = load_data() x_train = x_train / np.float32(255) y_train = y_train.astype(np.int64) train_dataset = tf.data.Dataset.from_tensor_slices( (x_train,y_train)).shuffle(60000) .repeat().batch(batch_size) return train_dataset def build_and_compile_cnn_model(lr): model = tf.keras.Sequential([ tf.keras.Input(shape=(28, 28)), tf.keras.layers.Conv2D(32, 3, activation='relu'), tf.keras.layers.Flatten(), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dense(10) ]) model.compile( loss=SparseCategoricalCrossentropy(from_logits=True), optimizer=SGD(learning_rate=lr)) return model NO CHANGES!
- 17. Building Blocks for Distribution Transparency
- 18. Distribution Context Single-host vs. parallel multi-host vs. distributed multi-host Worker 1 Worker 5 Worker 3 Worker 2 Worker 4 Worker 7 Worker 8 Worker 6 Driver TF_CONFIG Driver Experiment Controller Worker 1 Worker NWorker 2 Single Host
- 19. Distribution Context Single-host vs. parallel multi-host vs. distributed multi-host Worker 1 Worker 5 Worker 3 Worker 2 Worker 4 Worker 7 Worker 8 Worker 6 Driver TF_CONFIG Driver Experiment Controller Worker 1 Worker NWorker 2 Single Host Explore and Design Experimentation: Tune and Search Model Training (Distributed) Explainability and Ablation Studies
- 20. Model Development Best Practices ▪ Modularize ▪ Parametrize ▪ Higher order training functions ▪ Usage of callbacks at runtime Dataset Generation Model Generation Training Logic
- 21. Oblivious Training Function as an Abstraction Let the system handle the complexities System takes care of ... … fixing parameters … launching the function … launching trials (parametrized instantiations of the function) … generating new trials … collecting and logging results … setting up TF_CONFIG … wrapping in Distribution Strategy … launching function as workers … collecting results
- 22. Maggy Spark+AI Summit 2019 Today With Hopsworks and Maggy, we provide a unified development and execution environment for distribution transparent ML model development. Make the Oblivious Training Function a core abstraction on Hopsworks
- 23. Hopsworks - Award Winning Plattform
- 24. Recap: Maggy - Asynchronous Trials on Spark Spark is bulk-synchronous Wasted Compute Wasted Compute HopsFS Barrier Task11 Task12 Task13 Task1N Driver Metrics1 Barrier Task21 Task22 Task23 Task2N Metrics2 Barrier Task31 Task32 Task33 Task3N Metrics3 Wasted Compute Early-Stopping
- 25. Recap: The Solution Add Communication and Long Running Tasks Task11 Task12 Task13 Task1N Driver Barrier Metrics New Trial
- 26. What’s New? Worker discovery and distribution context set-up Task11 Task12 Task13 Task1N Driver Barrier Launch Oblivious Training Function in Context Discover Workers
- 27. What’s New: Distribution Context sp = maggy.optimization.Searchspace(...) dist_strat = tf.keras.distribute.MirroredStrategy(...) ab = maggy.ablation.AblationStudy(...) maggy.set_context('optimization’) maggy.lagom(training_function, sp) maggy.set_context(‘distributed_training’) maggy.lagom(training_function, dist_strat) maggy.set_context(‘ablation’) maggy.lagom(training_function, ab)
- 28. DEMO
- 29. What’s Next Extend the platform to provide a unified development and execution environment for distribution transparent Jupyter Notebooks.
- 30. Summary ▪ Moving between distribution contexts requires code rewriting ▪ Factor out obtrusive framework artifacts ▪ Let system handle distribution context ▪ Keep productive high-level APIs
- 31. Thank You! Get Started hopsworks.ai github.com/logicalclocks/maggy Twitter @morimeister @jim_dowling @logicalclocks @hopsworks Web www.logicalclocks.com Contributions from colleagues ▪ Sina Sheikholeslami ▪ Robin Andersson ▪ Alex Ormenisan ▪ Kai Jeggle Thanks to the Logical Clocks Team!
- 32. Feedback Your feedback is important to us. Don’t forget to rate and review the sessions.