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ENbook.pl

pImplementing and designing systems that make suggestions to users are among the most popular and essential machine learning applications available. Whether you want customers to find the most appealing items at your online store, videos to enrich and entertain them, or news they need to know, recommendation systems RecSys provide the way. p In this practical book, authors Bryan Bischof and Hector Yee illustrate the core concepts and examples to help you create a RecSys for any industry or scale. You'll learn the math, ideas, and implementation details you need to succeed. This book includes the RecSys platform components, relevant MLOps tools in your stack, plus code examples and helpful suggestions in PySpark, SparkSQL, FastAPI, Weights  Biases, and Kafka. p You'll learn ul liThe data essential for building a RecSys liHow to frame your data and business as a RecSys problem liWays to evaluate models appropriate for your system liMethods to implement, train, test, and deploy the model 

Building Recommendation Systems in Python and Jax | Bischof Bryan | Twarda

pstrongTransform your machine learning projects into successful deployments with this practical guide on how to build and scale solutions that solve real-world problemsstrongppbrppstrongIncludes a new chapter on generative AI and large language models LLMs and building a pipeline that leverages LLMs using LangChainstrongppbrppstrongKey Features strongppbrpulliThis second edition delves deeper into key machine learning topics, CICD, and system designliliExplore core MLOps practices, such as model management and performance monitoringliliBuild end-to-end examples of deployable ML microservices and pipelines using AWS and open-source toolsliulpbrppstrongBook Description strongppbrppMachine Learning Engineering with Python, 2nd Edition, is the practical guide that MLOps and ML engineers need to build robust solutions to solve real-world problems, providing you with the skills and knowledge you need to stay ahead in this rapidly evolving field.ppbrppThe book takes a hands-on, examples-focus

Machine Learning Engineering with Python | Second Edition | McMahon Andrew | Twarda

pGetting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is and how it differs from DevOps and shows you how to put it into practice to operationalize your machine learning models. p Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods along with AutoML and monitoring and logging, then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start. p You'll discover how to ul liApply DevOps best practices to machine learning liBuild production machine learning systems and maintain them liMonitor, instrument, load-test, and operationalize 

Practical Mlops | Gift Noah | Twarda

pWith demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production. p Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you'll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization's needs. p You'll learn how to provide rapid business value while answering dynamic MLOps requirements. This book will help you ul liLearn the MLOps process, including its technological

Implementing Mlops in the Enterprise | Haviv Yaron | Twarda

pstrongWork seamlessly with production-ready machine learning systems and pipelines on AWS by addressing key pain points encountered in the ML life cyclestrongppbrppstrongKey Features strongpulliGain practical knowledge of managing ML workloads on AWS using Amazon SageMaker, Amazon EKS, and moreliliUse container and serverless services to solve a variety of ML engineering requirementsliliDesign, build, and secure automated MLOps pipelines and workflows on AWSliulpbrppstrongBook Description strongppThere is a growing need for professionals with experience in working on machine learning ML engineering requirements as well as those with knowledge of automating complex MLOps pipelines in the cloud. This book explores a variety of AWS services, such as Amazon Elastic Kubernetes Service, AWS Glue, AWS Lambda, Amazon Redshift, and AWS Lake Formation, which ML practitioners can leverage to meet various data engineering and ML engineering requirements in production.ppThis machine learning book 

Machine Learning Engineering on AWS | Lat Joshua Arvin | Twarda

pstrongTrain, test, run, track, store, tune, deploy, and explain provenance-aware deep learning models and pipelines at scale with reproducibility using MLflowstrongppbrppstrongKey Features strongpulliFocus on deep learning models and MLflow to develop practical business AI solutions at scaleliliShip deep learning pipelines from experimentation to production with provenance trackingliliLearn to train, run, tune and deploy deep learning pipelines with explainability and reproducibilityliulpbrppstrongBook Description strongppThe book starts with an overview of the deep learning DL life cycle and the emerging Machine Learning Ops MLOps field, providing a clear picture of the four pillars of deep learning data, model, code, and explainability and the role of MLflow in these areas.ppFrom there onward, it guides you step by step in understanding the concept of MLflow experiments and usage patterns, using MLflow as a unified framework to track DL data, code and pipelines, models, parameters, 

Practical Deep Learning at Scale with MLflow | Liu Yong | Twarda

pstrongAutomate the process of building, training, and deploying machine learning applications to production with AWS solutions such as SageMaker Autopilot, AutoGluon, Step Functions, Amazon Managed Workflows for Apache Airflow, and morestrongppbrppstrongKey Features strongpulliExplore the various AWS services that make automated machine learning easierliliRecognize the role of DevOps and MLOps methodologies in pipeline automationliliGet acquainted with additional AWS services such as Step Functions, MWAA, and more to overcome automation challengesliulpbrppstrongBook Description strongppAWS provides a wide range of solutions to help automate a machine learning workflow with just a few lines of code. With this practical book, you'll learn how to automate a machine learning pipeline using the various AWS services.ppAutomated Machine Learning on AWS begins with a quick overview of what the machine learning pipelineprocess looks like and highlights the typical challenges that you may face 

Automated Machine Learning on AWS | Potgieter Trenton | Twarda

pstrongFully build and productionize end-to-end machine learning solutions using Azure Machine Learning ServicestrongppbrppstrongKey Features strongpulliAutomate complete machine learning solutions using Microsoft AzureliliUnderstand how to productionize machine learning modelsliliGet to grips with monitoring, MLOps, deep learning, distributed training, and reinforcement learningliulpbrppstrongBook Description strongppData scientists working on productionizing machine learning ML workloads face a breadth of challenges at every step owing to the countless factors involved in getting ML models deployed and running. This book offers solutions to common issues, detailed explanations of essential concepts, and step-by-step instructions to productionize ML workloads using the Azure Machine Learning service. You'll see how data scientists and ML engineers working with Microsoft Azure can train and deploy ML models at scale by putting their knowledge to work with this practical guide.ppThrough

Azure Machine Learning Engineering | Fakhraee Sina | Twarda

Brandosa

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Inez Pants Blue, Female, Odzież, podstawowe warstwy, Niebieski, XS/S

Caliroots

Mammoth Large Night Camo/black, Unisex, Ekwipunek, Torby, Czarny, ONESIZE

Trampki Jenny Fairy Kremowy Aż do 120 dni na zwrot  dla Klubowiczów!

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E.M.P.

KIHILIST by KILLSTAR - T-Shirt - Moonlit Reaper T-Shirt - 100% bawełna - XS, S, M, L, XL, XXL, 3XL, 4XL - czarny - duży nadruk z przodu - dekolt: okrągły - krój: regularny

Koszulka KIHILIST by KILLSTAR - T-shirt Moonlit Reaper T-Shirt - Mężczyźni - czarny

pHow can music withstand the death and destruction brought on by war Global conflicts of the 20th century fundamentally transformed not only national boundaries, power relations, and global economies, but also the arts and culture of every nation involved. An important, unacknowledged aspect of these conflicts is that they have unique musical soundtracks. iMusic in World War IIi explores how music and sound took on radically different dimensions in the United States and Europe before, during, and after World War II. Additionally, the collection examines the impact of radio and film as the disseminators of the war's musical soundtrack. Contributors contend that the European and American soundtrack of World War II was largely one of escapism rather than the lofty, solemn, heroic, and celebratory mode of war music in the past. Furthermore, they explore the variety of experiences of populations forced from their homes and interned in civilian and POW camps in Europe and the United States, 

Music in World War II | Potter Pamela M. | Twarda

ENdisc.pl

EAN: 0134120182022 "Kev Brown" Redefinition Records

"Homework" ("Kev Brown") (Vinyl / 12" Album)

EYERIm

Ray-Ban RB3445 002/58 Polarized, Spolaryzowane, Czarne, Materiał 1, Męskie okulary przeciwsłoneczne

Okulary przeciwsłoneczne > Męskie > Rectangular > Ray-Ban, RB3445 002/58 Polarized

Footway

Buty w swobodnym, sportowym stylu dla aktywnych.Wybierz buty adidas i ruszaj pobiegać po parku, a potem wyskocz na kawę z przyjaciółmi. Cholewka z siateczki zapewnia przewiewność i gwarantuje komfort przez cały dzień. Trwała gumowa podeszwa zewnętrzna stanowi solidną podstawę, która sprawdzi się bez względu na to, jak bardzo zabiegany jest Twój dzień.Standardowy krójModel sznurowanyCholewka z warstwowej siateczkiPoczucie lekkiego i przewiewnego komfortuPodeszwa środkowa z pianki EVAWspierająca, bezszwowa pięta

Runfalcon 2.0 Grey Five/silver Met./grey Two, Female, Buty, Sneakersy, buty sportowe, Szary/Niebieski, EU 42

Horze

Oficerki Verona są nie tylko eleganckie, ale także praktyczne - idealne na zawody, kiedy musisz wyglądać wspaniale i dać z siebie w wszystko.Obie odpinane lamówki posiadają małą, srebrną przypinkę z logo Horze.Gładka skóra po zewnętrznej stronie i tłoczona skóra po wewnętrznej stronie łączą się tworząc wysoce profesjonalne oficerki.Elastyczna wstawka, która biegnie wzdłuż cholewki sprawia, że oficerki leżą jak ulał.Stopa ozdobiona jest paskiem z dziurkami, elastycznymi, zamocowanymi na stałe sznurowadłami i posiada przydatną podpórkę na ostrogę.Skórzane paski zapinane na zatrzask na górze i dole suwaka gwarantują skuteczne zapięcie oficerek.

Oficerki damskie skórzane Horze Verona

NA-KD

Ta góra bikini ma kwadratowy dekolt i zdejmowane wyściełane miseczki. Ma szerokie ramiączka i regulowaną haftkę z tyłu.

NA-KD Gzęsty top od bikini z kwadratowym dekoltem - Bikini - Różowy - M (EU 38-40) - NA-KD / NAKD

Runforest

Wydajna cholewka z siateczki zapewnia lekkość i przewiewność Piankowa wyściółka umieszczona wokół kołnierza kostki i pod językiem zapewnia niewiarygodnie wygodne dopasowanie i czucie Wkładka z pianki Deluxe dopasowuje się do stopy, zapewniając dodatkowy komfort pod stopami Podeszwa środkowa Charged Cushioning® wykorzystuje formowaną kompresyjnie piankę, zapewniając najwyższą responsywność i trwałość Trwały wzór podeszwy zewnętrznej zapewnia elastyczność i przyczepność

Ua Charged Pursuit 3 White, Male, Buty, Sneakersy, Sneakersy, Biały, EU 40

Sportamore

Uzyskaj osłonę, której potrzebujesz, wychodząc na całość w staniku sportowym Nike Trophy. Ten niezbędny na co dzień jest zasilany technologią Dri-FIT, dzięki czemu możesz grać z pewnością siebie.

Trophy Bra Junior Pink, Unisex, Odzież, Tops, Trening, Różowy, M

StreetStyle24

Spodnie męskie Lacoste Tracksuit Trousers Bottom XH9624-CCA – szare Męskie spodnie dresowe Lacoste Tracksuit Trousers Bottom. Zostały wykonane z wysokiej jakości materiału, który gwarantuje komfort użytkowania. Odzież posiada klasyczny krój z elastyczną, regulowaną za pomocą sznurka talią. Nogawki zakończone ściągaczem świetnie wyeksponują Twoje buty i zapewnią optymalne dopasowanie, a boczne kieszenie zmieszczą niezbędne drobiazgi. Męskie spodnie dresowe zadowolą fanów miejskiego stylu. Model ten świetnie dopełni męskie bluzy. W takim zestawie będziesz mógł wyruszyć na podbój miejskiej dżungli. Specyfikacja: - materiał: 84% bawełna, 16% poliester - klasyczny krój - elastyczna talia regulowana za pomocą sznurka - boczne kieszenie - nogawki zakończone ściągaczem - branding producentaPodmiot odpowiedzialny: Devanlay Polska Spółka z o. o. ul. Złota 59, 00-120 Warszawa, Polska

practical mlops