Reproducible Data Science With Pachyderm

Author : Svetlana Karslioglu
Publisher : Packt Publishing
Page : 364 pages
File Size : 51,9 Mb
Release : 2022-03-18
Category : Electronic
ISBN : 1801074488

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Reproducible Data Science with Pachyderm by Svetlana Karslioglu Pdf

Create scalable and reliable data pipelines easily with Pachyderm Key Features: Learn how to build an enterprise-level reproducible data science platform with Pachyderm Deploy Pachyderm on cloud platforms such as AWS EKS, Google Kubernetes Engine, and Microsoft Azure Kubernetes Service Integrate Pachyderm with other data science tools, such as Pachyderm Notebooks Book Description: Pachyderm is an open source project that enables data scientists to run reproducible data pipelines and scale them to an enterprise level. This book will teach you how to implement Pachyderm to create collaborative data science workflows and reproduce your ML experiments at scale. You'll begin your journey by exploring the importance of data reproducibility and comparing different data science platforms. Next, you'll explore how Pachyderm fits into the picture and its significance, followed by learning how to install Pachyderm locally on your computer or a cloud platform of your choice. You'll then discover the architectural components and Pachyderm's main pipeline principles and concepts. The book demonstrates how to use Pachyderm components to create your first data pipeline and advances to cover common operations involving data, such as uploading data to and from Pachyderm to create more complex pipelines. Based on what you've learned, you'll develop an end-to-end ML workflow, before trying out the hyperparameter tuning technique and the different supported Pachyderm language clients. Finally, you'll learn how to use a SaaS version of Pachyderm with Pachyderm Notebooks. By the end of this book, you will learn all aspects of running your data pipelines in Pachyderm and manage them on a day-to-day basis. What You Will Learn: Understand the importance of reproducible data science for enterprise Explore the basics of Pachyderm, such as commits and branches Upload data to and from Pachyderm Implement common pipeline operations in Pachyderm Create a real-life example of hyperparameter tuning in Pachyderm Combine Pachyderm with Pachyderm language clients in Python and Go Who this book is for: This book is for new as well as experienced data scientists and machine learning engineers who want to build scalable infrastructures for their data science projects. Basic knowledge of Python programming and Kubernetes will be beneficial. Familiarity with Golang will be helpful.

Author : Svetlana Karslioglu
Publisher : Packt Publishing Ltd
Page : 365 pages
File Size : 41,5 Mb
Release : 2022-03-18
Category : Computers
ISBN : 9781801079075

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Reproducible Data Science with Pachyderm by Svetlana Karslioglu Pdf

Create scalable and reliable data pipelines easily with Pachyderm Key FeaturesLearn how to build an enterprise-level reproducible data science platform with PachydermDeploy Pachyderm on cloud platforms such as AWS EKS, Google Kubernetes Engine, and Microsoft Azure Kubernetes ServiceIntegrate Pachyderm with other data science tools, such as Pachyderm NotebooksBook Description Pachyderm is an open source project that enables data scientists to run reproducible data pipelines and scale them to an enterprise level. This book will teach you how to implement Pachyderm to create collaborative data science workflows and reproduce your ML experiments at scale. You'll begin your journey by exploring the importance of data reproducibility and comparing different data science platforms. Next, you'll explore how Pachyderm fits into the picture and its significance, followed by learning how to install Pachyderm locally on your computer or a cloud platform of your choice. You'll then discover the architectural components and Pachyderm's main pipeline principles and concepts. The book demonstrates how to use Pachyderm components to create your first data pipeline and advances to cover common operations involving data, such as uploading data to and from Pachyderm to create more complex pipelines. Based on what you've learned, you'll develop an end-to-end ML workflow, before trying out the hyperparameter tuning technique and the different supported Pachyderm language clients. Finally, you'll learn how to use a SaaS version of Pachyderm with Pachyderm Notebooks. By the end of this book, you will learn all aspects of running your data pipelines in Pachyderm and manage them on a day-to-day basis. What you will learnUnderstand the importance of reproducible data science for enterpriseExplore the basics of Pachyderm, such as commits and branchesUpload data to and from PachydermImplement common pipeline operations in PachydermCreate a real-life example of hyperparameter tuning in PachydermCombine Pachyderm with Pachyderm language clients in Python and GoWho this book is for This book is for new as well as experienced data scientists and machine learning engineers who want to build scalable infrastructures for their data science projects. Basic knowledge of Python programming and Kubernetes will be beneficial. Familiarity with Golang will be helpful.

Author : Dan Meador,Kevin Goldsmith
Publisher : Packt Publishing Ltd
Page : 330 pages
File Size : 44,8 Mb
Release : 2022-05-27
Category : Computers
ISBN : 9781800561564

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Building Data Science Solutions with Anaconda by Dan Meador,Kevin Goldsmith Pdf

The missing manual to becoming a successful data scientist—develop the skills to use key tools and the knowledge to thrive in the AI/ML landscape Key Features • Learn from an AI patent-holding engineering manager with deep experience in Anaconda tools and OSS • Get to grips with critical aspects of data science such as bias in datasets and interpretability of models • Gain a deeper understanding of the AI/ML landscape through real-world examples and practical analogies Book Description You might already know that there's a wealth of data science and machine learning resources available on the market, but what you might not know is how much is left out by most of these AI resources. This book not only covers everything you need to know about algorithm families but also ensures that you become an expert in everything, from the critical aspects of avoiding bias in data to model interpretability, which have now become must-have skills. In this book, you'll learn how using Anaconda as the easy button, can give you a complete view of the capabilities of tools such as conda, which includes how to specify new channels to pull in any package you want as well as discovering new open source tools at your disposal. You'll also get a clear picture of how to evaluate which model to train and identify when they have become unusable due to drift. Finally, you'll learn about the powerful yet simple techniques that you can use to explain how your model works. By the end of this book, you'll feel confident using conda and Anaconda Navigator to manage dependencies and gain a thorough understanding of the end-to-end data science workflow. What you will learn • Install packages and create virtual environments using conda • Understand the landscape of open source software and assess new tools • Use scikit-learn to train and evaluate model approaches • Detect bias types in your data and what you can do to prevent it • Grow your skillset with tools such as NumPy, pandas, and Jupyter Notebooks • Solve common dataset issues, such as imbalanced and missing data • Use LIME and SHAP to interpret and explain black-box models Who this book is for If you're a data analyst or data science professional looking to make the most of Anaconda's capabilities and deepen your understanding of data science workflows, then this book is for you. You don't need any prior experience with Anaconda, but a working knowledge of Python and data science basics is a must.

Author : Ross Brigoli,Faisal Masood
Publisher : Packt Publishing Ltd
Page : 238 pages
File Size : 41,5 Mb
Release : 2024-01-31
Category : Computers
ISBN : 9781805125853

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MLOps with Red Hat OpenShift by Ross Brigoli,Faisal Masood Pdf

Build and manage MLOps pipelines with this practical guide to using Red Hat OpenShift Data Science, unleashing the power of machine learning workflows Key Features Grasp MLOps and machine learning project lifecycle through concept introductions Get hands on with provisioning and configuring Red Hat OpenShift Data Science Explore model training, deployment, and MLOps pipeline building with step-by-step instructions Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMLOps with OpenShift offers practical insights for implementing MLOps workflows on the dynamic OpenShift platform. As organizations worldwide seek to harness the power of machine learning operations, this book lays the foundation for your MLOps success. Starting with an exploration of key MLOps concepts, including data preparation, model training, and deployment, you’ll prepare to unleash OpenShift capabilities, kicking off with a primer on containers, pods, operators, and more. With the groundwork in place, you’ll be guided to MLOps workflows, uncovering the applications of popular machine learning frameworks for training and testing models on the platform. As you advance through the chapters, you’ll focus on the open-source data science and machine learning platform, Red Hat OpenShift Data Science, and its partner components, such as Pachyderm and Intel OpenVino, to understand their role in building and managing data pipelines, as well as deploying and monitoring machine learning models. Armed with this comprehensive knowledge, you’ll be able to implement MLOps workflows on the OpenShift platform proficiently.What you will learn Build a solid foundation in key MLOps concepts and best practices Explore MLOps workflows, covering model development and training Implement complete MLOps workflows on the Red Hat OpenShift platform Build MLOps pipelines for automating model training and deployments Discover model serving approaches using Seldon and Intel OpenVino Get to grips with operating data science and machine learning workloads in OpenShift Who this book is for This book is for MLOps and DevOps engineers, data architects, and data scientists interested in learning the OpenShift platform. Particularly, developers who want to learn MLOps and its components will find this book useful. Whether you’re a machine learning engineer or software developer, this book serves as an essential guide to building scalable and efficient machine learning workflows on the OpenShift platform.

Author : Amit Bhanushali,Alekhya Achanta,Beena Bhanushali
Publisher : BPB Publications
Page : 254 pages
File Size : 53,5 Mb
Release : 2024-03-19
Category : Computers
ISBN : 9789355519726

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Continuous Integration and Delivery with Test-driven Development by Amit Bhanushali,Alekhya Achanta,Beena Bhanushali Pdf

Building tomorrow, today: Seamless integration, continuous deliver KEY FEATURES ● Step-by-step guidance to construct automated software and data CI/CD pipelines. ● Real-world case studies demonstrating CI/CD best practices across diverse organizations and development environments. ● Actionable frameworks to instill an organizational culture of collaboration, quality, and rapid iteration grounded in TDD values. DESCRIPTION As software complexity grows, quality and delivery speed increasingly rely on automated pipelines. This practical guide equips readers to construct robust CI/CD workflows that boost productivity and reliability. Step-by-step walkthroughs detail the technical implementation of continuous practices, while real-world case studies showcase solutions tailored for diverse systems and organizational needs. Master CI/CD, crucial for modern software development, with this book. It compares traditional versus test-driven development, stressing testing's importance. In this book, we will explore CI/CD's principles, benefits, and DevOps integration. We will build robust pipelines covering containerization, version control, and infrastructure as code. Through this book, you will learn about effective CD with monitoring, security, and release management, you will learn how to optimize CI/CD for different scenarios and applications, emphasizing collaboration and automation for success. With actionable best practices grounded in TDD principles, this book teaches how to leverage automated processes to cultivate shared ownership, design simplicity, comprehensive testing, and ultimately deliver exceptional business value. WHAT YOU WILL LEARN ● Construct smooth automated CI/CD pipelines tailored for complex systems. ● Master implementation strategies for diverse development environments. ● Design comprehensive test suites leveraging leading tools and frameworks. ● Instill a collaborative culture grounded in TDD values for ownership and simplicity. ● Optimize release processes for efficiency, quality, and business alignment. WHO THIS BOOK IS FOR This book is ideal for software engineers, developers, testers, and technical leads seeking to improve their CI/CD proficiency. Whether you are starting to explore the tool or looking to deepen your understanding, this book is a valuable resource for anyone eager to learn and master the technology. TABLE OF CONTENTS 1. Adopting a Test-driven Development Mindset 2. Understanding CI/CD Concepts 3. Building the CI/CD Pipeline 4. Ensuring Effective CD 5. Optimizing CI/CD Practices 6. Specialized CI/CD Applications 7. Model Operations: DevOps Pipeline Case Studies 8. Data CI/CD: Emerging Trends and Roles

Author : Ulrika Jagare
Publisher : John Wiley & Sons
Page : 237 pages
File Size : 43,5 Mb
Release : 2022-04-19
Category : Computers
ISBN : 9781119833215

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Operating AI by Ulrika Jagare Pdf

A holistic and real-world approach to operationalizing artificial intelligence in your company In Operating AI, Director of Technology and Architecture at Ericsson AB, Ulrika Jägare, delivers an eye-opening new discussion of how to introduce your organization to artificial intelligence by balancing data engineering, model development, and AI operations. You'll learn the importance of embracing an AI operational mindset to successfully operate AI and lead AI initiatives through the entire lifecycle, including key areas such as; data mesh, data fabric, aspects of security, data privacy, data rights and IPR related to data and AI models. In the book, you’ll also discover: How to reduce the risk of entering bias in our artificial intelligence solutions and how to approach explainable AI (XAI) The importance of efficient and reproduceable data pipelines, including how to manage your company's data An operational perspective on the development of AI models using the MLOps (Machine Learning Operations) approach, including how to deploy, run and monitor models and ML pipelines in production using CI/CD/CT techniques, that generates value in the real world Key competences and toolsets in AI development, deployment and operations What to consider when operating different types of AI business models With a strong emphasis on deployment and operations of trustworthy and reliable AI solutions that operate well in the real world—and not just the lab—Operating AI is a must-read for business leaders looking for ways to operationalize an AI business model that actually makes money, from the concept phase to running in a live production environment.

Author : Ramin Homayouni,Huixiao Hong,Prashanti Manda,Bindu Nanduri,Inimary Toby
Publisher : Frontiers Media SA
Page : 90 pages
File Size : 54,9 Mb
Release : 2022-07-07
Category : Science
ISBN : 9782889765393

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Unleashing Innovation on Precision Public Health: Highlights from the MCBIOS & MAQC 2021 Joint Conference by Ramin Homayouni,Huixiao Hong,Prashanti Manda,Bindu Nanduri,Inimary Toby Pdf

Author : Robert Winkler
Publisher : Royal Society of Chemistry
Page : 460 pages
File Size : 55,7 Mb
Release : 2020-03-19
Category : Science
ISBN : 9781788017213

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Processing Metabolomics and Proteomics Data with Open Software by Robert Winkler Pdf

Metabolomics and proteomics allow deep insights into the chemistry and physiology of biological systems. This book expounds open-source programs, platforms and programming tools for analysing metabolomics and proteomics mass spectrometry data. In contrast to commercial software, open-source software is created by the academic community, which facilitates the direct interaction between users and developers and accelerates the implementation of new concepts and ideas. The first section of the book covers the basics of mass spectrometry, experimental strategies, data operations, the open-source philosophy, metabolomics, proteomics and statistics/ data mining. In the second section, active programmers and users describe available software packages. Included tutorials, datasets and code examples can be used for training and for building custom workflows. Finally, every reader is invited to participate in the open science movement.

Author : Harvinder Atwal
Publisher : Apress
Page : 289 pages
File Size : 51,9 Mb
Release : 2019-12-09
Category : Computers
ISBN : 9781484251041

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Practical DataOps by Harvinder Atwal Pdf

Gain a practical introduction to DataOps, a new discipline for delivering data science at scale inspired by practices at companies such as Facebook, Uber, LinkedIn, Twitter, and eBay. Organizations need more than the latest AI algorithms, hottest tools, and best people to turn data into insight-driven action and useful analytical data products. Processes and thinking employed to manage and use data in the 20th century are a bottleneck for working effectively with the variety of data and advanced analytical use cases that organizations have today. This book provides the approach and methods to ensure continuous rapid use of data to create analytical data products and steer decision making. Practical DataOps shows you how to optimize the data supply chain from diverse raw data sources to the final data product, whether the goal is a machine learning model or other data-orientated output. The book provides an approach to eliminate wasted effort and improve collaboration between data producers, data consumers, and the rest of the organization through the adoption of lean thinking and agile software development principles. This book helps you to improve the speed and accuracy of analytical application development through data management and DevOps practices that securely expand data access, and rapidly increase the number of reproducible data products through automation, testing, and integration. The book also shows how to collect feedback and monitor performance to manage and continuously improve your processes and output. What You Will LearnDevelop a data strategy for your organization to help it reach its long-term goals Recognize and eliminate barriers to delivering data to users at scale Work on the right things for the right stakeholders through agile collaboration Create trust in data via rigorous testing and effective data management Build a culture of learning and continuous improvement through monitoring deployments and measuring outcomes Create cross-functional self-organizing teams focused on goals not reporting lines Build robust, trustworthy, data pipelines in support of AI, machine learning, and other analytical data products Who This Book Is For Data science and advanced analytics experts, CIOs, CDOs (chief data officers), chief analytics officers, business analysts, business team leaders, and IT professionals (data engineers, developers, architects, and DBAs) supporting data teams who want to dramatically increase the value their organization derives from data. The book is ideal for data professionals who want to overcome challenges of long delivery time, poor data quality, high maintenance costs, and scaling difficulties in getting data science output and machine learning into customer-facing production.

Author : Daniel Whitenack
Publisher : Packt Publishing Ltd
Page : 293 pages
File Size : 49,8 Mb
Release : 2017-09-26
Category : Computers
ISBN : 9781785883903

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Machine Learning With Go by Daniel Whitenack Pdf

Build simple, maintainable, and easy to deploy machine learning applications. About This Book Build simple, but powerful, machine learning applications that leverage Go's standard library along with popular Go packages. Learn the statistics, algorithms, and techniques needed to successfully implement machine learning in Go Understand when and how to integrate certain types of machine learning model in Go applications. Who This Book Is For This book is for Go developers who are familiar with the Go syntax and can develop, build, and run basic Go programs. If you want to explore the field of machine learning and you love Go, then this book is for you! Machine Learning with Go will give readers the practical skills to perform the most common machine learning tasks with Go. Familiarity with some statistics and math topics is necessary. What You Will Learn Learn about data gathering, organization, parsing, and cleaning. Explore matrices, linear algebra, statistics, and probability. See how to evaluate and validate models. Look at regression, classification, clustering. Learn about neural networks and deep learning Utilize times series models and anomaly detection. Get to grip with techniques for deploying and distributing analyses and models. Optimize machine learning workflow techniques In Detail The mission of this book is to turn readers into productive, innovative data analysts who leverage Go to build robust and valuable applications. To this end, the book clearly introduces the technical aspects of building predictive models in Go, but it also helps the reader understand how machine learning workflows are being applied in real-world scenarios. Machine Learning with Go shows readers how to be productive in machine learning while also producing applications that maintain a high level of integrity. It also gives readers patterns to overcome challenges that are often encountered when trying to integrate machine learning in an engineering organization. The readers will begin by gaining a solid understanding of how to gather, organize, and parse real-work data from a variety of sources. Readers will then develop a solid statistical toolkit that will allow them to quickly understand gain intuition about the content of a dataset. Finally, the readers will gain hands-on experience implementing essential machine learning techniques (regression, classification, clustering, and so on) with the relevant Go packages. Finally, the reader will have a solid machine learning mindset and a powerful Go toolkit of techniques, packages, and example implementations. Style and approach This book connects the fundamental, theoretical concepts behind Machine Learning to practical implementations using the Go programming language.

Author : Hannes Hapke,Catherine Nelson
Publisher : "O'Reilly Media, Inc."
Page : 398 pages
File Size : 41,8 Mb
Release : 2020-07-13
Category : Computers
ISBN : 9781492053149

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Building Machine Learning Pipelines by Hannes Hapke,Catherine Nelson Pdf

Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipeline Build your pipeline using components from TensorFlow Extended Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation and TensorFlow Transform Analyze a model in detail using TensorFlow Model Analysis Examine fairness and bias in your model performance Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices Learn privacy-preserving machine learning techniques

Author : Sam Kean
Publisher : Little, Brown
Page : 400 pages
File Size : 54,7 Mb
Release : 2010-07-12
Category : Science
ISBN : 0316089087

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The Disappearing Spoon by Sam Kean Pdf

From New York Times bestselling author Sam Kean comes incredible stories of science, history, finance, mythology, the arts, medicine, and more, as told by the Periodic Table. Why did Gandhi hate iodine (I, 53)? How did radium (Ra, 88) nearly ruin Marie Curie's reputation? And why is gallium (Ga, 31) the go-to element for laboratory pranksters?* The Periodic Table is a crowning scientific achievement, but it's also a treasure trove of adventure, betrayal, and obsession. These fascinating tales follow every element on the table as they play out their parts in human history, and in the lives of the (frequently) mad scientists who discovered them. THE DISAPPEARING SPOON masterfully fuses science with the classic lore of invention, investigation, and discovery--from the Big Bang through the end of time. *Though solid at room temperature, gallium is a moldable metal that melts at 84 degrees Fahrenheit. A classic science prank is to mold gallium spoons, serve them with tea, and watch guests recoil as their utensils disappear.

Author : Craig Johnston
Publisher : Apress
Page : 0 pages
File Size : 43,6 Mb
Release : 2020-09-18
Category : Computers
ISBN : 1484256107

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Advanced Platform Development with Kubernetes by Craig Johnston Pdf

Leverage Kubernetes for the rapid adoption of emerging technologies. Kubernetes is the future of enterprise platform development and has become the most popular, and often considered the most robust, container orchestration system available today. This book focuses on platforming technologies that power the Internet of Things, Blockchain, Machine Learning, and the many layers of data and application management supporting them. Advanced Platform Development with Kubernetes takes you through the process of building platforms with these in-demand capabilities. You'll progress through the development of Serverless, CICD integration, data processing pipelines, event queues, distributed query engines, modern data warehouses, data lakes, distributed object storage, indexing and analytics, data routing and transformation, query engines, and data science/machine learning environments. You’ll also see how to implement and tie together numerous essential and trending technologies including: Kafka, NiFi, Airflow, Hive, Keycloak, Cassandra, MySQL, Zookeeper, Mosquitto, Elasticsearch, Logstash, Kibana, Presto, Mino, OpenFaaS, and Ethereum. The book uses Golang and Python to demonstrate the development integration of custom container and Serverless functions, including interaction with the Kubernetes API. The exercises throughout teach Kubernetes through the lens of platform development, expressing the power and flexibility of Kubernetes with clear and pragmatic examples. Discover why Kubernetes is an excellent choice for any individual or organization looking to embark on developing a successful data and application platform. What You'll Learn Configure and install Kubernetes and k3s on vendor-neutral platforms, including generic virtual machines and bare metal Implement an integrated development toolchain for continuous integration and deployment Use data pipelines with MQTT, NiFi, Logstash, Kafka and Elasticsearch Install a serverless platform with OpenFaaS Explore blockchain network capabilities with Ethereum Support a multi-tenant data science platform and web IDE with JupyterHub, MLflow and Seldon Core Build a hybrid cluster, securely bridging on-premise and cloud-based Kubernetes nodes Who This Book Is For System and software architects, full-stack developers, programmers, and DevOps engineers with some experience building and using containers. This book also targets readers who have started with Kubernetes and need to progress from a basic understanding of the technology and "Hello World" example to more productive, career-building projects.

Author : Russell Jurney
Publisher : "O'Reilly Media, Inc."
Page : 177 pages
File Size : 46,9 Mb
Release : 2013-10-15
Category : Computers
ISBN : 9781449326920

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Agile Data Science by Russell Jurney Pdf

Mining big data requires a deep investment in people and time. How can you be sure you’re building the right models? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop. Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps. Create analytics applications by using the agile big data development methodology Build value from your data in a series of agile sprints, using the data-value stack Gain insight by using several data structures to extract multiple features from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future, and translate predictions into action Get feedback from users after each sprint to keep your project on track

Author : Brendan Burns,Eddie Villalba,Dave Strebel,Lachlan Evenson
Publisher : "O'Reilly Media, Inc."
Page : 281 pages
File Size : 48,9 Mb
Release : 2019-11-14
Category : Computers
ISBN : 9781492056423

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Kubernetes Best Practices by Brendan Burns,Eddie Villalba,Dave Strebel,Lachlan Evenson Pdf

In this practical guide, four Kubernetes professionals with deep experience in distributed systems, enterprise application development, and open source will guide you through the process of building applications with this container orchestration system. Based on the experiences of companies that are running Kubernetes in production successfully, many of the methods are also backed by concrete code examples. This book is ideal for those already familiar with basic Kubernetes concepts who want to learn common best practices. You’ll learn exactly what you need to know to build your best app with Kubernetes the first time. Set up and develop applications in Kubernetes Learn patterns for monitoring, securing your systems, and managing upgrades, rollouts, and rollbacks Understand Kubernetes networking policies and where service mesh fits in Integrate services and legacy applications and develop higher-level platforms on top of Kubernetes Run machine learning workloads in Kubernetes