Aymen el amri (51 risultati)

Paperback or Softback. Condizione: New. Learn Git in a Day: Everything you need, nothing you don't. Book.

Condizione: New.

Condizione: Very Good. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Paperback. Condizione: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Paperback. Condizione: Good.

Condizione: New.

Paperback. Condizione: Good.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: very good. Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.

Paperback. Condizione: New.

Paperback. Condizione: New.

Paperback. Condizione: new. Paperback. The model you depend on lives on someone else's hardware. They can change the price, change the rules, or retire it entirely, and you cannot stop them.Local AI Engineering with Ollama is how you stop renting and start owning. You take the model, the price, and the rules back into your own hands: run any model you want, when you want, where you want, and change how it behaves without a meter running.This is a practical book for developers who can run a command and edit a file but have no Machine Learning degree and want none. It skips the marketing and jumps into building things that run, on hardware you already own, with the network unplugged. Every command was executed on a real machine, and every output you see (JSON responses, error messages, token counts, training logs) came from an actual session, not from documentation.This book moves in one direction: from running your first model to shipping an agent that runs on your own hardware. Each chapter ends with something working, and each skill below builds on the one before it. By the end you will be able to: Understand what a model is actually doing: Tokens, predictions, weights, embeddings, attention, and the KV cache, each tied to a setting you will change.Install Ollama and size your hardware honestly: Install the runtime and tell if a model fits your RAM or VRAM before downloading.Pick, pull, and manage models: Read the Ollama and Hugging Face GGUF repos, choose quantization, and manage disk and memory.Drive Ollama from its API: Run models over HTTP from your code, and read tokens-per-second to compare on numbers.Control the context window: Size it so the model stops forgetting, and see what gets sent each turn.Operate a model under real conditions: Tune temperature, top_p, top_k, penalties, seed, keep-alive, and concurrency.Package a custom model with a Modelfile: One job, the same way every time, shipped as a single artifact.Fine-tune a model on your own data: Train Granite for English-to-SQL with QLoRA and Unsloth, then export to GGUF.Build against the Python SDK: Build Python programs with typed responses, ending in a management CLI.Build a working chat loop and see why it forgets: Write a REPL, then watch it fail to recall the last turn.Give the conversation a memory: Resend a running message list so the assistant follows the conversation.Stream replies and accept multi-line input: Print tokens as they arrive, and take multi-line prompts.Keep long chats inside the context window: Drop the oldest turns so the prompt never overflows.Summarize old turns instead of dropping them: Condense earlier messages with a second model through LangChain.Cache replies in Redis: Return repeated questions instantly, cutting latency and wasted compute.Add long-term memory that survives restarts: Wire in mem0 to recall user facts across sessions.Give the model tools to fetch live data: Add function calling, guarded against inventing numbers.Source those tools from an external MCP server: Serve tools over MCP, turning M times N into M plus N.Put a graphical interface in front of Ollama: Run Open WebUI in Docker, chat with your documents, lock it down for a team.If you can run a command and edit a file, you are qualified! Downloadable code included.So what are you waiting for to stop renting, start owning, and get a model running Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condizione: New.

Condizione: New. In English.

Paperback. Condizione: new. Paperback. You know Ansible. Your playbooks work. But your team is drowning in YAML sprawl, missed schedules, untracked changes, and "who ran what when" questions nobody can answer.AWX fixes this if you know how to wield it.AWX in Action: Ansible Orchestration at Scale is the practical guide for Ansible users ready to graduate from command-line scripts to a production-grade automation platform. AWX is the most powerful way to scale Ansible beyond CLI execution. But the official docs are dense, the moving parts are many, and most teams adopt AWX without ever using half of what it can do.This book changes that.Written by Aymen El Amri - cloud-native engineer with 15+ years in production systems, founder of FAUN.dev, and recognized by TechBeacon among the top 100 DevOps professionals to follow - this guide distills years of consulting engagements and battle-tested workshop teaching into a single hands-on reference.What you'll learnIn this guide, you'll work through AWX, the web-based interface for Ansible, and learn how to use it to scale your automation. Here's what you'll cover: Installation and Configuration: Set up AWX in your environment using Docker or Kubernetes. The official docs are overwhelming; this guide cuts to a clean, repeatable setup.Organizations, Users, and Teams: Structure AWX with organizations, users, and teams to manage permissions and secure your automation.Hosts and Inventories: Create and manage static and dynamic (e.g., AWS) inventories, group hosts, add credentials, and use constructed inventories for advanced cases.Projects, Templates, and Jobs: The core of AWX. Create projects, configure job templates, run ad hoc commands, launch and schedule jobs, and orchestrate complex flows with workflow templates and surveys. You'll also cover signature validation, project sync, notifications, and job artifacts, with practical examples and best practices throughout.AWX CLI: Install and authenticate the CLI, generate organization-scoped tokens, and manage, import, and export resources.Execution Environments and Instances: Build custom execution environments with Ansible Builder and Runner. Manage execution and hop instances, instance groups, clustering, container groups, and capacity, with the internals explained so you can scale and run AWX in HA.Settings: Configure authentication, job settings, logging, cache, and debugging.Monitoring and Status Views: Use the dashboard, jobs, schedules, activity streams, management jobs, notifications, logging, and metrics, including a head start on Prometheus monitoring.Real-World Example: Tie it together. Use custom execution environments and external roles, build a workflow with scheduling and notifications, and launch jobs from the AWX CLI, the AWX API, and your CI pipelines.By the end, you'll have what you need to roll out AWX confidently, solo or with a team.Who this book is forExperienced Ansible users, DevOps engineers, SREs, sysadmins, and platform teams who want to move beyond ansible-playbook and operate Ansible like production infrastructure - not just script it.What sets this book apartBuilt from real consulting engagements and a battle-tested workshop, not vendor marketing. Every chapter explains not just how AWX works but why - so you can debug, extend, and operate it long after you finish reading. The missing AWX manual Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condizione: New.

Condizione: New.

Condizione: New. In English.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: new. Paperback. The model you depend on lives on someone else's hardware. They can change the price, change the rules, or retire it entirely, and you cannot stop them.Local AI Engineering with Ollama is how you stop renting and start owning. You take the model, the price, and the rules back into your own hands: run any model you want, when you want, where you want, and change how it behaves without a meter running.This is a practical book for developers who can run a command and edit a file but have no Machine Learning degree and want none. It skips the marketing and jumps into building things that run, on hardware you already own, with the network unplugged. Every command was executed on a real machine, and every output you see (JSON responses, error messages, token counts, training logs) came from an actual session, not from documentation.This book moves in one direction: from running your first model to shipping an agent that runs on your own hardware. Each chapter ends with something working, and each skill below builds on the one before it. By the end you will be able to: Understand what a model is actually doing: Tokens, predictions, weights, embeddings, attention, and the KV cache, each tied to a setting you will change.Install Ollama and size your hardware honestly: Install the runtime and tell if a model fits your RAM or VRAM before downloading.Pick, pull, and manage models: Read the Ollama and Hugging Face GGUF repos, choose quantization, and manage disk and memory.Drive Ollama from its API: Run models over HTTP from your code, and read tokens-per-second to compare on numbers.Control the context window: Size it so the model stops forgetting, and see what gets sent each turn.Operate a model under real conditions: Tune temperature, top_p, top_k, penalties, seed, keep-alive, and concurrency.Package a custom model with a Modelfile: One job, the same way every time, shipped as a single artifact.Fine-tune a model on your own data: Train Granite for English-to-SQL with QLoRA and Unsloth, then export to GGUF.Build against the Python SDK: Build Python programs with typed responses, ending in a management CLI.Build a working chat loop and see why it forgets: Write a REPL, then watch it fail to recall the last turn.Give the conversation a memory: Resend a running message list so the assistant follows the conversation.Stream replies and accept multi-line input: Print tokens as they arrive, and take multi-line prompts.Keep long chats inside the context window: Drop the oldest turns so the prompt never overflows.Summarize old turns instead of dropping them: Condense earlier messages with a second model through LangChain.Cache replies in Redis: Return repeated questions instantly, cutting latency and wasted compute.Add long-term memory that survives restarts: Wire in mem0 to recall user facts across sessions.Give the model tools to fetch live data: Add function calling, guarded against inventing numbers.Source those tools from an external MCP server: Serve tools over MCP, turning M times N into M plus N.Put a graphical interface in front of Ollama: Run Open WebUI in Docker, chat with your documents, lock it down for a team.If you can run a command and edit a file, you are qualified! Downloadable code included.So what are you waiting for to stop renting, start owning, and get a Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condizione: new. Paperback. The model you depend on lives on someone else's hardware. They can change the price, change the rules, or retire it entirely, and you cannot stop them.Local AI Engineering with Ollama is how you stop renting and start owning. You take the model, the price, and the rules back into your own hands: run any model you want, when you want, where you want, and change how it behaves without a meter running.This is a practical book for developers who can run a command and edit a file but have no Machine Learning degree and want none. It skips the marketing and jumps into building things that run, on hardware you already own, with the network unplugged. Every command was executed on a real machine, and every output you see (JSON responses, error messages, token counts, training logs) came from an actual session, not from documentation.This book moves in one direction: from running your first model to shipping an agent that runs on your own hardware. Each chapter ends with something working, and each skill below builds on the one before it. By the end you will be able to: Understand what a model is actually doing: Tokens, predictions, weights, embeddings, attention, and the KV cache, each tied to a setting you will change.Install Ollama and size your hardware honestly: Install the runtime and tell if a model fits your RAM or VRAM before downloading.Pick, pull, and manage models: Read the Ollama and Hugging Face GGUF repos, choose quantization, and manage disk and memory.Drive Ollama from its API: Run models over HTTP from your code, and read tokens-per-second to compare on numbers.Control the context window: Size it so the model stops forgetting, and see what gets sent each turn.Operate a model under real conditions: Tune temperature, top_p, top_k, penalties, seed, keep-alive, and concurrency.Package a custom model with a Modelfile: One job, the same way every time, shipped as a single artifact.Fine-tune a model on your own data: Train Granite for English-to-SQL with QLoRA and Unsloth, then export to GGUF.Build against the Python SDK: Build Python programs with typed responses, ending in a management CLI.Build a working chat loop and see why it forgets: Write a REPL, then watch it fail to recall the last turn.Give the conversation a memory: Resend a running message list so the assistant follows the conversation.Stream replies and accept multi-line input: Print tokens as they arrive, and take multi-line prompts.Keep long chats inside the context window: Drop the oldest turns so the prompt never overflows.Summarize old turns instead of dropping them: Condense earlier messages with a second model through LangChain.Cache replies in Redis: Return repeated questions instantly, cutting latency and wasted compute.Add long-term memory that survives restarts: Wire in mem0 to recall user facts across sessions.Give the model tools to fetch live data: Add function calling, guarded against inventing numbers.Source those tools from an external MCP server: Serve tools over MCP, turning M times N into M plus N.Put a graphical interface in front of Ollama: Run Open WebUI in Docker, chat with your documents, lock it down for a team.If you can run a command and edit a file, you are qualified! Downloadable code included.So what are you waiting for to stop renting, start owning, and get a Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condizione: new. Paperback. You don't actually understand Git. You memorized a few commands, and you've been getting away with it.Most developers learn Git the same broken way: copy a command from Stack Overflow, paste it, pray. It works until it doesn't. Then a merge conflict, a detached HEAD, or a mangled history sends you into a panic, because you never understood the real model of what Git is doing underneath. You're not bad at Git. You were just never taught it properly.Learn Git in a Day: The Visual Guide fixes that in a single sitting.This book is neither a command reference nor a pile of recipes to memorize and forget. It's a structured, visual walkthrough that builds your understanding from the ground up, starting with why Git exists and how it thinks about your files and history, then moving into the exact workflows you'll use every single day on a real team.You'll learn by doing. Throughout the book, you build a small calculator app, not because the app matters, but because it gives you real files to track, real commits to make, and real conflicts to resolve. Each chapter adds a feature and introduces a new concept, so the project grows as your skills do.By the end, you'll be able to: Understand the three places your files live: your working directory, the staging area, and the repository, and move changes between them with total confidenceBrowse your full project history and inspect any past version in detailUndo edits, unstage files, and reverse saved changes without rewriting history or losing workStash unfinished work, switch tasks, and pick up exactly where you left offUse branches to juggle multiple features at once without them collidingMerge work cleanly and resolve conflicts when two people change the same linesConnect to a remote, authenticate securely without passwords, and push and pull like a proClone projects, open pull requests, and follow the professional branch-review-merge workflowRebase for a clean linear history, and know exactly when rebasing is safe and when it will break things for your teamWrite ignore rules, stop tracking files committed by mistake, tag releases, create aliases, and trace any line of code back to who changed itAnd much more!No prior Git or version control experience is assumed. You don't need to know what a commit is, what a branch does, or why any of it matters yet. If you've written even a small script in any language, you're ready!This book is for you if: You've heard of Git, maybe felt intimidated by it, and never sat down to properly learn itYou're a student, self-taught programmer, or career switcher who wants to stop avoiding GitYou already use Git but feel like you're faking it, and you want actually to understand the machineGit is not optional in tech: it's the baseline. Solo developers use it, teams of hundreds ship production code with it, and every serious software company on the planet runs on it. Whether you're heading into a developer, DevOps, or data science role, these are the skills you'll use on day one and every day after.Stop guessing and panicking; spend one focused day, and walk away understanding Git well enough to handle situations you've never even seen before. You've faked your way through Git with copy-pasted commands. It works until it doesn't. This visual build-as-you-go guide takes you from 0 to a professional workflow in 1 day, so you understand what Git is doing instead of guessing. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: New.

Paperback. Condizione: New.

Paperback. Condizione: new. Paperback. You know Ansible. Your playbooks work. But your team is drowning in YAML sprawl, missed schedules, untracked changes, and "who ran what when" questions nobody can answer.AWX fixes this if you know how to wield it.AWX in Action: Ansible Orchestration at Scale is the practical guide for Ansible users ready to graduate from command-line scripts to a production-grade automation platform. AWX is the most powerful way to scale Ansible beyond CLI execution. But the official docs are dense, the moving parts are many, and most teams adopt AWX without ever using half of what it can do.This book changes that.Written by Aymen El Amri - cloud-native engineer with 15+ years in production systems, founder of FAUN.dev, and recognized by TechBeacon among the top 100 DevOps professionals to follow - this guide distills years of consulting engagements and battle-tested workshop teaching into a single hands-on reference.What you'll learnIn this guide, you'll work through AWX, the web-based interface for Ansible, and learn how to use it to scale your automation. Here's what you'll cover: Installation and Configuration: Set up AWX in your environment using Docker or Kubernetes. The official docs are overwhelming; this guide cuts to a clean, repeatable setup.Organizations, Users, and Teams: Structure AWX with organizations, users, and teams to manage permissions and secure your automation.Hosts and Inventories: Create and manage static and dynamic (e.g., AWS) inventories, group hosts, add credentials, and use constructed inventories for advanced cases.Projects, Templates, and Jobs: The core of AWX. Create projects, configure job templates, run ad hoc commands, launch and schedule jobs, and orchestrate complex flows with workflow templates and surveys. You'll also cover signature validation, project sync, notifications, and job artifacts, with practical examples and best practices throughout.AWX CLI: Install and authenticate the CLI, generate organization-scoped tokens, and manage, import, and export resources.Execution Environments and Instances: Build custom execution environments with Ansible Builder and Runner. Manage execution and hop instances, instance groups, clustering, container groups, and capacity, with the internals explained so you can scale and run AWX in HA.Settings: Configure authentication, job settings, logging, cache, and debugging.Monitoring and Status Views: Use the dashboard, jobs, schedules, activity streams, management jobs, notifications, logging, and metrics, including a head start on Prometheus monitoring.Real-World Example: Tie it together. Use custom execution environments and external roles, build a workflow with scheduling and notifications, and launch jobs from the AWX CLI, the AWX API, and your CI pipelines.By the end, you'll have what you need to roll out AWX confidently, solo or with a team.Who this book is forExperienced Ansible users, DevOps engineers, SREs, sysadmins, and platform teams who want to move beyond ansible-playbook and operate Ansible like production infrastructure - not just script it.What sets this book apartBuilt from real consulting engagements and a battle-tested workshop, not vendor marketing. Every chapter explains not just how AWX works but why - so you can debug, extend, and operate it long after you finish reading. The missi Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…