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  Big Rig Business Startup: How to Start, Run & Grow a Successful Trucking Company , Hörbuch, Digital, ungekürzt, 116min

  After losing my corporate job in 2008, I went through the toughest time of my life. At the time my wife pregnant with our third child, and my monthly unemployment check wasn’t enough to cover our house and car payments. In desperation, I decided to go to a local trucking school and get my CDL. I found my first driving job a week after I got out of school. I still remember my first paycheck was just $55 shy of what I was making at my corporate desk job. I was hysteric, I was happy, I knew this was my new found life and freedom, and no one was going to take it away from me.Long story short, after two years of working for other companies, I decided to start my own trucking company, and I started out as an owner-operator. In 2013, I decided it was time for me to grow and time for me to get off the road and spend some time with my kids.This was when I entered the second phase of my business life. I started to buy one tractor every three months as I was hiring great drivers who are professional, family-oriented, and serious about making money. By 2016, I had 12 trucks on the road, and this is also the year when my net earning passed $350,000 mark. To me, the 350K mark was always a benchmark. Why? Because I knew that was the salary of the CEO of the company I used to work for.Last year, I received a call from a business broker, who asked me for a 10-minute meeting. He had brought an offer from a big trucking company, to buy my company. But I didn’t even remotely think about selling my company. Instead, I was talking to the bank and was in the process of finalizing a loan for four new tractors with trailers. Once again, long story short, after I refused their initial offer, they came back with an offer that no reasonable man can refuse, and I consider myself a very reasonable man.The income potential is truly amazing, and yes, if you can hire the right people, you will not only see significant growth, high net income but the satisfaction that ungekürzt. Language: English. Narrator: Jim Rising. Audio sample: https://samples.audible.de/bk/acx0/124053/bk_acx0_124053_sample.mp3. Digital audiobook in aax.

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  • Price: 9.95 EUR excl. shipping
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  Music Production: The Advanced Guide On How to Produce for Music Producers (eBook, ePUB)

  Learn to Produce Music Like a Pro and Take Your Music To a Whole New LevelDo you love producing music? Do you know what it takes to go from being a bedroom producer to a successful hit maker? If you believe you have what it takes then keep reading and let's create a masterpiece!With all the music production advice out there, it can be very easy to get overwhelmed. You may get a vague idea of the general topic, but you're more likely to be confused and you definitely won't have any workable knowledge.Well, the good news is this book changes that. Designed to take the complex world of music production, and explain it in simple terms. If you are a home based musician then this is a must have for making your music sound professional. For the pros and semi-pros out there, this is a great book for understanding what good music production entails. You can apply this knowledge to any genre of music and your music will sound balanced, clean, professionally mixed. The barrier to entry for making music is practically non-existent these days. That's why success can only come from you and not the equipment you use. While knowing how to use your tools is important, it's about the drive within that will take you to the next level. In this book you will discoverProduce a Track from Scratch Professional Singer Songwriter Secrets RevealedLearn about EQ, Compressor, Reverb, Delay, Sidechain and More Create Chord Progressions and Catchy Melodies How to Finish Your Ideas The Single Best Piece of Mixing Advice Ever Production Mistakes and How to Avoid Them Mastering and Finalizing Explained Sound Design Like a Boss The Mindset to Making More Music Learn a Proven Step By Step Mixing Process The Fundamentals You Need to SucceedAnd Much, Much More…So if you've ever wanted a single book that gives you all the knowledge to being a successful Music Producer, then click add to cart

  • Shop: buecher
  • Price: 2.99 EUR excl. shipping
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  Machine Learning Engineering in Action (eBook, ePUB)

  Field-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from concept to production. In Machine Learning Engineering in Action, you will learn: Evaluating data science problems to find the most effective solution Scoping a machine learning project for usage expectations and budget Process techniques that minimize wasted effort and speed up production Assessing a project using standardized prototyping work and statistical validation Choosing the right technologies and tools for your project Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices Ferrying a machine learning project from your data science team to your end users is no easy task. Machine Learning Engineering in Action will help you make it simple. Inside, you'll find fantastic advice from veteran industry expert Ben Wilson, Principal Resident Solutions Architect at Databricks. Ben introduces his personal toolbox of techniques for building deployable and maintainable production machine learning systems. You'll learn the importance of Agile methodologies for fast prototyping and conferring with stakeholders, while developing a new appreciation for the importance of planning. Adopting well-established software development standards will help you deliver better code management, and make it easier to test, scale, and even reuse your machine learning code. Every method is explained in a friendly, peer-to-peer style and illustrated with production-ready source code. About the technology Deliver maximum performance from your models and data. This collection of reproducible techniques will help you build stable data pipelines, efficient application workflows, and maintainable models every time. Based on decades of good software engineering practice, machine learning engineering ensures your ML systems are resilient, adaptable, and perform in production. About the book Machine Learning Engineering in Action teaches you core principles and practices for designing, building, and delivering successful machine learning projects. You'll discover software engineering techniques like conducting experiments on your prototypes and implementing modular design that result in resilient architectures and consistent cross-team communication. Based on the author's extensive experience, every method in this book has been used to solve real-world projects. What's inside Scoping a machine learning project for usage expectations and budget Choosing the right technologies for your design Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices About the reader For data scientists familiar with supervised machine learning and the basics of object-orientaFor data scientists who know machine learning and the basics of object-oriented programming.ed programming. About the author Ben Wilson is Principal Resident Solutions Architect at Databricks, where he developed the Databricks Labs AutoML project, and is an MLflow committer. Table of Contents PART 1 AN INTRODUCTION TO MACHINE LEARNING ENGINEERING 1 What is a machine learning engineer? 2 Your data science could use some engineering 3 Before you model: Planning and scoping a project 4 Before you model: Communication and logistics of projects 5 Experimentation in action: Planning and researching an ML project 6 Experimentation in action: Testing and evaluating a project 7 Experimentation in action: Moving from prototype to MVP 8 Experimentation in action: Finalizing an MVP with MLflow and runtime optimization PART 2 PREPARING FOR PRODUCTION: CREATING MAINTAINABLE ML 9 Modularity for ML: Writing testable and legible code 10 Standards of coding and creating maintainable ML code 11 Model measurement and why it's so important 12 Holding on to your gains by watching for drift 13 ML development hubris PART 3 DEVELOPING PRODUCTION MACHINE LEARNING CODE 14 Writing production code 15 Quality and acceptance testing 16 Production infrastructure

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  • Price: 42.40 EUR excl. shipping
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  Machine Learning Engineering in Action

  Field-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from concept to production. In Machine Learning Engineering in Action, you will learn: Evaluating data science problems to find the most effective solution Scoping a machine learning project for usage expectations and budget Process techniques that minimize wasted effort and speed up production Assessing a project using standardized prototyping work and statistical validation Choosing the right technologies and tools for your project Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices Ferrying a machine learning project from your data science team to your end users is no easy task. Machine Learning Engineering in Action will help you make it simple. Inside, you'll find fantastic advice from veteran industry expert Ben Wilson, Principal Resident Solutions Architect at Databricks. Ben introduces his personal toolbox of techniques for building deployable and maintainable production machine learning systems. You'll learn the importance of Agile methodologies for fast prototyping and conferring with stakeholders, while developing a new appreciation for the importance of planning. Adopting well-established software development standards will help you deliver better code management, and make it easier to test, scale, and even reuse your machine learning code. Every method is explained in a friendly, peer-to-peer style and illustrated with production-ready source code. About the technology Deliver maximum performance from your models and data. This collection of reproducible techniques will help you build stable data pipelines, efficient application workflows, and maintainable models every time. Based on decades of good software engineering practice, machine learning engineering ensures your ML systems are resilient, adaptable, and perform in production. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the book Machine Learning Engineering in Action teaches you core principles and practices for designing, building, and delivering successful machine learning projects. You'll discover software engineering techniques like conducting experiments on your prototypes and implementing modular design that result in resilient architectures and consistent cross-team communication. Based on the author's extensive experience, every method in this book has been used to solve real-world projects. What's inside Scoping a machine learning project for usage expectations and budget Choosing the right technologies for your design Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices About the reader For data scientists familiar with supervised machine learning and the basics of object-orientaFor data scientists who know machine learning and the basics of object-oriented programming.ed programming. About the author Ben Wilson is Principal Resident Solutions Architect at Databricks, where he developed the Databricks Labs AutoML project, and is an MLflow committer. Table of Contents PART 1 AN INTRODUCTION TO MACHINE LEARNING ENGINEERING 1 What is a machine learning engineer? 2 Your data science could use some engineering 3 Before you model: Planning and scoping a project 4 Before you model: Communication and logistics of projects 5 Experimentation in action: Planning and researching an ML project 6 Experimentation in action: Testing and evaluating a project 7 Experimentation in action: Moving from prototype to MVP 8 Experimentation in action: Finalizing an MVP with MLflow and runtime optimization PART 2 PREPARING FOR PRODUCTION: CREATING MAINTAINABLE ML 9 Modularity for ML: Writing testable and legible code 10 Standards of coding and creating maintainable ML code 11 Model measurement and why it's so important 12 Holding on to your gains by watching for drift 13 ML development hubris PART 3 DEVELOPING PRODUCTION MACHINE LEARNING CODE 14 Writing production code 15 Quality and acceptance testing 16 Production infrastructure

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  • Price: 59.99 EUR excl. shipping
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  Bestial Supremacy

  metalnews.de: (...) SARKOM hier eine starke Black Metal Scheibe veröffentlicht haben. Geradezu grandios sind sie sogar dann, wenn ihre Songs in langsame und erhabene Sphären abdriften.Sarkom's second album from 2008 is more back to basic with a classical Black Metal approach. Stripped down to the bone with ice cold riffs, groove and killer vocals, Bestial Supremacy belongs in any black metal fan's collection! No trigging or experimental in any way, just straight forward in the vein of old Darkthrone and Gorgoroth, yet still with a clear sound and good production. Sold out for years - now available again with a bonustrack! TRACKS: 1. Inferior Bleeding 2. I Call Your Name 3. Bestial Supremacy 4. Infected 5. Parallel to a Wall of Fire 6. Symbolic Revulsion 7. Artificial 8. Revival of Torment 9. Crushing the Retrospective Dominions 10. Finalizing Sovereignty 11. Alpha-Omega (Live)

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  • Price: 14.88 EUR excl. shipping

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