SKU: 35218750388

Omega Speedmaster Professional “Moonwatch” Blue Bezel Ref 145.022 from 1971

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Description

Omega Speedmaster Professional “Moonwatch” Blue Bezel Ref 145.022 from 1971Omega Speedmaster Professional MoonwatchRef. 145. 022 Calibre 861 1971 Presented here is a highly attractive and honest Omega Speedmaster Professional Moonwatch, reference 145. 022, powered by the legendary calibre 861. A true tool watch with deep historical roots, this example stands out thanks to its beautifully aged bezel, sharp case, and original Omega components throughout. The Speedmaster needs little introductionthis is the iconic chronograph

Omega Speedmaster Professional “Moonwatch”
Ref. 145.022 – Calibre 861 – 1971

Presented here is a highly attractive and honest Omega Speedmaster Professional Moonwatch, reference 145.022, powered by the legendary calibre 861. A true tool watch with deep historical roots, this example stands out thanks to its beautifully aged bezel, sharp case, and original Omega components throughout.

The Speedmaster needs little introduction—this is the iconic chronograph forever linked to NASA and lunar exploration, and the 145.022 represents one of the most important and longest-running references in the model’s history.

Dial

The watch features an original Omega black Speedmaster dial, crisp and well-preserved, with the classic tri-compax layout that defines the Moonwatch. The dial remains clean and legible, offering excellent contrast against the white printing and sub-dials.

The hands are original Omega service replacements, changed during a service at some point in the watch’s life—an entirely normal and acceptable practice for a tool watch that was meant to be used. They integrate perfectly with the dial and maintain the authentic Speedmaster look.

Bezel

One of the most charming features of this watch is its aluminium tachymeter bezel, which has aged beautifully over time. Originally black, it has developed a striking grey-to-blue hue, shifting in tone depending on the light. This subtle “ghost” effect gives the watch tremendous character and is highly appreciated by collectors.

Each glance reveals something different—an aging process that simply cannot be replicated.

Case

The 42mm stainless steel case remains sharp and well-defined, with strong lines and edges, and is possibly unpolished. This is exactly what collectors hope to see: an honest case that retains its original geometry and tool-watch presence.

The watch is fitted with an Omega-signed crown and Omega-signed plexiglass, preserving period-correct details that are essential to the Moonwatch identity.

Movement

Inside beats the legendary Omega calibre 861, introduced in 1968 and used in Speedmasters flown by NASA. Known for its robustness, accuracy, and ease of service, the calibre 861 is one of the most respected manual-wind chronograph movements ever produced.

The movement carries serial number 31,625,823, placing production in 1971, fully consistent with this reference.

Bracelet

The watch comes on an original Omega stainless steel bracelet reference 1171. The bracelet is notably long and will comfortably fit wrists up to 21,5cm, making it both practical and increasingly hard to find in this size. The 1171 bracelet is a classic pairing for vintage Speedmasters and completes the watch perfectly.

Why This Watch Stands Out

This Speedmaster 145.022 combines all the elements collectors look for: a sharp case, original dial, correct movement, and a beautifully aged bezel that gives the watch a unique personality. It is a true vintage Moonwatch—functional, historically important, and full of character.

Equally suited for daily wear or a serious collection, this piece embodies the Speedmaster’s legacy as a professional instrument built to withstand extreme conditions.

Technical Details

Brand: Omega
Model: Speedmaster Professional Moonwatch
Reference: 145.022
Year: 1971
Case: Stainless steel
Case Size: 42mm
Dial: Original Omega black dial
Hands: Original Omega service hands
Bezel: Aluminium tachymeter bezel with grey/blue aged patina
Crystal: Omega-signed plexiglass
Crown: Omega-signed crown
Movement: Omega manual-wind calibre 861
Movement Serial: 31,625,823
Bracelet: Original Omega steel bracelet ref. 1171
Wrist Size: Fits up to approx. 21,5cm

A True Tool Watch Icon

This Omega Speedmaster Professional is not just a chronograph—it is a piece of spaceflight history and one of the most important watches ever produced. With its attractive patina, sharp case, and legendary calibre 861, this example offers an exceptional opportunity to own a genuine vintage Moonwatch with real presence and soul.

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SKU: 35218750388

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Richard Hackathorn
Los Angeles, US
★★★★★ 5
Excellent Textbook for Hands-On Learning of ML
Format: Kindle
This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book. For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch. For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min). From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures. I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications. Several textbook editions later, what is different about this new edition? First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills. Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like. Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
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Reviewed in the United States on February 26, 2022
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Amazon Customer
San Leandro, US
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
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Reviewed in the United States on December 10, 2025
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Kindle Customer
Charlottesville, US
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
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Reviewed in the United States on May 3, 2026
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Tommy Jonsson
Phoenix, US
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
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Reviewed in the United States on May 4, 2026
M
Verified Purchase
Moses Kayanda
Cuba, US
★★★★★ 5
One of the best machine learning books...
Format: Paperback, Format: Paperback
Machine Learning can often be intimidating whether you are starting out or already a practitioner. It is easy to get stuck on one concept, walk away frustrated, or just copy that code you find on StackOverflow without really understanding what it does. What the authors of this book, Machine Learning with PyTorch and Scikit-Learn, have managed to do is to keep the reader engaged giving a deeper illustration as to how the concepts work. In this book, you get practical code examples, a detailed explanation of how the various library tools work, and exposure to the mathematical concepts behind machine learning algorithms. In addition, what I like about the book unlike many machine learning books is that the authors have managed to intuitively explain how each algorithm works, how to use them, and the mistake you need to avoid. I have not read a Machine Learning book that better explains Transformers as this one does. The authors have managed to give a detailed dive into this model architecture through well-explained codes and illustrations. As a reader, you walk away having intuitively grasped the concepts of attention and self-attention in ways that will make this crucial NLP architecture clear. You get exposed to pre-trained models from HuggingFace library which really helps to have that hands-on experience working with large datasets. As they have done throughout the book, the authors have broken down those complex mathematical operations into simple explanations that are easy to follow. What I generally like about the book is how it seamlessly connects all the chapters, not throwing off the reader. There are numerous external resources quoted throughout the book. This helps spark that curiosity to dig deeper. In addition, you get introduced to PyTorch, getting exposed to all those sophisticated libraries that help the reader learn how to maximize their compute power. I would say it is not intimidating at all even if you have not used PyTorch before. I would recommend this book to anybody seeking a textbook that is both easy to read and modern in its content. If were to rate the book I will give it a 10/10 as it really applies to both beginners and experienced practitioners, covers all the concepts one needs to apply in their operations, and acts as a quick reference.
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Reviewed in the United States on March 1, 2022

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