Gemini: Stepping Stone to the Moon, the Untold Story
by Jeffrey Kluger · 11 Nov 2025 · 305pp · 98,394 words
never much trusted Carpenter; the man’s approach to training had been nonchalant, and he regularly finished at the back of the astronaut pack in performance metrics. Worse, to Kraft’s way of thinking, there was a fundamental lack of seriousness to Carpenter; not only was he not performing well, he seemed
Site Reliability Engineering: How Google Runs Production Systems
by Betsy Beyer, Chris Jones, Jennifer Petoff and Niall Richard Murphy · 15 Apr 2016 · 719pp · 181,090 words
Tools As pieces of software, SRE tools also need testing.10 SRE-developed tools might perform tasks such as the following: Retrieving and propagating database performance metrics Predicting usage metrics to plan for capacity risks Refactoring data within a service replica that isn’t user accessible Changing files on a server SRE
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sophisticated services may aim for step 4. Precursors to Intent What information do we need in order to capture a service’s intent? Enter dependencies, performance metrics, and prioritization. Dependencies Services at Google depend on many other infrastructure and user-facing services, and these dependencies heavily influence where a service can be
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depends on where we can place Bar, Baz, and Qux. A given set of production dependencies can be shared, possibly with different stipulations around intent. Performance metrics Demand for one service trickles down to result in demand for one or more other services. Understanding the chain of dependencies helps formulate the general
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Foo need to serve N user queries? For every N queries of service Foo, how many Mbps of data do we expect for service Bar? Performance metrics are the glue between dependencies. They convert from one or more higher-level resource type(s) to one or more lower-level resource type(s
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). Deriving appropriate performance metrics for a service can involve load testing and resource usage monitoring. Prioritization Inevitably, resource constraints result in trade-offs and hard decisions: of the many
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to be made to rationalize resource acquisition costs. Monitoring Problems in Periodic Pipelines For pipelines of sufficient execution duration, having real-time information on runtime performance metrics can be as important, if not even more important, than knowing overall metrics. This is because real-time data is important to providing operational support
The Art of Scalability: Scalable Web Architecture, Processes, and Organizations for the Modern Enterprise
by Martin L. Abbott and Michael T. Fisher · 1 Dec 2009
hosts. 445 446 C HAPTER 29 S OARING IN THE C LOUDS The virtual hardware underperforms in some aspects by orders of magnitude. The standard performance metrics include memory speed, CPU, disk access, and so on. There is no standard degradation or equivalence among virtual hosts; in fact, it often varies within
Text Analytics With Python: A Practical Real-World Approach to Gaining Actionable Insights From Your Data
by Dipanjan Sarkar · 1 Dec 2016
. Also, the accuracy on the overall test data is 54.5 percent, which is quite decent for a start. For more details on what each performance metric signifies, refer to the “Evaluating Classification Models ” section in Chapter 4. Remember when I said annotated tagged metadata for text is useful in many ways
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new instances of text documents. Often the model is tuned on several of its internal parameters based on the learning algorithm and by evaluating various performance metrics like accuracy on the validation set or by using cross-validation where we split the training dataset itself into training and validation sets by random
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. false_positive = 6. false_negative = 5. true_negative = 4. Now that we have the necessary values from the confusion matrix, we can calculate our four performance metrics one by one. We have taken the values from earlier as floats to help with computations involving divisions. We will use the metrics module from
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movie reviews predicted_sentiments = svm.predict(test_features) # evaluate model prediction performance from utils import display_evaluation_metrics, display_confusion_matrix, display_classification_report # show performance metrics In [270]: display_evaluation_metrics(true_labels=test_sentiments, ...: predicted_labels=predicted_sentiments, ...: positive_class='positive') Accuracy: 0.89 Precision: 0.88 Recall: 0.9
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.89 7510 negative 0.90 0.88 0.89 7490 avg / total 0.89 0.89 0.89 15000 The preceding outputs show the various performance metrics that depict the performance of our SVM model with regard to predicting sentiment for movie reviews. We have an average sentiment prediction accuracy of 89
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(review) for review in test_reviews] from utils import display_evaluation_metrics, display_confusion_matrix, display_classification_report # get model performance statistics In [295]: print 'Performance metrics:' ...: display_evaluation_metrics(true_labels=test_sentiments, ...: predicted_labels=sentiwordnet_predictions, ...: positive_class='positive') ...: print '\nConfusion Matrix:' ...: display_confusion_matrix(true_labels=test_sentiments, ...: predicted
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_labels=sentiwordnet_predictions, ...: classes=['positive', 'negative']) ...: print '\nClassification report:' ...: display_classification_report(true_labels=test_sentiments, ...: predicted_labels=sentiwordnet_predictions, ...: classes=['positive', 'negative']) Performance metrics: Accuracy: 0.59 Precision: 0.56 Recall: 0.92 F1 Score: 0.7 Confusion Matrix: Predicted: positive negative Actual: positive 6941 569 negative 5510 1980
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movie reviews dataset vader_predictions = [analyze_sentiment_vader_lexicon(review, threshold=0.1) for review in test_reviews] # get model performance statistics In [302]: print 'Performance metrics:' ...: display_evaluation_metrics(true_labels=test_sentiments, ...: predicted_labels=vader_predictions, ...: positive_class='positive') ...: print '\nConfusion Matrix:' ...: display_confusion_matrix(true_labels=test_sentiments, ...: predicted
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_labels=vader_predictions, ...: classes=['positive', 'negative']) ...: print '\nClassification report:' ...: display_classification_report(true_labels=test_sentiments, ...: predicted_labels=vader_predictions, ...: classes=['positive', 'negative']) Performance metrics: Accuracy: 0.7 Precision: 0.65 Recall: 0.86 F1 Score: 0.74 Confusion Matrix: Predicted: positive negative Actual: positive 6434 1076 negative 3410 4080
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movie reviews dataset pattern_predictions = [analyze_sentiment_pattern_lexicon(review, threshold=0.1) for review in test_reviews] # get model performance statistics In [307]: print 'Performance metrics:' ...: display_evaluation_metrics(true_labels=test_sentiments, ...: predicted_labels=pattern_predictions, ...: positive_class='positive') ...: print '\nConfusion Matrix:' ...: display_confusion_matrix(true_labels=test_sentiments, ...: predicted
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_labels=pattern_predictions, ...: classes=['positive', 'negative']) ...: print '\nClassification report:' ...: display_classification_report(true_labels=test_sentiments, ...: predicted_labels=pattern_predictions, ...: classes=['positive', 'negative']) Performance metrics: Accuracy: 0.77 Precision: 0.76 Recall: 0.79 F1 Score: 0.77 Confusion Matrix: Predicted: positive negative Actual: positive 5958 1552 negative 1924 5566
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-score. In this section, we will briefly look at how each model’s performance compares against the other models. Figure 7-3 shows the model performance metrics and a visualization comparing the metrics across all the models. Figure 7-3. Comparison of sentiment analysis model performances From the visualization and the table
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(IMDb) Indexing Information retrieval (IR) Integrated development environments (IDEs) Internet Movie Database (IMDb) movie reviews datasets feature-extraction getting and formatting, data lexicons SeeLexicons model performance metrics and visualization positive and negative setting up dependencies supervisedML SeeSupervised machine learning technique text normalization Iterators J JAVA_HOME environment variable Java Runtime Environment (JRE
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inflections snippet Snowball Project user-defined rules Stopwords Strings indexing syntax literals operations and methods Supervised machine learning technique confusion matrix normalization and feature-extraction performance metrics positive and negative emotions predictions support vector machine (SVM) test dataset reviews text classification Support vector machines (SVM) SVD SeeSingular Value Decomposition (SVD) SVM SeeSupport
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applications and uses automated SeeAutomated text classification blueprint conceptual representation definition documents feature-extraction SeeFeature-extraction techniques inherent properties learning machine learning (ML) normalization prediction performance, metrics accuracy confusion matrix emails, spam and ham F1 score precision recall products types Text corpora/text corpus access Brown Corpus NLTK Reuters Corpus WordNet annotation
Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
by Martin Kleppmann · 16 Mar 2017 · 1,237pp · 227,370 words
), and provide tools to recompute data (in case it turns out that the old computation was incorrect). Set up detailed and clear monitoring, such as performance metrics and error rates. In other engineering disciplines this is referred to as telemetry. (Once a rocket has left the ground, telemetry is essential for tracking
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request routing, Request Routing-Parallel Query Executionapproaches to, Request Routing parallel query execution, Parallel Query Execution resilient systems, Reliability(see also fault tolerance) response timeas performance metric for services, Describing Performance, Batch Processing guarantees on, Response time guarantees latency versus, Describing Performance mean and percentiles, Describing Performance user experience, Describing Performance responsibility
Mastering Private Equity
by Zeisberger, Claudia,Prahl, Michael,White, Bowen, Michael Prahl and Bowen White · 15 Jun 2017
cashflows—so every €1m of EBITDA increase delivers €1.1m directly to the management pot. This is highly motivating to management. Consequently, they embrace the performance metrics and scrutiny of their private equity investors. They thrive on seeing the EBITDA increase and the net debt go down. A great private equity CEO
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to date. Exhibit 19.1 shows the basic steps taken to translate the value of a fund’s portfolio companies to its gross and net performance metrics. Exhibit 19.1 Evaluating PE Fund Performance Most limited partnership agreements require that a GP reports the fair market value of a fund’s investments
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its preference.3 Gross Performance With realized and unrealized valuations of its portfolio companies in hand, a GP will calculate a range of fund-level performance metrics, including the fund’s MoM, NAV and IRR. Calculating the MoM of each investment—and ultimately of the fund—is fairly straightforward; it is simply
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Template, as well as applying consistent return comparisons, will enhance an LP’s evaluation of the GP peer universe. The IRR Conundrum IRR is the performance metric of choice in the PE industry, and represents the discount rate that renders the net present value of a series of cash flows of an
Investment Banking: Valuation, Leveraged Buyouts, and Mergers and Acquisitions
by Joshua Rosenbaum, Joshua Pearl and Joseph R. Perella · 18 May 2009 · 444pp · 86,565 words
Statistics and Ratios The first stage of the benchmarking analysis involves a comparison of the target and comparables universe on the basis of key financial performance metrics. These metrics, as captured in the financial profile framework outlined in Steps I and III, include measures of size, profitability, growth, returns, and credit strength
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$25.3 million in 2013E. EXHIBIT 3.39 ValueCo Historical and Projected Capex Change in Net Working Capital Projections As with ValueCo’s other financial performance metrics, historical working capital levels normally serve as reliable indicators of future performance. The direct prior year’s ratios are typically the most indicative provided they
Kanban: Successful Evolutionary Change for Your Technology Business
by David J. Anderson · 6 Apr 2010 · 318pp · 78,451 words
service used a target lead time, for example, 28 days (4 weeks). The concept of offering a target lead time coupled with a due-date performance metric is an alternative to treating each item individually and having to estimate and commit to a delivery date for each item. The service-level agreement
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my team in 2007, approximately 30 percent of all requests were late compared to the target lead time. We reported this as the Due Date Performance metric. It was never above 70 percent. However, despite this dismal performance versus the target date, we had very few complaints. The reasons for this became
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is useful to have 13 months of data. With the Fixed Delivery Date class of service items, you can include these in the Due Date Performance metric. In this case, you are answering the question, “Was the item delivered on time?” However, although you will have a lead time recorded, that in
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provides this by asking the business owners to refill empty slots in the queue while providing them with a reliable lead-time and due-date performance metric. We already have six lofty and valuable goals for our Kanban system, and for many businesses, that might be enough. However, I and other early
Trading Risk: Enhanced Profitability Through Risk Control
by Kenneth L. Grant · 1 Sep 2004
good periods into great ones, mediocre periods into respectable ones, and otherwise catastrophic intervals into ones where the consequences are acceptable. Managing these types of performance metrics is hard work, but it is not nearly as difficult as losing lots of money or simply treading water. What is more, you’ll never
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price/underlying price, relationship between, 149–150 volatility arbitrage, 106 Out-of-the-money option, 150 Over-the-counter derivatives, 148 Performance analysis, 7–8 Performance metrics, 16, 35 Performance objectives: “going to the beach,” 32–36 importance of, 19–20, 29 nominal target return, 20, 24–26 optimal target return, 20
The Heart of Business: Leadership Principles for the Next Era of Capitalism
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preaching pure socialism.3 Milton Friedman’s perspective has one obvious advantage: it is simple. There is just one constituency to please—shareholders—and one performance metric that matters—profits. The Friedman doctrine remained business gospel for decades. In 1997, the Business Roundtable, which includes the CEOs of the largest and most
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