Data Science: A Team Spirit
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What Mistakes Are Usually Done By Organizations While Deploying Machine Learning?
- Develop an analytics center of excellence: These centers function as an analytics consultancy inside the organization. The center can consolidate analytical talent in one place and allow for the efficient use of analytical skills across the business.
- Build relationships with universities: Create an internship program or a university recruiting program to find new talent. You can also tap into university programs that pair students with businesses to help solve problems.
- Develop talent from within: Look for employees who have a natural aptitude for mathematics and problem solving, and invest in data science training.
- Make analytics more approachable: If your data visualization tools are user friendly and data is easy to explore, others in the business can solve problems with data, too, not just data scientists.
- Noisy Data:Data that contains a large amount of conflicting or misleading information.
- Dirty Data: Data that contains missing values, categorical and character features with many levels, and inconsistent and erroneous values.
- Sparse Data: Data that contains very few actual values, and is instead composed of mostly zeros or missing values.
- Inadequate Data: Data that is either incomplete or biased.
- Data security and governance: Address data security issues at the beginning of a machine learning exercise, especially if support from other departments is required. Likewise, early plans for data governance should consider how algorithms will be used, stored and reused.
- Data integration and preparation: After data has been collected and cleaned, it must still be transformed into a format that is logical for machine learning algorithms to consume.
- Data exploration: Productive, professional machine learning exercises should start with a specific business need and yield quantifiable results. Data scientists must have the ability to efficiently query, summarize and visualize data before and after machine learning models are trained, and build algorithms as new data is added.
- Flexible storage: Design an appropriate, organizationwide storage solution that meets data requirements and has room to mature with technology advances. Storage considerations should include data structure, digital footprint and usage.
- Powerful computation: A powerful, scalable and secure computing infrastructure enables data scientists to cycle through multiple data preparation techniques and different models to find the best possible solution in a reasonable amount of time. The following approaches have shown success for machine learning:
- Hardware acceleration: For I/O-intensive tasks such as data preparation or disk-enabled analytics software, use solid-state hard drives (SSDs). For computationally intensive tasks that can be run in parallel, such as matrix algebra, use graphical processing units (GPUs).
- Distributed computing: In distributed computing, data and tasks are split across many connected computers, often reducing execution times. Make sure you are using a distributed environment thatâ??s well suited for machine learning.
- Elasticity: Storage and compute resource consumption can be highly dynamic with machine learning, requiring high amounts in certain intervals and low amounts in others. Infrastructure elasticity allows for more optimal use of limited computational resources and/or financial expenditures.
- Anomaly detection: While no single approach is likely to solve a real business problem, several machine algorithms are known to boost the detection of anomalies, outliers and fraud.
- Segmented model factories: Sometimes markets have vastly different segments. Or, in health care, every patient in a treatment group can require special attention. In these cases, applying a different predictive model to each segment or to each patient may result in more targeted and efficient actions. Using a model factory approach to build models automatically across many segments or individuals allows the implementation of any gains in accuracy and efficiency.
- Ensemble models: Combining the results of several models or many models can yield better predictions than using a single model alone. While ensemble modeling algorithms such as random forests, gradient boosting machines and super learners have shown great promise, custom combinations of pre-existing models can also lead to improved results.
- Advanced Regression Techniques: Knowing when to use advanced techniques is essential. For example, penalized regression techniques are well suited for wide data. Generalized additive models allow you to fine-tune a trade-off between interpretability and accuracy. With quantile regression, you can fit a traditional, interpretable linear model to different percentiles of training data, allowing you to find different sets of variables for modeling different behaviors.
- Using Machine Learning models as benchmarks: A major difference between machine learning models and traditional linear models is that machine learning models usually take a large number of implicit variable interactions into consideration. If your regression model is less accurate than your machine learning model, youâ??ve probably missed some important interactions.
- Surrogate Models: Surrogate models are interpretable models used as a proxy to explain complex models. For example, fit a machine learning model to your training data. Then train a traditional, interpretable model on the original training data, but instead of using the actual target in the training data, use the predictions of the more complex algorithm as the target for this interpretable model.