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Best Practices in Machine Learning

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Best Practices in Machine Learning


Machine learning (ML) has given rise to several practical applications that fulfill real business interests such as saving time and money.

It has the potential to dramatically impact the future of your organization. Through applications such as virtual assistant solutions, machine learning automates tasks that would otherwise need to be performed by a live agent. Machine learning has made dramatic improvements in the past few years, but we are still very far from reaching human performance levels. Many a times, a machine needs the assistance of a human to complete its task. This is why, it is necessary for organizations to learn best practices in machine learning.

For the correct implementation of a machine learning algorithm, organizations are required to study machine learning use cases and execute best practices. Some such best practices include:

‘IMPORTANCE WEIGHT’ OF YOUR SAMPLED DATA

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When your organization has too much data, there is a temptation to take some files and drop rest of them. Dropping data while training your machine learning algorithms can cause several issues. Importance weighting means that if you decide that you are going to sample example X with a 30% probability, then give it a weight of 10/3. Thus, by importance weighting, all of the calibration properties are discussed and addressed.

Reuse Code

You must reuse code between your training pipeline and your serving pipeline whenever it is possible. Batch processing methods are different than online processing methods. In online processing, you have to handle each request as it arrives, whereas, in batch processing you have to combine tasks.

At serving time, you are doing online processing, while training is a batch processing task. For using the code, you can create an object that is particular to your system. You should be able to store the result of any queries or joins in a very human readable way.

Then, once you have gathered all the information, during serving or training, you should be able to run a common method for bridging between the human readable object that is specific to your system and whatever format the machine learning system expects.

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Avoid Unaligned Objectives

While measuring the performance of your machine learning system, your team will start to look at issues that are outside the scope of the objectives of your current system. If your product goals are not covered by the existing algorithmic objective, then you must either change your objective or your product goals. For instance, you may optimize clicks or downloads, but make launch decisions based in part on human raters.

Keep Ensembles Simple 

Unified models are those models that take in raw features and directly rank content. These models are the easiest models to debug and understand. However, an ensemble of models works better. To keep things simple, each model must either be an ensemble, only taking the input of other models, or it can be a base model taking many features, but not both

If your organization is having models on top of other models that are trained separately, then combining such models can result in bad behavior. You must use a simple model for ensemble that takes only the output of your “base” models as inputs. You can enforce properties on these ensemble models. For example, an increase in the score produced by a base model should not decrease the score of the ensemble.

Thus, implementing these best practices can ensure successful implementation of machine learning algorithms.



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TECHNOLOGY

How Data Analytics is Changing the Role of Employees

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How Data Analytics is Changing the Role of Employees


RPA and cognitive automation have pushed employees to assume creative roles as the mundane tasks are handled autonomously.

Big data in HR helps analyze trends, monitor performance, and dictate the course of action to follow in the future.

 Workforce trends are seeing a titanic shift toward automation led by Robotic Process Automation (RPA) and cognitive automation, among other technologies. Although the process of automation in industries began a few decades ago, the pandemic and the competitive advantages have accelerated it further. It raises concerns about human workers losing their jobs during the transition and it’s true.

However, AI-powered data analytics has matured enough to provide insights into the employees and their roles in an organization. As automation is taking away repetitive tasks, employees are assuming more creative and decision-making roles thanks to the inputs from analytics of big data in HR.

How Data Analytics Is Changing Employee Roles in a Cognitive Automation Era

Cognitive automation and RPA are taking up mundane tasks as they adhere to rule-based and repetitive tasks in a high volume. It helps produce items from scratch in comparatively less time and without much error. Additionally, it gives a competitive edge to any organization as they can produce items in large quantities while keeping the cost less than half of what a low-salary worker would charge. It is the predominant reason why manufacturing jobs are phasing out and this has propagated to other sectors, including HR, IT, retail, etc.

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Data analytics has transformed the world of human resources and employee roles in organizations. As cognitive automation increasingly handles mundane tasks, employees are left with exploring their verticals and using their time creatively and productively. It has proven to be instrumental as employees can focus on value-added activities rather than punching in numbers in invoices or making transactional logs, among others. Big data in HR is crucial to be processed to make sense of the data. It analyzes the trends and behavioral patterns of the employees in an organization that HR managers can utilize to assign different roles or take appropriate actions.

Organizations use data analytics to ascertain employees’ performance benchmarks that use all kinds of data sources available. It can help human resources retain employees with higher productivity and progress ratios while reassigning or terminating those with the wrong skill sets in the company. Since tools analyzing big data in HR can track trends, AI can be used to find employees that might leave the organization for some reason.

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The data-driven approach is a great way to analyze employee performance that helps HR managers to decide on promotions and other salary-related decisions, thereby minimizing any events of nepotism and bias. AI algorithms can be trained to take out any biases from the pie and generate outcomes based on legit factors deemed paramount by the organizations, such as KPIs. 

Cognitive automation is eating up jobs that involve mundane tasks and this is pushing employees to assume creative roles in any organization. Big data in HR can prove instrumental in deciding the employees’ roles in this automated era that can help stay relevant to the job profiles they are assigned while ensuring all the performance benchmarks are ascertained in deciding the fate of the employees.



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