**PRACTICES AND TECHNIQUES FOR DEPLOYING AND MAINTAINING ML MODELS**
Discover the key steps in the practical approach to Data Science, from data collection and cleaning to predictive modeling, to successfully carry out projects in an efficient and effective manner.
In conclusion, the practical approach to Data Science is a complex yet essential discipline for companies looking to make informed decisions based on data. To achieve this, IDEA follows key steps, from data collection and cleaning to predictive modeling and deployment. We also monitor and evaluate the model's performance in the long term to ensure the project's effectiveness. By following these steps, IDEA can help companies fully leverage the power of data to make informed decisions and maintain a competitive advantage.
The eighth and final step of the practical approach to Data Science is monitoring and evaluation. After the model is deployed, it is important to monitor and evaluate its performance to ensure it remains accurate and reliable. Data science professionals can use tools such as dashboards and reports to monitor the model's performance. Additionally, they must continuously update the model based on data changes or business needs. This step is essential to ensure that the company benefits from the data analysis results in the long term.
The fifth step is data modeling. Using the results from the previous step, a model can be built to answer the business question. We use techniques such as machine learning, linear regression, or logistic regression to build predictive models. This step also involves optimizing the model's parameters to improve its accuracy.
The first step is to understand the business question. This step is crucial as it sets the direction for data collection and analysis. To understand the business question, IDEA teams must discuss with stakeholders and clearly define the project objectives. This step also helps determine the key performance indicators (KPIs) that will be used to measure the success of the project.
The fourth step is data exploration. This step involves exploring the data to uncover patterns or trends that may help answer the business question. IDEA uses exploratory analysis techniques to better understand the data. This step also helps identify the important variables that will impact the results.
The second step is data collection. Data can be collected from various sources such as databases, Excel files, web data, sensors, images, or videos. It is important to collect the most relevant data to answer the business question.
The seventh step is deployment. Once the model is ready, it must be deployed and integrated into the company's existing system. IDEA uses tools such as FastAPI, React, and Docker to deploy the model and make it accessible to end users. This step is crucial for the company to benefit from the data analysis results and make informed decisions in real-time.
The sixth step is model evaluation. The created models must be evaluated to ensure they are accurate and reliable. IDEA uses metrics such as precision, recall, F-measure, or the ROC curve to evaluate the model's performance. This step also helps determine whether the model can effectively answer the business question.
The third step is data cleaning. The collected data may contain errors, missing values, or inconsistent data. IDEA uses modern tools to clean the data and remove duplicates.