Best Practices for Managing Data in an MLOps Environment

MLOps, or the study of Machine Learning operations, stands at the intersection of Machine Learning, Development practices and Developer Operations. Now countless times through our articles, we have emphasized the importance of the various steps that are involved in this entire set of practices and why and where they should be implemented. Let us look at one such step, which is Managing Data as a whole as an enterprise and startup, and what are some of the points that are essential to your checklist.

In this article, we are going to be looking at important steps that become an essential part of your pipeline when your enterprise is collecting and processing data on a large scale and what are some of the precautions that come with it. Some such steps are Data Acquisition, Data Storage, Data Pre-Processing, Data Security and Privacy, and finally, Data Governance.

1. Data Acquisition

Data Acquisition can range from anything like measuring any physical phenomenon using sensors to creating surveys to collect user data. This is the most integral part of any Data Science pipeline as it ensures and directs the path your model and your team will take in the future. This step also includes the right process of uploading and feeding the data in the data pipeline, ensuring the upcoming steps like storage, processing, etc., perform according to the need.

This data absolutely needs to be properly collected to ensure that the ML engineers at the end are not face-to-face with a mess that cannot even be touched. Unlike traditional statistical and machine learning algorithms, deep learning nowadays requires enormous amounts of data which are required for the features that it itself learns through this data and performs marvelous feats.

Some of the steps to ensure this system works well are:

1. Data Stewards: Ensure you have good communication with data stewards, i.e. people who have expertise in the field you are trying to tackle with machine learning. They will ensure that the correct data is collected, which is relevant to the problem statement and the final product.

2. Sensors: Sensors used for this step need to be conditioned and tweaked properly to somewhat produce values that are actually useful and make the further step easier.

3. Dealing with Pushback: When collecting data from users or people, have an explanation ready for each and every requested data because you may come face to face with people who are not willing to let go of their information.

Let us move to the next step in the pipeline and where your efforts should be centered once the data is acquired.

2. Data Storage

Data Storage is something that each and every one of us has been doing since our very schooling. Remember the first time you used Microsoft Excel? Enterprise data tends to inflate exponentially, with acquisition techniques becoming easier and easier. This data needs to be organized and stored properly before your machine learning engineers can tackle them, trying to make your next amazing product from or lead your next marketing initiative.

Data Storage can easily be tackled and can be used to transform the raw data collected into something truly beautiful. Over the years, you must have heard of amazing datasets like ImageNet, MNIST, FER2013, etc. What do you think made these datasets the legend they are, so much so they exist on each and every one of our resumes? The only reason these datasets work so well with deep learning models is because of the way they are managed and stored, be it through a DBMS system or a simple CSV-creating annotation tool.

Some of the tips to ensure data storage flourishes so that your pipeline does are:

1. Skip Cloud Building: Because of security concerns, you may be deviating all your efforts into creating a cloud system to hold your entire data, but that may not be the best way to store data as with a rudimentary system come to their own problems. The features services online may offer to pre-process your data are way better than what you may build.

2. Backups are your best friend: Nothing is more annoying than losing your hard-collected data to a server outage or server wipe. Ensure there are cloud-free copies of your data safely stored somewhere.

3. Account Authentication: Utilize the amazing storage service that you just invested in to ensure that access to various levels of data can be controlled by an administrator to ensure not all employees are getting access to everything.

Now that we have stored our data properly, let us look at how we can pre-process the data properly.

3. Data Pre-processing

Data Pre-Processing is where raw data is cleaned, transformed, and formatted into a structured format that can be easily understood and analyzed by machine learning algorithms. Data preprocessing is essential to ensure that the data is accurate, complete, and consistent and that it meets the requirements of the analytical process.

In more technical terms, data preprocessing includes a range of techniques such as data cleaning, data normalization, data transformation, and data integration. Data cleaning involves removing or correcting essentials, missing, or inaccurate data, while data normalization involves scaling the data to a common range to avoid bias in the analysis. Data transformation is used to convert the data into a format that is suitable for analysis, such as reducing the dimensionality of the data or encoding categorical variables. Finally, data integration involves combining data from multiple sources into a single dataset that can be analyzed.

Let us look at some tips that are essential to this step:

1. Handle Missing Values: Missing data is a common issue in many datasets, and it's important to handle it appropriately. You need to identify and impute missing values using techniques such as mean, median, or mode imputation or use advanced methods like multiple imputations or deep learning-based imputation.

2. Remove Outliers: Outliers can significantly affect the results of your analysis, so it's important to identify and handle them appropriately. You can identify outliers using statistical techniques such as box plots or Z-score and then either remove them or replace them with more appropriate values.

3. Normalize the Data: Normalizing the data involves scaling it so that the values fall within a specific range. This is important to ensure that all variables are treated equally and to avoid any biases in the analysis. You can normalize the data using techniques such as Min-Max scaling or Standardization.

Overall, data preprocessing is a critical step in the data science workflow, as it lays the foundation for accurate and reliable analysis and helps to ensure that the resulting models and insights are valid and useful.

4. Data Governance

Data Governance refers to the overall management of the availability, usability, integrity, and security of an organization's data assets. It involves defining policies, procedures, and standards to ensure that data is accurate, complete, and consistent and that it meets the needs of the business and regulatory requirements.

Data Governance is important because it helps organizations to effectively manage their data assets and ensure that they are adequately secured and used by regulatory and compliance requirements. This may be the most crucial step in your data science pipeline to ensure that your enterprise is safe from the legal aspect of collecting and using data.

Some tips when executing this step properly are:

1. Many industries are subject to regulatory compliance requirements, such as SOC-2 type-2, HIPAA, or GDPR. Ensure you comply with these requirements when using the collected data in your machine-learning pipeline.

2. Establish a Data Governance Team: Establish a dedicated team to oversee the data governance process. This team should comprise individuals from different departments responsible for defining and enforcing data governance policies.

3. Implement Data Quality Controls: Implement quality control checks throughout the data pipeline to ensure that data is accurate, complete, and consistent. This can include conducting data profiling, cleansing, and validation checks.

By implementing these tips, you can ensure that your data science pipeline is governed by a robust and effective data governance process that ensures your data assets' integrity, security, and compliance.

Conclusion

In conclusion, effectively managing data is critical to the success of any enterprise-level data science project. By implementing the best practices for managing data in an MLOps environment, organizations can ensure that their data science pipelines are scalable, reliable, and secure. By following these best practices, organizations can ensure that their data science initiatives are successful, delivering valuable insights that support key business objectives and drive innovation.