How to Optimize and Manage Machine Learning Lifecycle: A Helpful Guide

People who are partially familiar with machine learning think that these projects include model training, data processing, and model deployment. Even though this isn’t untrue, what you need to remember is that it involves a lot more things.

Besides that, it’s of huge importance that data and business understanding, along with data analytics, data gathering methods, model assessment, and model building are present as well. Even once the deployment is over, continuous maintenance is necessary.

There are various steps that are part of the machine learning life cycle, and they are essential because they offer the structure to the entire project, and, at the same time, split the business’s resources in an efficient way.

Additionally, there are different steps that need to be taken in order to ensure a smooth machine-learning project, and today, a couple of them will be discussed.

Everything Starts With Planning

This stage consists of evaluating the scope, success metric, and practicability of the ML usage. It’s essential to comprehend how to utilize machine learning to enhance the existing process.

There are several questions that need to be asked in these instances, like do you really need machine learning? Are there any other methods that can be employed instead? Moreover, you must define measurable and clear success metrics for the company, along with economic (key performance indicators) and machine learning models (F1 score, Accuracy, and AUC).

Then it’s time to focus on developing a feasibility report that’s going to include this information below:

• Availability of the data – Do you have enough data at your disposal to train the model?
• Applicability – How can you benefit from this solution?
• Legal constraints – Do you gather data in an ethical way?
• Scalability and robustness – Is this application scalable and robust?
• Explainability – Are you able to explain how the machine learning model provides the results?
• Availability of the resources – Do you have enough storage, computing, and human resources? Are you collaborating with experts?

You Need To Have An Extensive Data Strategy

If you want your machine learning solution to be effective and overall successful, then you need to craft a solid data strategy. Many experts suggest that adopting data can oftentimes be one of the biggest challenges in these instances.

If there are some issues with it, machine learning scientists will most likely spend a lot of time trying to resolve these problems, or if they figure that they are not able to do so, they will end up being very flustered.

Luckily, there’s a solution that can be of huge aid to the data teams. Namely, they can resort to some of the best MLOps tools to help them become a lot more efficient, decrease potential risks, and, simultaneously, accomplish quicker model development.

In addition, these tools offer reproducibility of ML pipelines, allowing better collaboration among all the members of data teams.

Speaking of data strategy, you need to make sure that it involves three pivotal elements if you want it to be effective. They include the following:

1. Data belongs to the entire organization, not only to the individuals who have gathered it.
2. Data is democratized, which means that it can easily be accessed and that it must adhere to regulatory and legal demands. This is important to remember because there are a lot of organizations these days that deal with various issues as far as this is concerned.
3. Data is put to work through machine learning so you can become a lot more effective, make well-informed decisions, and come up with different, useful inventions.

Moving On To Data Acquisition And Comprehension

Once this phase is completed, it’s time to move on to the next one, which consists of obtaining, exploring, and preparing the data that’s going to be employed with different machine learning models.

• Acquire data – You need to identify and collect every crucial data source. This refers to both external and internal sources. They typically involve data lakes, databases, logs, APIs, sensors, and many other things.
• Process data – Before you start with modeling, you need to clean the raw data and turn it into a unified dataset that’s optimized for various machine learning algorithms. This whole process includes dealing with missing values, encoding categorical variables, deduplicating data, scaling numerical features, etc.
• Analyze data – During this stage, you must conduct detailed exploratory data analysis so you can get an insight into the quality, characteristics, and restrictions of the data.

Model Engineering

During this stage, you need to utilize every single piece of information you acquired from the first phase in order to construct and train a machine-learning model. There are a few steps that must be taken in this phase, such as:

1. Building an efficient model architecture, which you can accomplish by conducting thorough research
2. Defining model metrics
3. Validating and training the model on the validation and training dataset
4. Supervising metadata, experiments, features, machine learning pipelines, and code changes
5. Executing model ensembling and compression
6. Interpreting the results by consulting domain knowledge professionals

You need to focus on the code quality, model architecture, model training, machine learning experiments, and ensembling. Once this part is completed, there are a couple more things to be done in order for this project to be perceived as successful, and they include model evaluation, model deployment, monitoring, and maintenance.

You Need To Have Experts On Your Team

If you want this entire project to be effective, then it’s of huge importance to surround yourself with people who are experts in this field. Keep in mind that if you are working with professionals, then it’s highly likely that everybody is going to adopt, embrace, and above all, implement the solution.

If your company doesn’t currently have people who are skilled enough when it comes to this, then you need to implement efficient employee training.

Even though there’s no denying that machine learning is an extremely omnipotent tool that many companies can reap the benefits from, it’s still very important to understand how to properly use it if you want to reduce any risks and errors.