Machine Learning Workflow

Machine learning (ML) workflows serve as one of the cornerstones of modern artificial intelligence (AI) technology, equipping AI models with a structured approach to learning from data and creating new insights or actions from that knowledge.

But the massive volume of data used and generated by ML workflows has created an urgent need for efficient data management and data center storage solutions. This blog post covers the opportunities and challenges of using machine learning workflows—and how Seagate Mozaic 3+™ can help you meet the unique and demanding storage needs of the top emerging data innovations.

What is a machine learning workflow?

An ML workflow is a structured approach to developing, training, and deploying ML models. It involves several steps, including defining the problem, collecting and preparing data, choosing a model, training the model, evaluating the model, tuning the parameter, and deploying the model.

This workflow is crucial to successfully execute ML projects.

How do machine learning workflows work?

ML workflows process and ‘learn’ from data by using a structured approach composed of iterative, repeatable phases. By executing these phases—learning from them and repeating them again after acquiring new knowledge—these workflows promote continuous improvement that doesn’t require programming or training from an engineer.

ML workflows consist of the following phases:

Problem definition.

Machine learning practitioners clearly define the problem to be solved, including understanding the business context, identifying relevant data sources, and establishing key performance metrics. These goals are critical to set guidelines the workflow can follow for a successful outcome.

Collaboration between practitioners and other stakeholders is key to making sure this phase successfully aligns with the ML workflow’s technical goals and business objectives.

Data collection and preprocessing.

Once the relevant data sources have been identified, data scientists or other professionals tasked with data collection and preprocessing should begin gathering data from the appropriate sources. Once this raw data is collected, preprocessing is performed to clean, organize, and transform this data into a format that the ML workflow can use.

Preprocessing is a critical phase to be sure the workflow is working with validated, high-quality data that has been cleared of missing values and other outliers. Effective preprocessing of data sets up your machine learning workflow for optimal model performance.

Exploratory data analysis.

Once data is processed and ready for use, exploratory data analysis (EDA) should be conducted to identify patterns and trends that best represent the characteristics of the data set. In a supply chain use case, for example, EDA can identify preliminary trends regarding shipping timelines and disruptions occurring across shipping networks.

EDA helps analysts understand what kind of value and insights the data set may offer, which can then be used to choose the ML model and specific feature selection methods that will deliver the best results.

Model selection and training.

With the problem definition and EDA complete, practitioners can choose the ML algorithms that best serve their needs. Once selected, the model will require training to optimize ML parameters and tailor the model to the intended use case.

Model evaluation and tuning.

Training, evaluating, and tuning your ML model will likely require multiple iterations. A number of different evaluations may be used to test your model training for accuracy, precision, and recall.

Cross-validation divides data into subsets to test the model’s ability to perform on its new data sets. Meanwhile, hyperparameter tuning uses a variety of external configurations of the model to test its performance. Many different hyperparameter combinations may be used to evaluate the ML workflow when placed in different contexts.

Model deployment.

Successful deployment requires the full, seamless integration of your trained ML model into a live production environment. Access to data sources must be maintained throughout this transition, and the deployment should be stress-tested to confirm its full functionality, particularly at scale.

Monitoring and maintenance.

While deployed ML models can learn through their own operation and data analysis, they still require continuous monitoring to track performance, diagnose errors, and implement model updates based on post-deployment performance, new data availability, and other factors.

Types of ML workflows.

Different workflows are designed to support varying project needs and team structures. When deploying ML into these workflows, the model must also be tailored to those requirements and optimized for success.

Here are the three most common types of workflows where ML solutions may be deployed:

• Linear. This type of workflow is straightforward, with one task following another without much room for improvisation or change. It’s a simple workflow designed for projects and use cases where the requirements and roles for each task are clearly defined.
  
• Iterative. This workflow features repeated cycles within the larger workflow that allow feedback and revision to be incorporated into the final project. Iterative workflows are great for software development and other projects where input from managers and other key stakeholders is required to produce a successful result.
• Agile. The least structured type of workflow—agile frameworks—maximize flexibility and the ability to collaborate across a number of team members and/or teams. Agile workflows are typically structured into shorter ‘sprints,’ where incremental progress is made in between iterative feedback loops.

Challenges in ML workflows

While ML workflows can offer transformative value to a wide range of business functions, organizations face several challenges in implementing them properly. The most common obstacles include:

• Data quality and availability. If data is poor quality and/or hasn’t been properly preprocessed, it will undercut the ML model’s performance. Similarly, inconsistent availability of data will limit the ability for ML workflows to function.
• Model deployment and monitoring. Properly deploying models can be difficult when optimizing the workflow for success. Even when successfully deployed, continued monitoring and management are required to maintain the performance and efficiency of the workflow, and to perform ongoing training when required.
• Algorithmic bias and fairness. Biases in both the data and the ML model can lead to algorithmic inaccuracies and bias that create ethical concerns around the model’s deployment. Organizations need a system for recognizing biases, addressing known biases, and mitigating the risk of unseen bias to support fairness throughout the ML workflow.

Data storage and management in ML workflows.

Effective, scalable, and highly available data storage and management is critical to maximize the value of ML workflows.

Seagate Mozaic 3+ hard drive platform is designed with these unique needs in mind, equipping your ML workflows with innovative, high-capacity hard drives that can meet your ever-increasing need for storage space, availability, and performance—all while recognizing the limitations organizations face regarding data storage, power availability, and data infrastructure budgets.

Impact of data storage on ML workflow efficiency

ML workflows place significant demands on your storage infrastructure—in terms of required storage volume and data availability. Outdated storage can increase latency that slows down ML processes and inhibits real-time insights.

Organizations that are serious about harnessing the full power of ML workflows need a storage infrastructure that won’t hold back this intelligent technology’s performance. With the right AI storage solutions in place, ML models can deliver insights in real-time and accelerate business processes wherever those models have been deployed.

How Seagate Mozaic 3+ optimizes data management for ML.

Seagate Mozaic 3+ solutions, including Exos® Mozaic 3+ hard drives, break the barriers of areal density in data center storage using heat-assisted magnetic recording (HAMR). HAMR’s data density—combined with its improved thermal and magnetic stability—allow businesses to significantly increase their data storage without expanding physical storage space.

Mozaic 3+ also promotes high-speed write/read performance via a precision-engineered laser. A 12nm integrated controller serves as the highly tailored servo-processor chip at the heart of Mozaic 3+ hard drives—equipping your storage infrastructure with innovative technology that’s on par with your transformative ML workflows.

Benefits of effective machine learning workflows.

When businesses commit the time and resources to implementing a well-defined machine learning workflow, they’re able to realize the following benefits:

• Improved model accuracy. Structured workflows lead to better data handling and feature engineering, resulting in more accurate and high-performing models.
• Efficient workflows for development and other processes. When processes are clearly defined and the ML model is tailored to its intended use case, businesses can realize cost and resource savings while also improving collaboration and coordination among team members—even when deploying ML to more complex iterative and agile workflows.
• Scalability. Robust workflows allow organizations to maintain ML workflows at scale, even when incorporating new, larger datasets or deploying ML models to new projects and use cases.

Integrating ML workflows with existing infrastructure.

Successfully integrating ML workflows can be a challenge even when working with modern systems. For legacy systems, the challenge is even steeper. Poor compatibility can limit the performance and value of your ML model.

Fortunately, Mozaic 3+ drives are 95% the same as other Seagate hard drives, but feature elevated, cutting-edge performance. As a result, these drives are compatible with all systems, making the road to ML adoption much easier.

Businesses can position ML workflows for a successful and complete integration by verifying each piece of the technology’s compatibility prior to implementation, and by following our recommended ML workflow best practices.

Best practices in ML workflows.

Here are our recommendations to position your ML workflow for the best results and ROI possible:

• Use iterative implementation workflows.
• Collect feedback and guidance from a wide range of stakeholders to verify alignment of technical and business goals.
• Invest in supportive technologies designed for a seamless fit with existing infrastructure.
• Choose an ML model based on your business objectives and intended use case.
• Commit to ongoing management and retraining to keep the ML model optimized for peak performance.

Conclusion

ML workflows give organizations new, powerful capabilities to transform massive data sets into granular insights and actions. To achieve these operational advantages, businesses must be careful to properly design, test, and implement their ML models for their intended use case.

The right storage infrastructure is also critical. New business capabilities require innovative storage that’s up to the difficult task of supporting the high-powered operations of an ML model. That’s what Seagate Mozaic 3+ can do for your business.

Learn more about Seagate’s ability to support your ML and AI ambitions. Explore our innovative solutions for AI storage with Mozaic 3+ today.

Mass-capacity storage at unprecedented areal densities.

Equip your business with the high-performance, high-density data storage required to support machine learning and AI innovation.

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