Modern artificial intelligence (AI) technology relies on machine learning workflow to teach AI models how to learn from data and make new decisions.
Due to the massive amount of data generated and processed by ML workflows, appropriate data management and data center storage solutions are needed. The possibilities and difficulties of implementing machine learning workflows are discussed in this blog article, along with how Seagate Mozaic 3+ can assist you in satisfying the particular and exacting storage requirements of the most cutting-edge data innovations.
What is ML Workflow?
An organized method for creating, honing, and implementing ML models is called an ML process. The process entails a number of processes, such as problem definition, data collection and preparation, model selection, training, evaluation, parameter tuning, and model deployment.
How do machine learning workflows work?
Machine learning workflow use an organized method made up of repeating, iterative steps to process and “learn” from data. These workflows encourage continual growth without the need for programming or engineer training by carrying out these phases, machine learning from them, and repeating them after gaining new knowledge.
The following stages make up ML workflows:
Problem definition
The problem to be solved is precisely defined by machine learning practitioners, who also develop important performance criteria, comprehend the business environment, and locate pertinent data sources. These objectives are essential for establishing rules that the process can adhere to in order to produce a successful result.
For this phase to successfully align with the technical aims and business objectives of the ML workflow, practitioners and other stakeholders must work together.
Data collection and preprocessing
Data scientists or other experts charged with data collection and preparation should start collecting data from the right sources as soon as the pertinent ones have been located. Following collection, preprocessing is done to clean, arrange, and convert the raw data into a format that the machine learning pipeline can utilize.
To ensure that the workflow is using validated, high-quality data that has been free of missing values and other outliers, preprocessing is an essential step. For the best model performance, your machine learning workflow is set up with efficient data preprocessing.
Exploratory data analysis
Exploratory data analysis (EDA) should be carried out to find patterns and trends that best capture the features of the data collection after it has been processed and made usable. For instance, EDA can detect early patterns about delivery schedules and interruptions across shipping networks in a supply chain use case.
In order to select the ML model and particular feature selection techniques that will yield the best results, analysts can use EDA to better grasp the potential value and insights the data collection may give.
Model selection and training
Practitioners can select the ML algorithms that best meet their needs once the problem specification and EDA are finished. After being chosen, the model must be trained in order to maximize machine learning parameters and customize it for the specific use case.
Model evaluation and tuning
It will probably take several iterations to train, assess, and fine-tune your machine learning model. You can test your model training for accuracy, precision, and recall using a variety of evaluations.
To assess the model’s performance on its new data sets, cross-validation separates the data into subsets. In the meantime, hyperparameter tuning tests the model’s performance using a range of external setups. When applied in various scenarios, a wide variety of hyperparameter combinations can be utilized to assess the ML workflow.
Model deployment
Your trained ML model must be fully and seamlessly integrated into a real production environment for deployment to be successful. Throughout this shift, data sources must remain accessible, and the deployment should be stress-tested to ensure complete functionality, especially at scale.
Monitoring and maintenance
Even though deployed machine learning models are capable of learning on their own through data analysis and operation, they still need to be continuously monitored in order to measure performance, identify problems, and apply model upgrades depending on post-deployment performance, the availability of fresh data, and other variables.
Types of ML workflows
Workflows are made to accommodate different team configurations and project requirements. The model must also be customized to meet those needs and successful when ML is implemented into these operations.
The three most typical workflow categories in which machine learning solutions can be used are as follows:
Linear
There is little opportunity for improvisation or change in this kind of process, which is simple and involves one activity after another. It’s a straightforward workflow made for use cases and projects with well-defined responsibilities and needs for every action.
Iterative
This workflow incorporates comments and revisions into the final product through several cycles within the bigger workflow. For software development and other projects where managers’ and other important stakeholders’ participation is necessary to get a successful outcome, iterative workflows are ideal.
Agile
Agile frameworks, which are the least organized kind of workflow, optimize flexibility and teamwork among several participants. Agile processes are usually organized into shorter “sprints,” with iterative feedback loops interspersed with incremental progress.
Challenges in ML workflows
Although machine learning workflow have the potential to revolutionize a variety of commercial operations, their effective implementation presents a number of obstacles for enterprises. The most typical challenges consist of:
Data quality and availability
Poor quality and/or improperly preprocessed data will hinder the effectiveness of the machine learning model. In a similar vein, irregular data availability will restrict the functionality of ML operations.
Model deployment and monitoring
It can be challenging to install models correctly when streamlining the process for success. Even once a deployment is successful, continuing management and monitoring are necessary to keep the workflow operating smoothly and efficiently and to conduct continuous training as needed.
Algorithmic bias and fairness
Both the data and the ML model may have biases that result in algorithmic errors and bias, raising ethical questions about the model’s application. To promote fairness across the ML workflow, organizations require a mechanism for identifying biases, resolving known biases, and reducing the possibility of unknown bias.
Data storage and management in ML workflows
To optimize the value of machine learning operations, data management and storage must be efficient, scalable, and highly available.
The Seagate Mozaic 3+ hard drive platform was created with these particular requirements in mind. It provides your machine learning workflow with cutting-edge, high-capacity hard drives that can satisfy your growing demands for performance, availability, and storage space while acknowledging the constraints that businesses have with regard to data infrastructure budgets, power availability, and storage capacity.
Impact of data storage on ML workflow efficiency
When it comes to the necessary storage volume and data availability, machine learning workflow put a lot of strain on your storage infrastructure. Older storage might raise latency, which hinders real-time insights and slows down machine learning operations.
A storage infrastructure that won’t impede the functioning of this intelligent technology is essential for organizations that are serious about utilizing ML workflows to their full potential. Wherever ML models are used, they may speed up company processes and provide insights instantly with the correct AI storage solutions.
How Seagate Mozaic 3+ optimizes data management for ML
Seagate Mozaic 3+ solutions, such as Exos Mozaic 3+ hard drives, use heat-assisted magnetic recording (HAMR) to overcome the limitations of areal density in data center storage. Businesses can greatly extend their data storage without adding more physical storage space with HAMR’s data density and enhanced thermal and magnetic stability.
Through the use of a precisely designed laser, Mozaic 3+ also facilitates high-speed write/read performance. The highly customized servo-processor chip at the core of Mozaic 3+ hard drives is a 12nm integrated controller, giving your storage infrastructure cutting-edge technology comparable to your game-changing machine learning operations.
Benefits of machine learning workflows
Businesses can reap the following advantages when they invest the time and money necessary to establish a clearly defined machine learning workflow:
Precision
Improved feature engineering and data handling through structured processes produce more precise and effective models.
Optimization
Even when using ML to more intricate iterative and agile workflows, firms can achieve cost and resource savings while simultaneously enhancing teamwork and coordination provided procedures are well-defined and the ML model is customized to the specific use case.
Scalability
Even when adding new, bigger datasets or applying ML models to new projects and use cases, robust workflows enable organizations to keep ML workflows running at scale.
Integrating ML workflows with existing infrastructure
Even with contemporary systems, integrating ML operations successfully can be difficult. The task is significantly more difficult for legacy systems. Inadequate compatibility may restrict your ML model’s usefulness and performance.
Thankfully, Mozaic 3+ drives offer improved, state-of-the-art performance while being 95% identical to other Seagate hard drives. These drives are therefore interoperable with all platforms, which greatly facilitates the adoption of ML.
By following its suggested ML workflow best practices and confirming the compatibility of every component of the technology before implementing it, businesses may set up ML workflows for a successful and comprehensive integration.
In conclusion
Organizations may now convert large data sets into detailed insights and actions with ML processes. Businesses must take care to appropriately develop, test, and deploy their ML models for their intended use case in order to realize these operational benefits.
It’s also crucial to have the proper storage infrastructure. Innovative storage that can handle the challenging duty of supporting the powerful operations of an ML model is necessary for new commercial capabilities. Seagate Mozaic 3+ can help your company achieve that.
Find out more about how Seagate can help you achieve your ML and AI goals. Discover it cutting-edge AI storage options with Mozaic 3+.
