Machine learning (ML) models identify patterns in business data to make predictions. These predictions can prove valuable for your enterprise in various ways, such as helping you build accurate customer profiles for specific demographics of your audience or forecasting product demand for optimized inventory stocking. When you need to make critical business decisions, it's essential to have up-to-date, accurate and high-quality data on your side, and machine learning models can read this data to streamline your operations.
Machine learning operations (MLOps) optimize this process further by speeding up ML model development and offering data validation, continuous monitoring and deployment and model management. Learn more about the value of machine learning operations in business and how this process can advance your goals.
MLOps is a process that automates and optimizes various stages involved in creating machine learning models, including data discovery & acquisition, model testing, deployment, monitoring and governance.
MLOps enables users to leverage machine learning technologies by facilitating seamless collaboration between people, data and processes. With MLOps, data scientists who work with information can more easily collaborate with ML engineers who develop ML models and put them into production. Faster and more efficient communication between the various teams involved in creating ML models allows for shorter development times and more reliable models, creating better outcomes.
The initial phases of MLOps involve determining a business's problem and identifying a solution by analyzing the companies needs, its data, its users and the users' problems. This process known as scoping is where data scientists must determine what value their available data holds for solving this problem. It key for data be properly labeled to avoid ambiguity and ensure uniformity. This data is then used to train the machine learning model.
When the ML model is developed, trained and validated, it is then brought into production using DevOPs principles, like continuous delivery and monitoring. We explore more about DevOps principles and their relation to MLOps below.
DataOps and DevOps are similar to MLOps in many ways, but they also pose notable differences. The DevOPs process helps optimize software development, enabling information technology (IT) and application development teams within a company to collaborate and complete tasks more efficiently. Much like MLOps, it enhances deployment speeds and introduces automation into software and application creation while maintaining flexibility.
DataOps fosters improved communication and collaboration between data engineers and other data managers and consumers within an organization. It involves enhancing data analysis, delivery and management through various measures, including automated data flow and streamlined integration.
MLOps and DataOps are often thought of as subsets of DevOps, with both processes using similar concepts from DevOps in optimizing business operations, cross-team collaboration and data flow. Compared to DevOps and DataOps, however, MLOps includes a few more nuanced steps to achieve ML model training, such as algorithm selection and data labeling. Machine learning's applicability for solving a given business problem must be verified using proof of concept, which helps identify or refine the appropriate machine learning algorithm for addressing the challenge.
Additionally, while the traditional application development procedures involved in DevOps often occur linearly, MLOps can require more complicated feedback loops due to experimentation and model retraining. Similarly, you may need several multi-step pipelines when deploying an ML model for handling continuous data retraining, revalidation and redeployment.
MLOps offers many advantages for businesses that want to adopt machine learning or refine their current ML practices, but it comes with its share of hurdles. Two notable obstacles to your desired level of accuracy when using ML processes are model concept drift and data drift.
Machine learning models are based on what a business currently needs — in other words, its use case. Suppose an organization's use case changes over time, which is likely to happen as enterprises goals continue to evolve. In order to satisfy for changing needs of business, now the data will potentially require a change, and its models will require retraining to fit this new information. The gap between the business's current use case and its new targeted use case creates differences in the relationships between its input and output data, which is referred to as model concept drift.
Data drift is similar to model concept drift, but this concept deals more specifically with the data itself. A business may gather information on a demographic of users and use this data to train their ML model, but then realize that this information doesn't match the user base they're using it on. Data drifts can happen suddenly and drastically or over time — such as your user base shifting up or down in its average age or adopting a new preferred social media channel, such as YouTube over Instagram.
With the automation MLOps provides, data scientists can work more effectively with other tech teams without manually managing every aspect of the process. Daily governance tasks can be completed with a new level of efficiency and free up time for these niche professionals to address other things that may require more attention.
Already, various industries have felt the impactful benefits of MLOps tools in their own operations. Some sectors that have seen the advantages of integrating this process include:
MLOps can help increase virtually any business's productivity and aid them in tightening up operations, but ML best practices must be followed for users to get the most benefit from this technology. Adhering to MLOps best practices can help your business avoid the hurdles mentioned above, ensuring the technology works for your company's needs:
MLOps helps businesses grow by unifying multiple teams and tech professionals within separate areas of an organization, offering them more control and better communication and creating a company culture of collaboration. Applying MLOps to your business model can be conceptualized with the following process stages:
Having effective software at hand is just as crucial as fostering a collaborative MLOps company environment. High-quality data testing and pipeline building platforms are essential for gathering the information you need to create your desired machine learning model. You must be able to work with data with low latency, analyze information and gain insights as needed, clean the data in preparation for ML training and much more.
The Dextrus platform can help you accomplish these tasks by enabling self-service data cleaning, wrangling and reporting. Visualizations built within the pipeline let data scientists and company leadership quickly view information and form insights, which is also useful for stakeholders in their decision-making. Some leadership teams may hesitate to adopt machine learning because of its relative newness and unfamiliarity, but they may become more inclined when they have an easier and more comprehensive way to receive accurate data for ML modeling.
Once an ML model is packaged and deployed, monitoring is vital to ensure the desired performance and avoid or respond to model decay. Models can decay because of data drift, but real-world events such as major economic changes can also bring a model out of context depending on what it is used for. Therefore, having a feedback loop that can refer back to the data training stage even after deployment helps you — or an automated model itself — decide whether updates are needed and what kind of changes must be made.
Three types of metrics a system might use to analyze model performance include:
An increase in machine learning models within business settings, especially for projects involving time series data and predictive analytics, means the future of MLOps will continue steadily growing as companies adopt this advanced technology. Increased usage of machine learning will subsequently call for processes and tools that can make this technology more efficient and easier to integrate within enterprise operations. By adhering to MLOps best practices, businesses can ethically use ML models and scale them up or down as needed while ensuring they function as intended during production.
Having educated leadership, harmonious, knowledgeable teams, and effective software will determine whether machine learning techniques and MLOps will work for many businesses. Siloed organizations will experience more obstacles if data scientists and IT engineers have little or no access to each other's resources and data. To create the best possible ML infrastructure to solve a given challenge and accommodate for future data shifts, business leaders will need to think about:
If you're ready to explore the benefits of machine learning and MLOps, now is the best time to do so. With more enterprises utilizing this technology, you can benefit from seriously considering this solution to stay ahead of the competition in your industry. To begin building and operationalizing machine learning models for accurate business classifications and predictions, you need a platform like Dextrus.
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