Many organizations are employing machine learning to streamline their operations and stay ahead of the competition in today’s business landscape. A machine learning project is also an excellent way to leverage internal and external data sources in a way that gives you an edge over the competition.
Avoid pitfalls in ML workflow management by understanding which machine learning tools and other essentials you should have:
1. A Clear Business Case
Every organization has unique needs, including improved forecasting, more responsive customer service, speedier product development, or more effective equipment lifecycle management. Whatever it is, determine why you are using machine learning and how you evaluate success.
A well-defined use case helps your ML team develop data-driven solutions to fulfill your goals. This narrows your scope for tool and vendor selection, and also reduces time to production, costs, and missed opportunities.
2. Engaged Stakeholders
Having the right machine learning tools offers better returns than expected when you get stakeholders involved in the process. Stakeholders add value to your machine learning program by bringing diverse perspectives based on how they will use and be impacted by your machine learning models. This perspective helps your team evaluate tools and vendors to develop innovative ML projects and simplify complex data-driven processes.
For instance, data warehouse integration to your pipelines allows data science and engineering teams to run more experiments, make dataset iterations faster, and deploy ML models quicker without sacrificing reproducibility.
3. Capable Expertise
Due to the complexity of machine learning, you’ll want a team of professionals to keep your MLOps on track around the clock. Forming a dependable machine learning (ML) team is essential, but you must also provide them with the necessary technology stack and leadership to execute a successful, steady program despite unavoidable continuity gaps.
4. Data Orchestration
Data orchestration automates processes related to data management, ensuring that the data used is accurate, updated, deduplicated, and accessible. Having big data won’t put you ahead of the competition; you must understand what’s relevant and crucial for your ML projects. With data orchestration tools, you can combine the correct data from multiple sources for the right purpose.
Properly orchestrated data pipelines guarantee good data quality and, by extension, accurate machine learning model results.
5. Humans in the Loop
ML models are sophisticated and advanced. Even so, they can’t beat human judgment. Adding human input to automated processes speeds up iterations and improves accuracy. As Pachyderm and Toloka showed with crowdsourced data, humans are more accurate at data labeling.
6. Data Responsive Tools
Not all available machine learning tools in your MLOps platform focus on your data. While they can process a lot of data, they do not keep track of its source, history, transformations, usage across the code-data loop, and storage throughout the environment. Failure to monitor changes and edits to your data may cost you, especially when required for compliance.
Invest in applications with data-responsive features like version control to rein in machine learning-related costs on storage, access, and development. Other advantages include faster, smoother ML workflows and better collaboration across teams.
Even with the right machine learning tools, including data-centric ones, some teams still struggle with manually debugging and tracking changes to within your models and data. Instead, reproduce models at any scale by incorporating data-driven pipelines with automated labeling into workflows to create high-quality training data. Just as your code is versioned in GitHub, your data can be updated on top of the lineage.
8. An Integrated Technology Stack
Your MLOps platform will work best with machine learning tools that seamlessly integrate. Choosing applications with established partnerships ensures long-term durability, flexibility, and scalability. Interoperability and integration are essential when choosing software since they simplify debugging and troubleshooting.
If you need guidance in building a canonical ML technology stack, check out platform partner alliances like the AIIA.
9. Enterprise Support
Even if your ML team consists of experts, they can’t know everything. So when critical data for your MLOps is on the line, it pays to have experienced support that’s always ready to assist your data science professionals, developers, and engineers.
Pachyderm Enterprise customers can count on our seasoned ML product engineers to provide troubleshooting anytime and simplify product onboarding. So you can direct your attention to what matters the most—achieving your machine learning goals.
Choose the Right Machine Learning Tools for Ops
Building an end-to-end enterprise ML platform is not easy. With the right resources, your organization can reap the benefits of scalable and resilient MLOps. Learn how to choose the right MLOps platform. Get the report.