ML Operationalization: Building a path to real-world business success
Operationalizing and scaling machine learning to drive business value can be challenging. While many businesses have started diving into it, only 13% of data science projects actually make it to production. Moving from the experiment phase of ML (machine learning) to real-world deployment is difficult, as the journey requires finetuning ML models to fit the practical needs of a business and ensuring the solution can be implemented at scale.
Many organizations struggle with ML operationalization due to a lack of data science and machine learning capabilities, difficulty harnessing data science best practices, and insufficient collaboration between data science and IT operations. Organizing a brainstorming session that includes not only data scientists or ML experts, but also other stakeholders like project management, hardware, and UX who may contribute to define use cases and later help validate them. Operationalization in machine learning requires identifying high-ROI use cases to implement that will capitalize on new business opportunities, according to Forrester’s survey.
Common challenges with ML operationalization
Many organizations get attracted towards buzzwords like “machine learning” and “AI” and spend most of their development budgets pursuing the technology rather than addressing the real-world business problem. The cost of an ML project can be a barrier for start-ups and smaller companies, depending on the nature of the data and the timeline. For non-technical folks, ML projects start with model development and end with its deployment. But practically, this is a continuous improvement process that includes building data pipelines and activities like training, packaging, validating, as well as ongoing monitoring even after its deployment.
ML projects are an investment, and obstacles in the process to operationalize the solution make it even harder for the business to realize value from these solutions.
Here, we look at some common ML operationalization bottlenecks, and the solutions to tackle them.
- Mismatch in skillsets: Data scientists are very good at building models, but their focus is more quantitative than software engineering oriented. On the other hand, IT doesn’t fully understand the nuances of machine learning algorithms and how to support them throughout the lifecycle. This is where MLOps comes in to bridge the gap in the organization and align the different roles for analytics architecture, ML engineering, and end-to-end support.
- Lack of communication, collaboration, and coordination: Proper collaboration between the data scientist team and other teams, like business, engineering, and operations is very crucial. Without proper alignment and feedback, the ML project may not add any real-world business value.
- Unclear ROI: Without a clear framework to test drive the models and their impact to business outcomes, it’s difficult to attribute an accurate value addition. This in turn can cause an organization to pursue a less optimal approach, invest their budget into unnecessary features, or end the project prematurely.
- Insufficient infrastructure: ML models deal with vast amounts of data to train the model. Without the proper infrastructure, the majority of the time is spent on preparing data and dealing with quality issues. Data security and data governance are two crucial factors that need to be considered and addressed in the initial phase.
- Trade-off between prediction accuracy and model interpretability: Generally speaking, complex models are harder to interpret, but provide more accurate predictions. The business will need to decide what’s an acceptable tradeoff to get a “right-sized” solution. Also, businesses sometimes can be reluctant to trust black-box models, so they need to have an adequate level of transparency and explainability.
- Compliance, governance, and security: Data science teams may not always consider other issues like legal, compliance, IT operations, and others that occur after deployment of ML models. In production, it’s important to set up performance indicators and monitor how the model can run smoothly. So, understanding how the ML models run on production data is a crucial part of risk mitigation.
Unfortunately, many ML projects fail at various stages, without ever reaching production. However, with the correct approach and a mix of technical and business expertise, such as that provided by Neal’s data science team, it is possible to avoid or quickly resolve many of these common pitfalls. Neal Analytics can help organizations deploy more ML models to production and achieve a faster time to value for ML projects with the tools, practices, and processes of MLOps.
Starting with the business objective
Neal Analytics’ proven MLOps methodology helps streamline and standardize each stage of the ML lifecycle from model design to production. It allows collaboration between technical and non-technical users alike and empowers everyone to actively participate in the development process.
We have helped many organizations implement an enterprise approach for MLOps, allowing them to overcome the challenges they’re facing to reach production. Our approach includes a process for streamlining model training, packaging, validating, deployment, and monitoring to help ensure ML projects run consistently from end-to-end.
Our successful 5-stage approach to ML projects
- Train: We create and train an initial model based on available data, business requirements, and desired outcomes.
- Package: Once the model is trained, we package up the model to make it easy to test, iterate, and deploy at scale.
- Validate: Later, we help validate models by measuring candidate models against predefined KPIs, deployment testing, and testing application integrations.
- Deploy: On validating, we deploy models by identifying the deployment target, planning the deployment, and then deploying the models to their production environment. We ensure whether the services are implemented to support scalability, data pipelines are automated, and a model selection strategy is implemented.
- Monitor: At last, we monitor models to track behavior, continuously validate KPIs, measure accuracy and response times, watch for drift in model performance, and more.
We can help you successfully deploy and integrate more ML models to production and achieve a faster time to value for ML projects with more efficient model training.
Leveraging both business and technical expertise
Our flexible engagement model offers immediate support, allowing businesses to quickly tackle obstacles faced by the team of data scientists and IT operations, while providing a mix of technical and business expertise.
The team of MLOps and business consulting experts from Neal Analytics can immediately help organizations overcome obstacles to MLOps adoption. Here are a few common example engagements that include:
- Working with the clients to determine MLOps maturity and identifying key areas where implementing MLOps would be beneficial
- Building MLOps Proof-of-Concepts based on sample use cases
- Creating and implementing a roadmap for an organization-wide MLOps rollout.
How Neal’s methodology benefits organizations
- Eliminates guesswork
- Supports consistency
- Enables continuous packaging, validation, and deployment of models to production
- Rapid time to value
As we reach the end of 2021, the MLOps market continues to grow at a rapid pacey. ML applications are a key component for maintaining a competitive advantage, and businesses are realizing that they need a systematic and reproducible way to implement ML models. According to the analyst firm Cognilytica, MLOps is expected to be a $4 billion market by 2025. Neal Analytics has deep expertise in MLOps and can help deliver solutions for unique business challenges across virtually all industries and sectors, enabling customers to realize the full value of their ML projects.
Want to learn more about the process? Here are some tips to consider before starting your MLOps journey.