A Great Model is Not Enough: Deploying AI Without Technical Debt

DataKitchen
Jul 16 · 11 min read

DataOps in Data Science and Machine Learning

AI and Data Science are all the rage, but there is a problem that no one talks about. Machine learning tools are evolving to make it faster and less costly to develop AI systems. But deploying and maintaining these systems over time is getting exponentially more complex and expensive. Data science teams are incurring enormous technical debt by deploying systems without the processes and tools to maintain, monitor and update them. Further, poor quality data sources create unplanned work and cause errors that invalidate results.

A couple of years ago, Gartner predicted that 85 percent of AI projects would not deliver for CIOs. Forrester affirmed this unacceptable situation by stating that 75% of AI projects underwhelm. We can’t claim that AI projects fail only for the reasons we listed. We can say, from our experience working with data scientists on a daily basis, that these issues are real and pervasive. Fortunately, data science teams can address these challenges by applying lessons learned in the software industry.

Traditional Model Development versus AI

AI is most frequently implemented using machine learning (ML) techniques. Building a model is different than traditional software development. In traditional programming, data and code are input to the computer which, in turn, generates an output. This is also true of traditional modeling where a hand-coded application (the model) is input to a computer, along with data, to generate results.

In machine learning, the code can learn. The ML application trains the model using data and target results. An ML model developer feeds training data into the ML application, along with correct or expected answers. Errors are then fed back into the learning algorithm to boost the model’s precision. This procedure continues until the model reaches the target level of accuracy. When complete, this process generates a set of parameters that are described by a set of files (code and configuration). In production, the model evaluates input data and generates results. Figure 1 shows the different processes governing traditional and ML model development.

Figure 1: Traditional model programming versus machine learning model development

Below, Figure 2 further elaborates on the complex set of steps that are involved in model building. Naturally, AI projects begin with a business objective. Data is often imperfect so the team has to clean, prepare, mask, normalize and otherwise manipulate data so that it can be used effectively. Feature extraction identifies metrics (measured values) that are informative and facilitate training. After the building and evaluation phases (see Figure 1), the model is deployed, and its performance is monitored. When business conditions or requirements change, the team heads back to the lab for additional training and improvements. This process continues for as long as the model is in use.

Figure 2: The complex sequence of steps in building an ML model

AI development and deployment is a complex workflow. If executed manually, it is slow, error-prone and inflexible. The actual output of the model development process (a set of files, scripts, parameters, source code, …) is only a small fraction of what it takes to deploy and maintain a model successfully. Figure 3 below shows the Machine Learning Code in a system context. Notice that the ML code is only a small part of the overall system.

Figure 3: Machine Learning Code is only a small part of the overall system.

Model creation and deployment commonly use the tools shown in Figure 4. Note that this is only a portion of what is required by the system in Figure 3. If the responsibility for these processes and toolchains falls on the data science team, they can end up spending the majority of their time on data cleaning and data engineering. Unfortunately, this is all too common in contemporary enterprises. Addressing this situation requires us to take a holistic view of the value pipeline and analytics creation.

Figure 4: Model development and deployment requires a complex toolchain

Two Journeys / Two Pipelines

We conceptualize AI (and all data analytics) as two intersecting pipelines. In the first pipeline, data is fed into AI and ML models producing analytics that deliver value to business stakeholders. For example, an ML model reviews credit card purchases and identifies potential fraud. We call this the “Value Pipeline.”

The second pipeline is the process for new model creation — see Figure 1 and Figure 2. In the development pipeline, new AI and ML models are designed, tested and deployed into the Value Pipeline. We call this the “Innovation Pipeline.” Figure 5 depicts the Value and Innovation Pipelines intersecting in production.

Figure 5: The Value and Innovation Pipelines

Conceptually, each pipeline is a set of stages implemented using a range of tools. The stages may be executed serially, parallelized or contain feedback loops. In terms of artifacts, the pipeline stages are defined by files: scripts, source code, algorithms, html, configuration files, parameter files, containers and other files. From a process perspective, all of these artifacts are essentially just source code. Code controls the entire data-analytics pipeline from end to end: ideation, design, training, deployment, operations, and maintenance.

When discussing code and coding, data scientists who create AI and ML models, often think “this has nothing to do with me.” I am a data analyst/scientist, not a coder. I am an ML tool expert. In process terms, what I do is just a sophisticated form of configuration. This is a common misconception and it leads to technical debt. When it is time for that debt to be paid, the speed of new analytics development (cycle time) will slow to a crawl.

AI / ML / Data Science Work Is Just Code

Tools vendors have a business interest in perpetuating the myth that if you stay within the well-defined boundaries of their tool, you are protected from the complexity of software development. This is ill-considered albeit well-meaning.

Don’t get us wrong. We love our tools, but don’t buy into this falsehood. The $50+ billion AI market is divided into two segments: “tools that create code” and “tools that run code.” The point is — AI is code. The data scientist creates code and must own, embrace and manage the complexity that comes along with it.

Lessons Learned — DataOps

The good news is that when AI is viewed through a different lens, it can leverage the same processes and methodologies that have boosted the productivity of software engineering 100x in the last decades. We call these techniques (collectively) DataOps. It includes three important methodologies: Agile Software Development, DevOps and statistical process controls (SPC).

  • Studies show that software development projects complete significantly faster and with far fewer defects when Agile Development, an iterative project management methodology, replaces the traditional Waterfall sequential methodology. The Agile methodology is particularly effective in environments where requirements are quickly evolving — a situation well known to data science professionals. Some enterprises understand that they need to be more Agile and that’s great. (Here’s your chance to learn from the mistakes of many others.) You won’t receive much benefit from Agile if your quality is poor or your deployment and monitoring processes involve laborious manual steps. “Agile development” alone will not make your team more “agile.”
  • DevOps, which inspired the name DataOps, focuses on continuous delivery by leveraging on-demand IT resources and by automating test and deployment of code. Imagine clicking a button in order to test and publish new ML analytics into production. This merging of software development and IT operations reduces time to deployment, decreases time to market, minimizes defects, and shortens the time required to resolve issues. Borrowing methods from DevOps, DataOps brings these same improvements to data science.
  • Like lean manufacturing, DataOps utilizes statistical process control (SPC) to monitor and control the Value Pipeline. When SPC is applied to data science, it leads to remarkable improvements in efficiency and quality. With SPC in place, the data flowing through the operational system is verified to be working. If an anomaly occurs, the data team will be the first to know, through an automated alert. Dashboards make the state of the pipeline transparent from end to end.

DataOps eliminates technical debt and improves quality by orchestrating the Value and Innovation Pipelines. It catches problems early in the data life cycle by implementing tests at each pipeline stage. Further, it greatly accelerates the development of new AI, enabling the data science team to respond much more flexibly to changing business conditions.

DataOps for your AI and ML Project

DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics. DataOps is not any specific vendor’s solutions. It leverages automation, tools and other best practices.

You can implement DataOps for your AI and ML project yourself by following these seven steps:

Step 1 — Add Data and Logic Tests

To be certain that the data analytics pipeline is functioning properly, it must be tested. Testing of inputs, outputs, and business logic must be applied at each stage of the data analytics pipeline. Tests catch potential errors and warnings before they are released so the quality remains high. Manual testing is time-consuming and laborious. A robust, automated test suite is a key element in achieving continuous delivery, essential for companies in fast-paced markets.

Step 2 — Use a Version Control System

All of the files and processing steps that turn raw data into useful information are source code. All of the artifacts that data scientists create during ML development are just source code. These files control the entire data-analytics pipeline from end to end in an automated and reproducible fashion. When these file artifacts are viewed as code, they can be managed liked code.

In so many cases, the files associated with analytics are distributed in various places within an organization without any governing control. A revision control tool, such as Git, helps to store and manage all of the changes to code. It also keeps code organized, in a known repository and provides for disaster recovery. Revision control also helps software teams parallelize their efforts by allowing them to branch and merge.

Step 3 — Branch and Merge

When an analytics professional wants to make updates, he or she checks a copy of all of the relevant code out of the revision control system. He or she then can make changes to a local, private copy of the code. These local changes are called a branch. Revision control systems boost team productivity by allowing many developers to work on branches concurrently. When changes to the branch are complete, tested and known to be working, the code can be checked back into revision control, thus merging back into the trunk or main code base.

Figure 6: With “Branch and Merge” team members can work on features independently without impacting each other or value pipeline.

Branching and merging allow the data science team to run their own tests, make changes, take risks and experiment. If a set of changes proves to be unfruitful, the branch can be discarded, and the team member can start over.

Step 4 — Use Multiple Environments

Infrastructure-as-a-service (IaaS) (or alternatively, platform-as-a-service or virtualization) has evolved to the point where new virtual machines, operating systems, stacks, applications and copies of data can be provisioned, with a click or command, under software control. DataOps calls for development and test environments that are separate from operations. The last thing that anyone would want is for a data scientist making changes to crash enterprise-critical analytics. When the team creating new analytics is given their own environments, they can iterate quickly without worrying about impacting operations. IaaS makes it easy to set-up development and test system environments that exactly match a target operations environment. This helps prevent finger-pointing between development, quality assurance and operations/IT.

It’s worth reemphasizing that data scientists need a copy of the data. In the past, creating copies of databases was expensive. With storage on-demand from cloud services, a Terabyte data set can be quickly and inexpensively copied to reduce conflicts and dependencies. If the data is still too large to copy, it can be sampled.

Step 5 — Reuse & Containerize

Data science team members typically have a difficult time leveraging each other’s work. Code reuse is a vast topic, but the basic idea is to componentize functionalities in ways that can be shared. Complex functions, with lots of individual parts, can be containerized using a container technology like Docker and Kubernetes. Containers are ideal for highly customized functions that require a skill set that isn’t widely shared among the team.

Step 6 — Parameterize Your Processing

The data analytics pipeline should be designed with run-time flexibility. Which dataset should be used? Is a new data warehouse used for production or testing? Should data be filtered? Should specific workflow steps be included or not? These types of conditions are coded in different phases of the data analytics pipeline using parameters. In software development, a parameter is some information (e.g., a name, a number, an option) that is passed to a program that affects the way that it operates. With the right parameters in place, accommodating the day-to-day needs of the users and data science professionals becomes a routine matter.

Step 7 — Orchestrate Two Journeys

Many data science professionals dread the prospect of deploying changes that break production systems or allowing poor quality data to reach users. Addressing this requires optimization of both the Value and Innovation Pipelines. In the Value Pipeline (production), data flows into production and creates value for the organization. In the Innovation Pipeline, ideas, in the form of new analytics and AI, undergo development and are added to the Value Pipeline. The two pipelines intersect as new analytics deploy into operations. The DataOps enterprise masters the orchestration of data to production and the deployment of new features both while maintaining impeccable quality. With tests (statistical process control) controlling and monitoring both the data and new development pipelines, the dev team can deploy without worrying about breaking the production systems. With Agile Development and DevOps, the velocity of new analytics is maximized.

Figure 7 below shows how the seven steps of DataOps tie directly into the steps for model development shown in Figure 2. For example, orchestration automates data preparation, feature extraction, model training and model deployment. Version control, branch and merge, environments, reuse & containers and parameterization all apply to these same phases. Tests apply to all of the phases of the model life cycle.

Figure 7: How the 7 steps of DataOps related to model development

Conclusion

While AI and data science tools improve the productivity of model development, the actual ML code is a small part of the overall system solution. Data science teams that don’t apply modern software development principles to the data lifecycle can end up with poor quality and technical debt that causes unplanned work.

DataOps offers a new approach to creating and operationalizing AI that minimizes technical debt, reduces cycle time and improves code and data quality. It is a methodology that enables data science teams to thrive despite increasing levels of complexity required to deploy and maintain AI in the field. It decouples operations from new analytics creation and rejoins them under the automated framework of continuous delivery. The orchestration of the development, deployment, operations and monitoring toolchains dramatically simplifies the daily workflows of the data science team. Without the burden of technical debt and unplanned work, they can focus on their area of expertise; creating new models that help the enterprise realize its mission.

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