Understanding and communicating strategic goals helps speed MLOps tempo across different levels of an organization:
- Individual Contributor: A clear strategic goal helps create clear project goals for tasks. Clear tasks helps build morale, helps identify bottlenecks, and increase work effciency and value from it. It also helps the quality of info they push up the chain of command.
- Manager: Larger strategic is crucial for project management, aiding in resource allocation, task prioritization, and aligning team efforts with organizational goals and timelines. Or for support from specific sponsors.
- Middle and Upper Management: Well defined strategic goals help put context and make informed decisions, evaluating new initiatives, and making informed decisions about scaling operations.
Good strategic goals helps the entire organization from top to bottom, allowing the flow of important information up, and well informed decisions downwards. This helps speed up turnaround times, evaluate if a project is working, and of course, if a ML project aligns with strategic goals.
A good understanding of MLOPs tempo at different levels helps each better navigate the complexity of ML projects.
Strategic goals must start with the ‘why’. The reason it is important to a larger business goal is important. It helps prioritize resources, avoid unnecessary complexity, enables faster decision making for development, or even if a ML solution is needed.
This filters down from the decision makers to the project and product managers, and finally to the data science teams building a ML or AI solution. When everyone understands the intent, it improves communication between teams — a fundamental lynchpin in good operations tempo for any industry. Less time is spent in meetings for clarification, and more on implementation.
Use cases must always start with the end products in mind. Then filter through the technical aspects and limitations. This is the ‘why’ of a ML project, is absolutely essential to determine if the project aligns with the strategic goals, what resources are needed, etc.
The ‘why’ forms the basis of other key questions that need to be asked at the start of the model-building process. It affects the other factors of MLOps tempo significantantly
There are specific why questions that need to be asked that help good strategic goals for ML:
Why are we building this?
ML is not always needed for some use cases. This question helps define if its needed, saving resources and time that could be use elsewhere. It also helps define if there’s more than one use case. This helps scope and sets the direction.
Defining the purpose of the machine learning model is pivotal as it sets the direction for the entire project. It helps to align all the efforts and resources towards a common goal, ensuring efficient use of time and enhancing the MLOps tempo. It he
What do we want it to do?
This focus means placing the business use case and the desired end product at the forefront. What matters is that use case(s) is aligned with the strategic goals. ML model(s) need to be able to answer these use cases.
Knowing what you want to do helps make the why doable. During development, this helps guide the technical strategy and execution. Definitions at this stage reduces trial-and-error, especially during the model building, experimentation, and deployment stages, speeding up the project timeline.
What are potential risks?
Identifying potential risks allows mitigation. Models don’t always work. Good strategic goals helps leadership and build teams innovate and design alternative solutions that accomplish the desired end. It helps establish alternate paths to achieve the strategic goals the organization has for machine learning.
This ability to adapt is critical. Friction between a plan and reality is common. Clear, well communicated, and understood strategic goals prevent unforseen circumstances from stopping or halting the project. It allows a steady MLOps tempo and efficient turnarounds.
When do we want this to be completed by?
Strategic goals helps guide not just timelines, but the timing and coordination of resources to create an ML solution. Not all resources are needed from the start. Specific resources, such as money, shared tools specialists, sponsors, and others must be leveraged at the right time, to avoid taking away from other projects.
Establishing a timeline sets a pace for the work and helps in planning the tasks in an orderly manner. This ensures tasks are prioritized and completed in a sequence that optimizes the project’s tempo, enabling on-time delivery. Time boxing is important, otherwise too much will be spent on certain parts of the project over another.
If the risks and time line outweigh the value generated, you may have to go back to the ‘why’ step. This can seem tedious, but even an informal review can save a bunch of development costs, lost productivity, and time.
How are We Going to Build It?
This is generally left to the operations and build teams. Individuals at a strategic level will be less involved with it. However, answering the ‘why’, ‘what’, ‘risks’, and ‘when’ of the model is really important. It sets the foundations for these teams efforts.
So many ML projects end up out of budget without these being answered. For project and product managers an reference to realign building teams. For building teams, it helps align the managers direct the data scientists, data engineers, and ML engineers by giving them a framework to align with to research, develop, experiment, test, models effectively.
The strategic goals affects the way they build models — no two builds will be the same with different goals.
Case Study: How Strategic Goals Affect MLOps Tempo
During my startup days, a midsized retail firm approached us. They were struggling how impliment ML solutions — operations processes were slow and ML projects weren’t delivering value. The projects in the past had failed to deliver value or results. They realized something was missing.
We started out first by discovery session and workshops trying to understand their strategic goals. We found out very quickly that they had built around a ML algorithm they wanted to use. Rather than asking why they needed to use it.
The first question we zeroed in on was “Why are we building it”. We discovered several of their previous ML projects were not necessary. A number of use cases didn’t require machine learning model, and could be solved with simpler methods like conditional logic. Other use cases did require ML models, but used more a more complex model than needed. We broke up complex use cases into smaller ones that delivered value, which helped in scoping and clear directions for project managers overseeing MLOps teams workflow.
Data and ML Products
Next we focused on the ‘what’. We focused on business use cases and desired end data products. Then helped narrow and refine their strategic goals. This help them reduce a lot of the trial and error during model building, model development and experimentation, and deployment stages. This shorted project timelines and increased effectiveness of resource allocation.
Models don’t always work as planned. So we worked closely with the firm to identify potential risks to ML projects, plan alternatives, and establish criteria when to pull the plug. Clear strategic goals allowed leadership and build teams to recognize when a project was becoming a cost center, allowing them to adjust their efforts. It also helped build teams to innovate and design alternative solutions, ensuring a steady MLOps tempo and efficient turnarounds despite unforeseen challenges. This reduced the amount of cost associated with projects, up and down the chain of ML model development.
We also helped the firm establish realistic timelines, coordinating the use of resources to avoid conflicts with other ongoing projects. Time boxing was used to ensure that resources were allocated for a set period of time. This included streamlining communications so that realistic assements of build teams about timelines reached decision makers quickly.
How to Build the Solution.
We left the question of ‘how’ to the operations and build teams. Stepping back and letting specialists and domain experts helps them learn processes and deliver long term value. We did ensure that we were available to help, as well as setting up sessions to help ensure they had a strong foundation on the processes. This alignment helped project and product managers guide the data scientists, data engineers, and ML engineers effectively.
Following this strategic goal-setting approach, the firm was able to prevent budget overruns and build ML solutions that aligned with and delivered on their strategic goals.
The firm noticed a siginificant improvement in the effectivness of ML itatives, and increased a more efficient development time with higher value than before.
Focused strategic goals require a good understanding of both the business strategy and technical strategy. Each contributes a unique strength, and taking both into account speeds up your MLOps tempo. Business and technical strategy strongly influence strategic goals you set for your ML development.
Business strategy focuses on on how these strategic goals tie into the business’ profitability.
While technical strategy focuses on how to implement those goals using available tech resources.
Each has a distinct effect on MLOps tempo. Unclear business strategy can slow down technical strategy by setting overly ambitious outcomes beyond the available tech. While unclear tech strategy can develop ML models for business use cases where they aren’t needed.
Both business strategy and technical strategy have several factors that affect strategic goals.
For the business strategy, there are three key pillars:
- Business Strategy and Vision: A well-defined vision, an articulate roadmap of goals, becomes a guide post for all stakeholders. Aligning everyone’s effort towards these goals naturally accelerates operations, streamlining decision-making processes, and eliminating unnecessary detours.
- Project and Product Management: The art of managing projects and products lies at the core of the operational tempo. When projects are managed efficiently, tasks are executed timely, enhancing the overall pace and propelling the project towards its target.
- Current Business Resources: A comprehensive understanding of available resources presents a realistic picture of the present. This allows efficient planning and allocation, with the most promising initiatives and campaigns getting the most focus and value.
In technical strategy, there are four fundamental elements:
- Tech Resources: Strategic allocation of tech resources is the backbone of the technical strategy. Resources can be tools, specialists, funding, sponsorship, etc. Ensuring the right amount of resources at the right the right time and place can turn the gears of the project faster.
- Available Infrastructure: An understanding of the existing technical setup helps determine if a business strategic goal is possible. Identifying potential blocks early lets you quickly adapt, leverage resources, and change project and data product planning.
- Machine Learning Strategy: This the blueprint for applying machine learning within the project. Its defines the specific problems the ML models will solve, experimentation, iteration, and deployment. It helps focus development teams on key actionable items — reducing the amount of data, tech, and model debt.
- Development Frameworks: These are the guiding frameworks that enable smooth versioning, tracking, and testing of models, pipelines, and deployments. It also helps techincal teams know best practices to deploy and develop them. When well-established, they ensure consistency and efficiency, minimizing errors and turbocharging the project’s trajectory.
Establishing these elements prior to any project is of high importance. It prevents bottlenecks, facilitates a smoother journey, and enables a steady flight towards the set objectives.
Unclear larger strategic goals can make ML project fail, resulting in massive amounts of technical, data, and model debt. In some cases, this can even lead to the perception of the data science department as a cost center. With ML initiatives by extension seen as a costly expense to be avoided.
With proper alignment, assessment, and definition of business and technical strategy, a organization can speed up their MLOps tempo quite a bit. While keeping the data science departments and teams constantly delivering and showing value.
Knowing strategic goals is important to gauge the speed of MLOps tempo. Defining strategic goals are important to improving strategic decision making about ML projects and realigning them to be more effective and efficient.
- Have clear defined strategic goals, with defined use cases and data product alignment.
- Ask the why first behind business use cases — it will help assess the difference between vanity projects and business needs.
- Focus on how strategic goals align with technical and business strategy — its important in making sure business goals for ML are effectively implemented.
Thank you for reading!