How to unlock business value from MLOps?

According to Gartner, 85 percent of all Artificial Intelligence (AI) projects tend to fail and the trend is expected to run well through 2022. What are the key reasons for this high failure rate in AI projects? There are three key ones:

  1. Model deployment is not an easy tasks; it requires diverse expertise from software engineering, to machine learnings engineering along with data scientists
  2. Model performances or effectiveness deteriorate on real-world applications
  3. Models designed without collaboration between domain experts and engineers are unlikely to deliver the desired results

However, organizations can flip the equation by adopting Machine Learning Operations or MLOps, which allows organizations to redefine a process of putting model into productions, helps break from the shackles of siloes and allows different teams to collaborate in real-time with a goal to serve model for business and help achieve ROI. MLOps also ensures that the ML models created through the process are scalable and can be redeployed to solving other problems.

What is MLOps?

Before DevOps, developers were spending hours and hours working on code that may never go into production. As a result, DevOps got its footing in the tech industry over a decade ago as a means to bring the development teams and the IT teams together and make these somewhat different communities collaborate in a frictionless manner. Before DevOps, developers were spending hours and hours working on code that may never go into production.However, by being able to collaborate with the IT teams, nearly all DevOps teams today are convinced about their code even before it goes into production.

As AI and ML started to grow, they faced similar challenges that developers faced in the pre-DevOps era—getting stuck trying to take an AI project from ideation to production stage. And so, came Machine Learning Operations (MLOps). Modelled on the principles of DevOps, MLOps brings together people, processes, and practices by allowing collaboration between data, development and production teams.

Underpinning the idea of MLOps are technologies that automate the deployment, monitoring, and management of machine learning models. MLOps, in fact, goes a step ahead and ensures that the code that goes into production is scalable and provides a measurable business while having a strong governance framework at the same time.
What are the key components of MLOps?
MLOps acts as a guiding principles for data scientists, engineers and operations professionals to collaborate and help manage the production ML lifecycle. MLOps leverages automation to improve the quality of production ML with a constant eye on business goals.

Broadly, there are three key phases of any MLOps process—Designing the ML-powered application, ML Experimentation and Development, and finally, ML Operations.The design phase for the ML-powered application begins with understanding the business and the available data. Next, potential users need to be identified in this stage, and then an ML solution is designed to solve their problems while also looking at the possibilities of scaling the application to other areas. Typically, this phase looks at either enhancing user productivity or increasing the interactivity of the ML application.

The design phase also clearly defines the ML use-cases and prioritizes them. The available data is inspected and used to train the ML model. The requirements gathered from this exercise are then used to design the architecture of the ML application, establish the serving strategy, and create a test suite for the future ML model.

In the next phase of MLOps, it is vital to verify the applicability of ML for the identified problems through the deployment of an ML Model Proof-of-Concept. This phase is run iteratively to identify or polish the suitable ML algorithm for the given situation, data engineering, and model engineering. The idea is to build a stable quality ML model thatcan be runin production.

The last and final phase of operations aims to deliver the previously developed ML model in production by using established DevOps practices such as testing, versioning, continuous delivery, and monitoring.

The three phases are highly interconnected while also influencing each other. Each of these phases contributes key elements that work to close the ML lifecycle loop within an organization.
What are the benefits of MLOps?
MLOps can be highly beneficial for CXOs, data scientists, and data engineers alike. Let’s take for example on how MLOps can benefit CXOs. C-suite leaders require fast, accurate, and unbiased predictions. They are also looking for an AI solution that can provide them with a clear return on investment. That has been challenging for years but MLOps changes that forever by making it simple to highlight ROI on AI investments. By putting MLOps in place, CXOs can therefore utilize their energies into scaling AI capabilities throughout the organization while focusing on tracking KPIs that matter to each team and department.

Data scientists can similarly gain immense benefits from MLOps as it automates several parts of their daily lives while also allowing them to effectively collaborate with their operations counterparts. MLOps also eases out data scientists and ML engineers’ efforts by offloading much of the burden of day to day model management. This allows them to focus on the larger problems such as identifying new use cases, managing feature discovery, and developing more in-depth business expertise. A large part of a data scientist’s time goes into maintaining models or reviewing their performance manually. All of that gets automated with MLOps and frees up valuable resources.

For DevOps and data engineers, MLOps offers a way to manage their actual machine learning models in a single pane—right from testing and validation to updates and performance metrics. This enables the organization to scale ML deployment over a period of time to meet latency, throughput, and reliability SLAs, thereby generating more value from it.

How to implement MLOps?

Even before one thinks of implementing MLOps, it is important to start with a clear business goal or objective. These objectives need to be fleshed out with target performance measures, technical requirements, budget for the project, and KPIs that drive the process of monitoring the deployed models.

Once that’s in place, MLOps can be implemented in three different ways, depending on the organization’s maturity level in terms of the understanding of MLOps. The three types of implementation include manual process, ML pipeline automation, and CI/CD pipeline automation. These are also commonly referred to as the three levels of MLOps—MLOps level 0 (manual process), MLOps level 1 (pipeline automation), and MLOps level 2 (CI/CD pipeline automation).

Typically while starting their journey with ML, organizations begin with the manual ML workflow. In this type of deployment, every step of the journey is manual, including data analysis, data preparation, model training and even validation. In this type of implementation, data scientists work on the ML model and hand it over after training it to the engineering team to deploy on their API infrastructure.

This type of deployment is suitable when the assumption is that your data science team manages a few models that don’t change frequently. And since there are no frequent changes, there is no need for Continuous Integration and Continuous Deployment.

The second type of implementation is ML pipeline automation. This type of implementation goes a step ahead of the manual process and automates the ML pipeline to perform continuous training of the ML model. This type of implementation is suitable for solutions that operate in a constantly changing environment and need to proactively address shifts in indicators such as customer sentiment, market prices etc. While in MLOps level 0, the trained model is deployed as a prediction service to production, in level 1, an entire training pipeline is deployed that automatically and iteratively runs to serve the trained model as the prediction service.

However, this model is still not suitable for new ML idea, rather only new models based on new data. Moreover, it is not ideal for environments where you need to manage multiple ML pipelines in production.

To overcome the limitations of MLOps level 1, MLOps level 2 takes things up a notch and fits well with tech-driven companies that continuously retrain their ML models on a daily basis and redeploy the code on thousands of servers simultaneously.

The automated CI/CD pipeline, data scientists can spend more time on high-value items such as feature engineering, model architecture and hyperparameters. The output of MLOps level 2 is a deployed model prediction service.

The challenges to implementing MLOps

In 2013, IBM partnered with The University of Texas MD Anderson Cancer Center to build Watson for Oncology with an aim to eradicate cancer. Five years down the line, the project was shelved as it started giving erroneous treatment advice. Later it is found that the ML model was trained not on real patient data but rather on a small number of hypothetic patients.

Mistakes like these are pretty common in the ML domain. A typical ML lifecycle involves the identification of a business problem, establishing the success criteria, and then delivering an ML model to production. The delivery part happens in multiple steps, and each of these steps can either be performed manually or through an automatic pipeline.

While it may sound prudent to focus on solving the business problem, it is easy to lose focus on the complexities in managing the entire ML process. ML is a highly iterative process, and data scientists end up spending a lot of time in these iterations. Forcing models into production after the first or the second iteration can quickly turn into a failed deployment.

Data scientists need to not only deal with short response times but also support a large number of users. Moreover, working with thousands of code lines bring along their own set of difficulties to manage. Therefore, while data scientists were previously only required to produce an ML model, today, the first step is bringing ML models to production.

Lack of synergies between data science and operations teams sometimes also becomes a big challenge for organizations. Often the data science teams don’t have enough process understanding, and operations teams end up overestimating their understanding of ML processes, leading to disastrous outcomes.

MLOps requires dedicated people and resources to succeed. CXOs need to understand that MLOps is an iterative process and requires significant advance planning. The process cannot be taken casually, and companies need to be prepared for various contingencies.

Lately, there are plathero of tools, frameworks and platforms available in the market to bring together highly disparate space of “model production management” into the center of AI ecosystem. These tools and frameworks are primarily focused technical engineers to centralize the orchestration of model production using principles of MLops.

At the same time, while AI is going no-code and enabling business users or citizen data scientist to handle data science projects. It is equally important to domain users, analytic experts to enable their ML models build into production. Hence, there are no code MLOps platforms such as HyperSense AI studio. It is designed exclusively for domain and analytic experts to take chart of machine learning models and deploy and manage complete life-cycle of ML models.
Tips to implement MLOps
New age MLOps platforms have significantly reduced the management challenges faced by data scientists, allowing them to be more confident about their code going into production. HyperSense AI Studio is a great example of new-age MLOps. The platform enables any enterprise user to build and operationalize AI successfully using automated machine learning. It increases the efficiency of data scientists allowing them to focus on higher-value tasks. It automates every step of the data science lifecycle including, feature engineering, algorithm selection, and hyper-parameter tuning.

By leveraging HyperSense AI Studio, data scientists and experts can easily and quickly build ML models with larger scale, productivity, and efficiency while sustaining the model quality. By automating a large part of the ML processes, the platform accelerates the time to get production-ready models with greater ease and efficiency. It also reduces human errors mainly because of manual measures in ML models. Further, HyperSense also makes data science accessible to all, enabling both trained and non-trained resources to rapidly build accurate and robust models, thus fostering a decentralized process.

The quality of the machine learning model is not only based on code but also on the features used for running the model. Around 80% of data scientists’ time goes into creating, training, and testing data.

HyperSense AI Studio comes built-in with a feature store that allows features to be registered, discovered, and used as a part of an ML pipeline. In addition, it enables reusing components instead of rebuilding again from scratch for different models driving AI at scale.

HyperSense AI Studio increases the efficiency of data scientists by allowing them to focus on higher-value tasks. The platform also automates every step of the data science lifecycle including, feature engineering, algorithm selection, and hyper-parameter tuning.
Key Takeaways
Data scientists are a highly coveted lot. Yet, 80 percent of their time ends up being wasted doing repetitive tasks that can easily be automated. At the same time, the lack of synergies between data science and operations teams has led to a majority of AI projects to fail. This can be easily avoided.

MLOps allows all the stakeholders in the ML process to work collaboratively and ensure the models they work on gets into production. New tools such as HyperSense AI that brings in automation and low code capabilities also bridge the data science skills gap to a large extent by freeing up nearly 70-80% of the time spent by data scientists on model testing and validation.

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