Artificial Intelligence

2024 Enterprise Machine Learning Outlook: How MLOps Strategies Can Future Proof Your AI Investments

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Every year, AI and ML see exponential advancements and innovations, and 2024 will be no exception. The field is evolving quickly, and MLOps is emerging as the solution to bring your AI strategy to the next level and gain a true edge over competitors.

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AI is indispensable today. It is gradually evolving from a data science discipline to a means of survival. But wait. Although it is evolving gradually, its adoption is sudden. This is one of many reasons why more than 80% of AI projects fail.

According to a recent survey by Gartner, companies with the lowest AI maturity do not lack AI skills. 56% of respondents said they either have enough talent or can swiftly recruit or train AI talent. As the organization’s AI maturity increases, the reported AI talent level will also increase, with 89% having no problems acquiring the highest AI skills. Despite the Covid crisis, AI investment has continued unabated! Source- Gartner

So what’s/where’s the problem?

Most companies that implement AI follow traditional implementation methods. They usually resort to AI vendors who promise to bring complex, AI-laden systems to market in the shortest possible time. Even after that, many deployments fall to a dead end. This is because integrating AI into an already running system is a tedious task. The best AI models are useless if your existing workforce and end users cannot effectively use the anticipated structures.

In addition, AI’s impact on companies differs from a few years ago, when there was no choice but to use machine learning to build solutions. AutoML and intelligent applications are the most dynamic, but other methods are also popular AI/ML platforms as a service or cloud service.

AI is more valuable to companies that manage a large amount of information and less helpful to companies that manage little.

Misalignment between actual business needs and AI/ML objectives

Using ML for business impact hinges on the culmination of data, technique, process, and training. Machine learning cycles involve substantial data that becomes a blocker if unavailable or tardy.

A typical ML lifecycle looks like: Data collection, cleaning, wrangling, and training the model to solve a real business problem.Packaging the model in an executable format. Validating the model to ensure it meets all the performance thresholds and regulatory compliance. Deploying it for making forecasting. Monitoring the model to ensure it meets the KPIs and meets the set parameters of data drifts. Once the model is outdated or does not work as expected, you will need to kick off the training pipeline again!

The goal is to convert the insights into the desired value.

The lifecycle tunes the rhythm of the technology’s business application and the technology itself, which means you must train models (technology, first three steps) to gain insights and integrate with business use cases (business application, last two steps) to create value. It has two key elements – Training and Inference. However, the stumbling block is that these two do not exist in isolation but are cyclically linked. The trained model is sent to Inference, and then the new data is used in the next round of training to refine the model further. This cycle usually takes months or years to provide the right insights. However, this snail-paced process can be accelerated or replaced with the latest algorithms, analytics engines, cloud platforms, and managed services.

Testing and validation issues

ML models are more complex and less transparent than traditional models and pose intricate risk management and validation challenges. Although the increase in model complexity increases the likelihood of obtaining actionable insights, it also adds a new dimension of risk. When an organization builds an ML model, such as a finance firm, it follows a strict regulatory standard and knows its limitations. Thus, they avoid using the model in a way inconsistent with the original intent.

Now, a model with fundamental flaws may lead to inaccurate results. This can also happen if the implementation or use of the model is incorrect or if its limitations or assumptions are not fully understood. Therefore, data scientists should evaluate models from multiple perspectives, including process safety, conceptual consistency, continuous monitoring, and outcome analysis.

Although ML can enhance the quality of models in terms of precision, forecasting, and insight, the ever-increasing complexity of these models presents unique validation challenges for data scientists. Using traditional validation approaches will lead to the dismissal of good models and the approval of bad models. Therefore, they need to recognize these challenges and build tailored methods to validate ML models to reduce model risk.

Deployment and serving hurdles

One of the well-known realities in the data science fraternity is that it takes much longer to deploy ML models into production. In addition, the development and deployment of ML systems are somewhat fast and low-cost, but over time, they are difficult and expensive to maintain.

Established software engineering practices have demonstrated that strict abstraction boundaries through modular design and encapsulation facilitate the development of maintainable code in which isolated adjustments and enhancements can be easily carried out. It helps express the logical consistency and immutability of information. Sadly, it is tricky to impose the exact strict abstraction boundaries on ML systems. ML is needed when the intended behavior cannot be effectively expressed in software logic without external data. Although most software development practices and principles can be applied to ML, many ML-specific challenges still need to be addressed differently.

ML model requires many data sets throughout the training process to improve its accuracy. It can be hundreds of GB or even more. Therefore, moving data is not easy, and data transfer is usually costly and time-consuming.

Failed Tactics for scalable ML in production

ML models make predictions by capturing data patterns, unlike traditional development builds. The ML build runs a pipeline that extracts patterns from the data to create model artifacts, making it far too complex and experimental.

To run the project successfully in a real-time environment, you need to find the problem situation and solve the problem when it occurs. You need to continuously monitor the process to see the difference between correct and incorrect predictions (bias) and know in advance how your training data will represent real-time data.

If your production environment is not scalable and integrated with the datasets, you will likely waste money on the modeling machinery. Moreover, most organizations do not have teams that can work in a collaborative environment that challenges the operational systems and teams. In the case of a complex codebase, if you do not have a well-structured outline and standardized process, your team will get stuck at various stages of the model deployment.

It is crucial to have a cross-functional team who knows their work in advance, and sans a well-set strategic plan, your resources will scatter away.

A few examples of routine issues

CPG: A global FMCG company started an ML project to generate demand forecasts. It helped improve the forecast accuracy by 5 to 15 percentage points and simplify procurement, production planning, and transportation by avoiding depreciation and other inefficiencies, saving more than $50 million. However, during the recent recession, the data it generated was inaccurate, resulting in the company’s revenue downfall. Learn- Opportunities, problems, and ML models: How MLOps is becoming a requisite for CPG leaders?
Retail: From Uber to Amazon, headlines continue to provide us with examples of the business impact of model degradation. Post-pandemic, we have all seen companies work hard to fix corrupted and critical business models. Instacart is one of the most well-documented problems of this type. Its inventory prediction model accuracy dropped from 93% to 61%, leaving a bitter aftertaste for customers and teams. Learn- Build A Resilient Retail Organization: Pivot on Data and Analytics Modernization
Other examples from industries Logistics: Problems with tracking shipments, identifying optimal networks, etc. Supply Chain: Supply chains are hungry for solutions for product recommendation, anomaly detection, inventory forecasting solutions, and finding alternative products/sources.

Companies today want to resolve potential problems before they arise. However, the production system lacks transparent processes, tools, and requirements. The industry still lacks guidelines on what the best ML infrastructure should look like.

Filling the Gap between ML and Ops

The biggest obstacle in ML adoption is getting ML models into production. You need to improve your operational, data science, and machine learning talents to solve this. Even as the days go by, more and more ML algorithms become accessible, analytics engines become prevalent and robust, and GPUs become more powerful. So, sieving business values have essentially moved to production.

Operationalizing ML solutions in on-prem or cloud environments is challenging for the entire industry. Enterprise customers usually have a long and random software update cycle once or twice a year. Therefore, it is impractical to couple the deployment of the ML model with irregular update cycles. Besides, data scientists deal with data governance, model serving and deployment, system performance drifts, picking model features, ML model pipelines, setting the performance threshold, and explainability.

And data architects have enough databases and systems to develop, install, configure, analyze, test, maintain… the verb would keep accumulating, depending on the ratio of the company’s size to the number of data architects!

Therefore, it is not sufficient to focus on complex model development. Enterprises need to aim at laying the foundation for reliable and repeatable processes. It is impossible to industrialize machine learning by relying on a few talented practitioners of professional skills and technology; industrialization requires a diverse combination of talents and technologies to come together.

This is where MLOps rescue the team, solution, and enterprise!

MLOps promotes model development throughout the life cycle of ML models, from design to implementation to management. If companies develop only a few models for a limited product line in a project cycle of several months, they will see little value in introducing AI and ML. The continuous impact will come from machine learning models designed, productionized, automated, operated, and embedded in continuous business functions for enterprise-level use.

MLOps in 2024

As we have entered 2024, let’s look at what lies above us to be prepared, face challenges, and thrive as a business. What will be 2024 biggest challenges? And what do MLOps have to do with them?


What is MLOps

According to Forbes, some of the issues we should prepare for are uncertainty in the global economy, the rising risks and concerns regarding Artificial Intelligence, and ongoing international conflicts.

The global economy continues to confront the challenges of low growth and elevated inflation. Consumers are changing, spending is shrinking, especially due to inflation and weaker purchasing power. As a result of the overall situation, businesses are facing difficult and uncertain times.

How do you thrive in an uncertain economy?

Experts believe the macroeconomic situation in 2024 will remain difficult and uncertain: some positive factors mitigate the problems, like slowly decreasing inflation and relatively low unemployment, but others are wreaking havoc around the world, like wars, climate disasters, and extremely high interest rates.

The widespread adoption of Artificial Intelligence in 2023 is another important factor, as it poses opportunities and risks to business leaders. Risks include ethical issues, biased and inaccurate media from generative AI, loss of human jobs, drastic organizational changes, and ultimately loss of human control over AI. However, the potential gains offered by artificial intelligence are too big to ignore.

Combined, the rise of AI and uncertain macroeconomic conditions, among which international conflicts pose serious business challenges. One of the best solutions might come from MLOps to stay ahead of the curve.

What are MLOps solutions? Definition and context

MLOps is a term formed by Machine Learning (ML) and Operations (Ops), and it is a crucial part of the Artificial Intelligence industry. To give a definition,

MLOps is the process of applying DevOps principles and structure to the ML pipeline. MLOps solutions allow data scientists to work and share their results in an organized, transparent, and efficient way with data engineers who deploy the frameworks.

Applying MLOps frameworks not only gives structure to the machine learning process but also offers many important benefits for businesses looking to take their AI usage to the next level. While most companies still lack the technical skills and knowledge required, MLOps companies are increasing accessibility by offering an MLOps platform and MLOps services to improve your AI/ML solutions.

One significant difference between MLOps and DevOps is that ML models are made of both code (static) and data (dynamic). And since data is constantly changing, the models keep on adapting.

MLOps as a service to Thrive in 2024

Machine learning models can provide great value to companies, but only once the models are operationalized and deployed in real business applications. ML operationalization is putting the models and MLOps framework to work in real-life settings to leverage all the power and potential of machine learning. The importance of ML operationalization is now well understood:

IDC predicts that in 2024, 60% of companies will have operationalized their MLOps workflow.

Acting now is crucial to avoid being left behind and to be able to respond to business challenges using data analytics and automation. But is MLOps useful?

According to McKinsey, employing MLOps solutions allowed leading organizations to increase process efficiency by 30% while growing revenues by 5% to 10%. Implementing MLOps and data analytics into their processes allows companies to be stronger when facing 2024 challenges and reap benefits that will put them ahead of the competition.

Better understanding your consumers with MLOps and data analytics

Consumers are changing together with macroeconomic conditions. As needs, desires, and buying behaviors shift, MLOps frameworks collect and analyze vast amounts of customer and market data and provide actionable insights. This leads to improved products and services, targeted marketing strategies, optimized pricing, and better customer experience.

The main focus of MLOps remains improving machine learning processes and data strategies through automation, structure, and increased efficiency. As more and more executives are adopting MLOps solutions and new trends are emerging, here is what your company should strive for after ML operationalization:

  • Automation and scalability in MLOps
  • MLOps Democratization
  • Ethical concerns and regulations

How can a MLOps company benefit from automation in 2024?

One of the main goals of MLOps is to automate and scale the machine learning process, which will likely be one of the main focus areas in 2024. In the upcoming months, we expect to see important improvements in automation and scalability, aiming to increase productivity and efficiency.

An important aspect of automation is MLOps orchestration, coordinating workflow fundamentals to achieve automation, standardization, and reproducibility.

Together, orchestration, automation, and scalability will optimize resources, improve productivity, and democratize MLOps, making them more accessible.

Democratization of MLOps solutions will be a main trend in 2024. MLOps consulting is already helping companies integrate MLOps frameworks into their business processes, and this trend is bound to continue. The rise of MLOps platforms like databricks MLOps, unified and relatively simple-to-use solutions providing end-to-end management for the entire ML lifecycle, will make MLOps even more accessible to companies regardless of their skill level or background. In 2024, we will see more and more companies across various industries adopt MLOps solutions to get ahead of the competition.

What are the main MLOps challenges and ethical concerns?

MLOps comes with its challenges. Implementation difficulties, data collection and preparation problems, unrealistic expectations, and ethical concerns are all MLOps challenges that must be recognized and addressed before jumping on the bandwagon.

In particular, ethical issues and lack of regulations are tricky problems that experts and business leaders must face. As machine learning becomes more used and impactful in our society, ethical considerations must be made to ensure this technology’s ethical development and deployment. This includes:

  • Ensuring fairness within models
  • Safeguard data safety and privacy
  • Addressing bias
  • Ensuring transparency and accountability at all stages of the ML lifecycle

Moreover, in 2024, we are likely to see the implementation of regulations from governments and stakeholders and AI audits and certifications becoming more frequent. Companies must be informed and responsive to new AI laws and regulations, ready to act quickly to ensure compliance.

The challenges are many and very serious, but the benefits offered by MLOps solutions are simply too important to ignore. Investing in the right strategy with the help of an MLOps consulting company will be your ticket to winning in 2024.

Abhishek is a vertical marketing lead and data science enthusiast with 9 years of experience. His insightful work and practical experience make him a trusted authority in the field. His impactful contributions extend beyond groundbreaking projects to influential thought leadership, as reflected in his authored publications.