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Guide to Enterprise Machine Learning and its Use Cases

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Introduction to Enterprise Machine Learning

Organizations are increasingly incorporating Machine Learning technologies into their corporate models, as technology has allowed enterprises to execute activities on a large scale while also creating new business opportunities. The growing use of Enterprise Machine Learning operations is mirrored in the ever-increasing number of use cases. The following are few examples of use cases

Recommendation Engines

Companies nowadays use recommendation engines to get the right offer to their consumers at the right time. It has become the standard technology for online shopping pages, with tools that consider the browsing history of aligning the preferences represented by other items’ history.

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Fraud Detection

As more financial transactions move electronically, the risk of fraud rises, necessitating fraud detection software.

Customer Analysis

Businesses now receive a vast volume of data from their clients. ML algorithms use in data lakes, where businesses store raw data and create consumer insights. ML uses to create customized content campaigns that improve consumer satisfaction.

Financial Trading

ML algorithms will look at records, identify trends in market results, and forecast how stocks will do in the future.

Virtual Assistants

Digital assistants such as Siri and Alexa are familiar to most people nowadays. Deep learning (DL) is critical in developing natural language processing, which allows the bot to communicate with the user and understand their interests.

Self-driving Cars

Autonomous vehicles learn to perceive events observed by cameras and other sensors and decide what step to take to drive a car down the road using neural networks. In this way, data-driven ML algorithms will approach human-like vision and decision-making.


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Importance of Artificial Intelligence and ML in IT industry?

According to Forbes, 43% of businesses believe AI and ML programs are more important than we thought. In 2020, businesses will focus on projects that result in sales growth and cost savings. Enterprises will continue to address previous problems in the ML pipeline in 2021, emphasizing scalability, elasticity, and ML functionality. The main emphasis in 2018/19 was on reproducible data science, and businesses were worried about the rollout.

When and where do things get tricky?

The below are the points where do things get difficult;

Wrong Use-Case

The failure of specific ML systems can be traced back to an incorrect use case. Rather than having the challenge to decide the solution, most companies lead by technologies and search for opportunities to apply ML in their use cases. When ML is used in a use case, it often fails to produce output.

Incorrect Data

In ML, data is king. When a model trains on incomplete, disorganized, and biased data, it can produce unsatisfactory results. The incorrect data will devastate ML models more quickly than anything else.

Bias

When the data used to train the algorithm does not accurately represent reality. The data collection is incorrect, incomplete, or lacking in diversity.

Technical Complexity

The idea of feeding training data to a model and letting it learn from it may seem easy, but there is a lot of technical difficulty behind the scenes. Algorithms based on complex mathematical principles and the code these algorithms operate on can be challenging to grasp.

Lack of Generalizability

ML implementations know what they've learned; if they've only practiced on one item, they won't be able to apply it to something else. The algorithm should train again for any new use case.


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What are the challenges of Enterprise Machine Learning?

The below are the most critical issues for business data executives to consider in 2021.

Technical Debt

The most significant problem with AI and ML at scale is that computer scientists aren't doing any data science. When you look at how data scientists spend their days, you'll see that they spend most of their time doing things like configuring CPUs, GPUs, and ML orchestration software like Kubernetes containers.

Resource Management

A data scientist's roles have expanded to include resource management. Allocating computational power ML can be time-consuming and distracting from data science activities. Besides, hybrid cloud computing is becoming more common as a means of scaling AI.

Model Management

DL algorithms are also a challenge for data science teams. Tasks include data versioning, model maintenance, implementation of software, using open-source resources and frameworks. A data scientist should concentrate on designing ML models and measuring model output to speed up ML models.

Disconnected Workflows

There are two distinct significant workflows in the industry today. The DevOps workflow focuses on resource control, infrastructure, and output visualization. Businesses face technical debt, which affects manufacturing time and expense. Data science is the second disconnected workflow, which focuses on data collection, data processing, and model analysis.

Global Pandemic Transition and Recovery

Enterprises struggle with the worldwide pandemic's ramifications, just as they did in 2020. Data leaders must build technology that can withstand a hybrid work climate.


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What are the best solutions for Enterprise ML?

The enterprise ML faces various problems, but there are opportunities to improve productivity. Here are some tactics that decision-makers can use to boost ML's scalability, elasticity, and operationalization in the coming year.

Scale MLOps

ML operations (MLOps) minimize friction and bottlenecks between ML production teams and engineering teams. MLOps incorporates DevOps techniques with ML and AI development's specific requirements. MLOps enables teams to simplify DevOps processes and productionize ML models in the context of organizations. Many businesses neglect the technical difficulty and commitment involved in delivering ML models to market. Scaling MLOps activities would be critical in scaling and speeding up ML outputs. MLOps and automation will significantly boost the ML workflow and reduce manual DevOps assignments.

Container-based Orchestration

A container-based development platform can increase cluster workload coordination, speed, and size. Containers often help with reproducibility by exchanging the entire execution environment. Enterprises should even think about using a managed service that enables on-demand self-service provision of instances.

Adopt a Hybrid Cloud infrastructure

Combining public clouds, proprietary clouds, and on-premise tools provides mobility and stability. Businesses may not choose to use a virtual cloud system due to price, scalability, or legacy architecture. Containers are essential for a compact and scalable ML infrastructure. ML workloads can be assigned to various computing resources using containers.


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Critical trends for Enterprises to focus on as they head into future

The AI/ML world has shifted dramatically in the last year due to COVID-19's economic effects. Companies turn to their AI investments for short-term cost-cutting and long-term technological growth to drive sales and performance in these turbulent times.

  • Organizations Are Increasing AI/ML Budgets, Staff, and Use Cases

Before the pandemic, companies raised their AI/ML budgets, and the economic instability caused by COVID-19 heightened the urgency. The number of organizations with more than five AI/ML use cases has risen by 74% year over year. The top use cases that companies are focusing on are consumer service and process automation. These use cases will have top-and-bottom-line gains during periods of economic turmoil.

  • Challenges Span the ML Lifecycle, Especially with Governance

Organizations are facing problems in the ML lifecycle, with AI/ML governance being the most important. Management, compliance, and auditability issues are top concerns for 56 percent of all organizations, and 67 percent of all organizations say they must comply with several regulations for their AI/ML.

Organizations continue to deal with the requisite deployment and operational problems, in addition to governance issues. According to the report, fundamental integration concerns citing a challenge by 49% of organizations and cross-functional coordination remains a significant roadblock to AI/ML maturity.

  • Despite Increased Budgets and Hiring, Organizations Are Spending More Time and Resources—Not Less-on Model Deployment

Despite increased expenditures and headcount, companies are devoting more time and energy to model rollout. The time it takes to deploy a trained model to manufacturing has risen year over year. Organizations with more templates expend more of their data scientists' time on deployment.

In the end, companies have expanded their AI/ML resources without addressing fundamental organizational performance issues. As a consequence, businesses are devoting more time and resources to concept implementation, exacerbating the problem.

  • Organizations Report Improved Outcomes with Third-party MLOps Solutions

To handle ML processes, many companies get better results (MLOps) by using third-party solutions. Organizations that either incorporate commercial point technologies into their operations or use a third-party network spend an average of 19-21 percent less on maintenance costs than organizations that develop and operate their systems from the ground up. Their data scientists spend a lower percentage of their time on model implementation on average, and it takes them less time to deploy a qualified model.

The shortage of professional in-house talent was the top challenge for companies implementing AI/ML programs before the pandemic. Organizations are now more concerned with moving ML models into production faster and ensuring their long-term success.

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Conclusion

In 2021, AI will almost certainly deliver on its previous commitments. Enterprises can reduce the expense, time, and technical difficulty of building ML if decision-makers efficiently respond to emerging technology and infrastructures. Many enterprises use MlOps to set up their ML for scale by using container-based applications and managed services.

Furthermore, improved resource optimization and the use of a hybrid cloud environment will help businesses save money and boost ML. ML and DL accelerators are two other ways that companies are leading the AI competition. Many of these techniques are interconnected, and they can necessitate a complete overhaul of legacy processes. But, thankfully, companies are finding it more comfortable to convert their ML infrastructures. Even the most developed architectures have been able to implement a new AI infrastructure.

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