Embedding AI across an enterprise to tap its full business value requires shifting from bespoke builds to an industrialized AI factory.&nbsp; For AI to make a sizable contribution to a company’s bottom line, organizations must scale the technology across the organization, infusing it in core business processes, workflows, and customer journeys to optimize decision making and operations daily.Achieving such scale requires a highly efficient AI production line, where every AI team quickly churns out dozens of race-ready, risk-compliant, reliable models.

AI At Scale

As AI has matured, so too have roles, processes, and technologies designed to drive its success at scale. Specialized roles such as data engineer and machine learning engineer have emerged to offer skills vital for achieving scale.A rapidly expanding stack of technologies and services has enabled teams to move from a manual and development-focused approach to one that’s more automated, modular, and fit to address the entire AI life cycle, from managing incoming data to monitoring and fixing live applications.

The Rising Bar for AI

A best-in-class framework for ways of working, often called MLOps (machine learning operations), now can enable organizations to take advantage of these advances and create a standard, company-wide AI “factory” capable of achieving scale. Since MLOps is relatively new and still evolving, definitions of what it encompasses within the AI life cycle can vary.One needs to examine the potential improvements from four essential angles: productivity and speed, reliability, risk, and talent acquisition and retention.&nbsp;

Business impact of MLOps

Moving AI solutions from idea to implementation takes nine months to more than a year, making it difficult to keep up with changing market dynamics. Even after years of investment, leaders often tell us that their organizations aren’t moving any faster.Companies applying MLOps can go from idea to a live solution in just two to 12 weeks without increasing headcount or technical debt, reducing time to value and freeing teams to scale AI faster.

Increasing productivity and speed to embed organization-wide AI

Achieving productivity and speed requires streamlining and automating processes, as well as building reusable assets and components, managed closely for quality and risk, so that engineers spend more time putting components together instead of building everything from scratch.

Automation Of Processes

Another critical element for speed and productivity improvements is developing modular components, such as data pipelines and generic models that are easily customizable for use across different AI projects. By building a central AI platform and modular premade components on top, the company was able to industrialize a base AI solution that could rapidly be tailored to account for different drug combinations in each market.

Modular Components

Organizations often invest significant time and money in developing AI solutions only to find that the business stops using nearly 80 percent of them because they no longer provide value—and no one can figure out why that’s the case or how to fix them. In contrast, we find that companies using comprehensive MLOps practices shelve 30 percent fewer models and increase the value they realize from their AI work by as much as 60 percent

Enhancing reliability to ensure 24/7 operation of AI solutions

While a robust risk-management program driven by legal, risk, and AI professionals must underlie any company’s AI program, many of the measures for managing these risks rely on the practices used by AI teams. MLOps bakes comprehensive risk-mitigation measures into the AI application life cycle.Reusable components, replete with documentation on their structure, use, and risk considerations, also limit the probability of errors and allow for component updates to cascade through AI applications that leverage them.

Reducing risk to ensure regulatory compliance and trust at scale

The availability of technical talent is one of the biggest bottlenecks for scaling AI and analytics in general. When deployed well, MLOps can serve as part of the proposition to attract and retain critical talent. Most technical talent gets excited about doing cutting-edge work with the best tools that allow them to focus on challenging analytics problems and seeing the impact of their work in production.

Better talent retention and acquisition for implementing AI at scale

Implementing MLOps requires significant cultural shifts to loosen firmly rooted, siloed ways of working and focus teams on creating a factory-like environment around AI development and management. Building an MLOps capability will materially shift how data scientists, engineers, and technologists work as they move from bespoke builds to a more industrialized production approach. As a result, CEOs play a critical role in three key areas: setting aspirations, facilitating shared goals and accountability, and investing in talent.

The Role Of Leaders In MLOps

As in any technology transformation, CEOs can break down organizational barriers by vocalizing company values and their expectations that teams will rapidly develop, deliver, and maintain systems that generate sustainable value. CEOs should be clear that AI systems operate at the level of other business-critical systems that must run 24/7 and drive business value daily. While vision setting is key, it pays to get specific on what’s expected.

Setting a clear aspiration for impact and productivity

Among the key performance metrics CEOs can champion are the following:<ul><li>the percentage of models built that are deployed and delivering value, with an expectation of 90 percent of models in production having real business impact</li><li>the total impact and ROI from AI as a measurement of true scalability</li><li>near-real-time identification of model degradation and risks, including shifts in underlying data (particularly important in regulated industries).</li></ul>

Key Performance Metrics

One of the fundamental litmus tests for impact is the degree to which goals are shared across business leaders and the respective AI, data, and IT teams. Ideally, the majority of goals for AI and data teams should be in service of business leaders’ goals. Conversely, business leaders should be able to articulate what value they expect from AI and how it will come to fruition.

Ensuring shared goals and joint accountability

Newer roles needed on AI teams have emerged, like that of the machine learning engineer who is skilled in turning AI models into enterprise-grade production systems that run reliably. To build out its ML engineering team, a North American retailer combined existing expertise of internal IT developers who understood and could effectively navigate the organization’s systems with new external hires who brought broad experience in MLOps from different industries.

Investing in upskilling existing AI talent and new roles

Stashed ideas

Scaling AI like a tech native: The CEO’s role

... so they can leverage the considerable benefits to be gained through greater knowledge of their customers, explore new markets, and counteract new, AI-driven companies that might seek their market share.
To extract the benefits from AI while mitigating the risks, companies must ensure that they are sufficiently agile so they can adopt best practices to create responsible transformation with AI.

All companies will need to become “AI companies”

<ol><li>The whole organization must be engaged with the AI strategy, which involves a total organizational review and potential changes.</li><li>All employees need education and training to understand how AI is used in the company so that diverse teams can be created to manage AI design, development, and use. Additionally, employees should understand how the use of AI will impact their work and potentially help them do their jobs.</li><li>Responsibility for AI products does not end at the point of sale: Companies must engage in proactive responsible AI audits for all ideas and products before development and deployment.</li></ol>

The three principles of responsible AI

Organizations must recognize the drawbacks that some algorithms bring into the screening and hiring process as a result of the way they are trained, which can have a direct impact on outcomes such as diversity and inclusion.&nbsp;<ul><li>When it comes to reskilling and retaining employees, AI can be helpful to companies when deployed for screening and training employees for new positions.&nbsp;</li><li>Many employees have skills that can be built upon to cross into a new position, but companies often don’t realize the full extent of their employees’ capabilities.</li><li>For companies seeking to attract and retain employees with AI skills, it helps to develop responsible AI policies, because many of the most talented AI designers and developers value their company’s positions on ethics and transparency in their work.&nbsp;</li></ul>

Responsible AI and the employee experience

<ul><li>It’s more important than ever that companies develop a strategy around responsible AI and communicate it well to internal and external stakeholders in order to maintain accountability.&nbsp;</li><li>Companies should also keep in mind that a one-size-fits-all approach does not always work with emerging technology; instead, they need to match the right AI solution to the right customers and create business offerings that align with customer needs.</li><li>Startups with responsible AI strategies will be more valuable. The purchase of a responsible AI startup may depend on the startup’s approval of the acquirer’s apprcoach. Investors may refuse to buy stock in companies that don’t have responsible AI. Indeed, there may be an increase in activist investors in this space.</li></ul>

Responsible AI for customers and stakeholders

To gain traction throughout an organization, support for responsible AI needs to come from its leadership. Unfortunately, many board members and executive teams lack an understanding of AI. The World Economic Forum created a toolkit for boards to learn about the different oversight responsibilities in companies involved with AI. They can use it to understand how responsible AI can be adopted across different areas of the business — including branding, competitive and customer strategies, cybersecurity, governance, operations, human resources, and corporate social responsibility — and prevent ethical issues from taking hold.

Responsible AI needs support at the top

Companies adopting the best practices of responsible AI extract benefits while mitigating AI’s risks.

Building an Organizational Approach to Responsible AI

In 2019, near 87% of data science projects did not get into production. However, due to COVID -19, most companies have scaled up their AI adoption and increased their AI investment. In 2020, almost 50 % of enterprises employed an ML model. But to completely harness the power of AI, multiple models need to be created and deployed.

AI adoption

AI model development involves multiple stages that interconnect to each other. <ol><li>Identify the business problem. Instead of asking how to improve your artificial intelligence, ask how to improve your business.</li><li>Identify and collect data. Identifying the correct data is vital to ensure model accuracy and relevance.</li><li>Preparing the data.</li><li>Model building and training.</li><li>Model testing. The model is trained and tuned using the training and validation data sets.</li><li>Model deployment. Once the model is tested with different datasets, you will have to validate model performance using the parameters from Step 1.</li></ol>

The AI Model development lifecycle

Define the business problem you are trying to solve.<ul><li dir = "auto">What results are you expecting from the process?</li><li dir = "auto">What processes are used to solve this problem?</li><li dir = "auto">Do you see AI improving the current process?</li><li dir = "auto">What are the key performance indicators (KPIs) that will help track progress?</li><li dir = "auto">What resources will be needed?</li><li dir = "auto">Consider how to break down the problem into iterative sprints.</li></ul> Once you have answers, then identify how you can solve the problem using AI.

Identify the business problem

Ask questions, such as.<ul><li dir = "auto">What data is needed to solve the business problem?</li><li dir = "auto">What quantity of data is required?</li><li dir = "auto">Do you have enough data to build a model?</li><li dir = "auto">Do you need more data to extend the existing data?</li><li dir = "auto">How is the data obtained, and where is it stored?</li><li dir = "auto">Can you use pre-trained data?</li></ul> Consider if your model will operate in real-time to determine if you need to create data pipelines to feed the model. Consider what form of data is required:<ul><li>Structured data in the form of rows and columns.</li><li>Unstructured data, such as images.</li><li>Static data, such as previous sales data.</li><li>Streaming data.</li></ul>

Identifying and collecting data

This step is the most time-consuming, with ML engineers spending around 80% of the AI model development time in this stage. A significant amount of time is spent cleaning the data and transforming it into the required format. Things to consider include:<ul><li>Transforming the data into the required format.</li><li>Cleaning the data set of inaccurate and irrelevant data.</li><li>Enhance and augment the data set if the quality is low.</li></ul>

Preparing the data

At this step, all the requirements have been collected for the solution modelling to proceed. ML engineers will define the features of the model, taking the following into account:<ul><li dir = "auto">Use the same features for training and testing the model to avoid inaccurate results.</li><li dir = "auto">Consider working with Subject Matter Experts to direct you on what features would be necessary for the model.</li><li dir = "auto">Be wary of using multiple features that might be irrelevant to the model.</li></ul> Once the features are defined, choose the most suitable algorithm.

Model building and training

While the model is trained and tuned using the training and validation data set, the model will behave differently when used in the real world, which is fine. The main objective is to minimise the change in model behaviour when it is deployed. Three data sets are used when experiments are carried out: training, validation, and testing.<ul><li>If the model performs poorly on the training data, select a better algorithm, increase data quality, or feed more data into the model.</li><li>If the model does not perform well on testing data, the model may not extend the algorithm, and more data needs to be added.</li></ul>

Model testing

Analyse if the KPIs and the business objective of the model are achieved. If the parameters are not met, consider changing the model or improving the quality and quantity of the data. Before deployment:<ul><li dir = "auto">Ensure to measure and monitor the model performance continuously.</li><li dir = "auto">Define a baseline to measure future iterations of the model.</li><li dir = "auto">Keep iterating the model to improve model performance.</li></ul> When all the defined parameters are met, deploy the model into the intended infrastructure.

Model deployment

In 2019, Venturebeat reported that almost 87% of data science projects do not get into production. Redapt, an end-to-end technology solution provider, also re…

A Step By Step Guide To AI Model Development

The rich have a developing mentality, not just a fixed one. People with a growth mindset are more resilient in the face of adversity, have more drive, are in better situations, have better connections, and communicate better, which leads to better outcomes.

Career

ImSanjamS

Back in 1930, the economist John Maynard Keynes predicted that with technological change and improvements in productivity, we’d only be working 15 hours a week by now. But while working hours have declined by 26 percent, most of us still average 42.5 hours a week, according to Eurostat figures.One of the things Keynes underestimated is the human desire to compete with our peers – a drive that makes most of us work more than we need to.

Overwork as a choice

The pandemic has accelerated the move towards automation and artificial intelligence, especially for jobs with high physical proximity – from Amazon developing delivery drones to self-driving cabs. By 2050, 40-50 percent of current jobs will be lost to automation.There are exceptions. Jobs that involve complex social interactions are beyond current robot skills: so teaching, social care, nursing and counselling are all likely to survive the AI revolution. As are jobs that rely on creativity. The same also goes for cleaning jobs, due to the multitude of different objects cleaners encounter and the variety of ways those objects need to be dealt with.

Future of work: predictions

Interestingly, areas of the workplace traditionally dominated by women won’t be so easily adopted by AI.future, just differently. Women are still shouldering three-quarters of all unpaid care work and doing 40 percent more household chores according to the ONS. Robots are unlikely to assist in the ‘work’ of childrearing, preparing lunchboxes and doing the laundry.
Those whose work falls outside the caring/cleaning/creative realms will still work in the

Areas of the workplace traditionally dominated by women

A job is an important part of how we find happiness in our lives. But what if the machines start doing everything for us? Are our working days are numbered?

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AI At Scale

Scaling AI like a tech native: The CEO’s role

All companies will need to become “AI companies”

Building an Organizational Approach to Responsible AI