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Is It Easy to Succeed in the AI world?

Is It Easy to Succeed in the AI world?

AI is getting even more traction in recent times. All Major Companies, Organizations, and industries are adopting Artificial Intelligence to scale up and enhance their corporate strategies. Innovations in Deep Learning are the aiding drive for business success from e-commerce to national security. Information and data are the most important elements to a fruitful AI model. Unlike traditional coding prototypes, the consequence of an AI algorithm is very reliant on the data used to train it as it extrapolates conclusions built on what it has been trained on.

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Artificial Intelligence (AI) and Machine Learning (ML) are the foundations for every data-driven organizations today. These technologies have been explored in countless ways. While we are envisioning a fully AI-powered world of self-driven cars and self-ordering refrigerators, we are yet to entirely leverage the true potential of AI techniques.

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AI defined workflows are created to tap into organizational data and put AI to work across multiple departments – from finance to customer care, to supply chain. Unlike Robots taking over humans, here AI is building a smarter decision-making taskforce making our lives even easier. Many businesses that are exploring AI techniques extensively are increasingly moving these workloads to the cloud for obvious reasons. Increasing relevance and success rates of such projects, however, depends on a lot of factors.

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So, what does it take to succeed with AI / ML at scale and operationalize your ML models? Here are a few key considerations:

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  2. Defining Industry Goals:
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Before starting an AI proposal, it is wise to define the goals, identify them and decide on what metrics are you aiming to achieve. Predict what a particular customer is likely to buy. Decisions provide the business context for your AI and ML investments.

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Familiarity with working with thousands of business decisions around the world demonstrates that the most efficient way to concentrate on your decision-making is to work directly with the business owners to build a decision model. Decision models, like the one shown below, let you break down even overly complex decisions into easier to manage and describe components.

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  2. Ensure Stakeholder Orientation
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Constant inputs from your Clients and stakeholders are necessary to define the goals properly and setting up the pace wherever required. Set clear objectives on how that certain business problem can be solved by applying technology.

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AI projects can also fail due to a lack of unanimity between various stakeholders. Once you have known the problem, map out the various stakeholders who need to be involved. Classification, detection, segmentation, or recommendation can be the steppingstones of a proper machine learning model. Take frequent feedbacks from the client prior to and throughout the project execution. All successful AI projects are accomplished in proper cooperation with the client.

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  2. Make Sure you have Right Team
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Data scientists, data engineers are all around. But to make sure you have the right balance of the team hierarchy is essential to complete a project on time. From data prep and model building to training and inference; it is a team spirit requiring multiple different roles, including data engineers, ML architects, and operations. Organizing and scaling the team effectively is another challenge.

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Hiring the right people at every stage with proper skill sets can take the project to deliverables. Proper training and execution at every stage is important to scale up the operation.

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  2. Provide Tools and Technologies
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The right balance of different technologies is important to solve all the complex business problems. Upgrading to the latest tools which is important for the project is essential. Not to forget the proper training programs to utilize the efficiency of the new tools in the market.

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The Machine Learning space is continuously evolving, and your technology stack needs to sustain multiple different frameworks including TensorFlow, Keras, PyTorch, and much more. So relying on one particular tool is never a good idea, when you have the magnitude of tools to make your project deliverable smooth.

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Finally, a company may collect more data than its existing human or computer firepower can adequately analyze and apply. And having the proper AI system to take decisions on the performance is what makes this technology a next-gen technology.