What are the top challenges around introducing machine learning at your company?
Machine Learning
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Machine learning has the potential to deliver significant benefits for companies, from improving customer service to optimizing tedious operations. However, implementing machine learning in a company is not without its challenges. If business leaders can begin to understand these hurdles, they may be able to anticipate and address them before they arise. Here are some common challenges that companies often face when beginning to implement machine learning.?
1. Getting the right data: One of the most common obstacles for companies is obtaining the right data to train machine learning algorithms. Data is often siloed, incomplete or inaccurate, which can limit the effectiveness of machine learning models. Proper data collection, processing and curation are essential in order to make the most of machine learning.
2. Understanding the algorithms: Machine learning is complex, and oftentimes data scientists are needed to configure and fine-tune the algorithms to get the best results. Many companies lack the internal expertise to fully understand the algorithms and how they work, which can mean that they miss out on important insights. Hiring and growing a team that can understand the tools you’re hoping to use can be invaluable to your company’s growth and success.?
3. Determining where to apply machine learning: Machine learning can be used in a variety of areas in your company, but each use case requires different approaches, data and resources. Understanding each use case is essential for being able to use the technology effectively.?
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4. Managing expectations: Expectations are often high when it comes to developments in machine learning. When projects don't deliver the expected results right away, stakeholders can become impatient or lose faith altogether. Clear communication and realistic expectations are key to successful machine learning projects.
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This article was edited by LinkedIn News Editor Felicia Hou and was curated leveraging the help of AI technology.
Engineering Leader @ Morningstar | Cloud Architect, AWS, C#
2 年There are two top challenges to introducing a machine learning feature at any organization (1) Lack of vision from product and technology leaders (2) Selection of business problem to solve. Leaders need to understand what problems ML can and can't solve. Based on this, they need to pick a problem ML can solve and deliver maximum values to the clients. Learning ML, gathering data and training an algorithm are minor tech challenges which any technologist can overcome.
Head of Marketing Data and AI @ Equitable
2 年The biggest hurdle by far is Adoption. If customers and staff are not sure of how to interact and use the algorithm created, then that can cause distrust in your analytics team along with the ML as being not valuable. Focus on leveling up your organization on AI and ML. Product managers are great at products, customer service teams are great at service. It's unfair to expect them to under AI and how to properly use it. Develop training programs that level up your team members who consume analytics and AI. Define what AI means at your organization and create a foundational understanding, so everyone is talking apples to apples. Dispel myths and unknowns about AI, and help them play through business case scenarios to get an interactive feel for how and when AI should be used, when insights should be used, or at best, when not to use these!
Coaching Women in Tech to Build Executive Presence, Make an Impact, and Earn the Recognition & Rewards They Deserve | Executive & Leadership Coach | Fractional Eng & Product Leader/Advisor | ex-VPE at Tile | ex-Apple
2 年One challenge I’ve seen is ensuring that the underlying data being used for machine learning is not biased. There have been numerous examples in tech where this has been an issue. One example was Amazon’s recruiting engine that was supposed to help them figure out who to proceed to interviews. It turned out that so many of the resumes they trained the system with were male resumes that the system was unintentionally biased against women.? To combat this we need to ensure we are taking into account the potential biases in the data we use for machine learning.?
CEO @ Power-MI | Predictive Maintenance, Mechatronics, Executive Leadership
2 年I've helped clients in implementing Machine Learning to predict machinery failure. The most difficult challenge is to obtain reliable results in such a way that maintenance work orders generated by the algorithm can be executed without the need for a "second opinion" from analysts. If an analyst's diagnosis is still required, Machine Learning isn't worth it.
CEO @ Seerist | Leading Global Businesses | AI-driven Risk Management
2 年Building an understanding of the role of the machines and the humans. Machines take us a long way to the end but humans are still vital to final outcomes. In most business applications, ML models are built to predict outcomes (make decisions) in large datasets. The challenging points come from determining the application of ML to a specific desired outcome (problem being solved), choosing the appropriate data to solve the problem and making the right design choice (training dataset broad enough contain the answer and narrow enough for relevant inference) and importantly constantly training the models to “see” things that they previously didn’t recognize so that new anomalies become adopted, and the efficacy of the model remains viable.?Finally, having the best humans who can work through how to engineer the desired outcome consistently.