At this point there is no doubt that any organization can take the advantage of AI/ML into their business process. The significance of machine learning application will depend on how it is applied and what kind of problem you as an organization trying to solve. Although ML is a relatively accessible technology with improved hardware and open sources tools, only a small portion of businesses are able to benefit it from today.
What is the problem?
While machine learning offers advantages for nearly every industry, very few companies have actually adopted it for real business use cases. Here are some of the fundamental barrier to adoption:
- Misalignment between business need and what ML can do
Most businesses have a hard time mapping business problems to ML capabilities. On the one hand, there are a lot of advanced ML models created and published that could potentially solve business problems and enable new ways to perform tasks. On the other hand, it seems quite difficult to find and adapt those models to extract value for businesses.
2. Lack of required talent and data science skills
AI/ML industry is relatively new, finding the right people is a challenge. There are not many experienced specialists available and most of them are already employed by major companies or research institutions. Also there is simply the lack of awareness about the capabilities and limitations of ML and how it can be tailored to meet your use cases.
3. Lack of quality data
Data, of course, is needed to train machine learning algorithms. Many companies simply don’t have the data assets necessary for such training. Even if you have the data it is never clean. Companies need to spend most of their time cleaning and preparing the data and cost and ROI becomes a factor.
4. Lack of trust and explainability
Since many machine learning models are black boxes, it’s difficult to explain with confidence how they came to their decisions — making them difficult to trust in fields such as medicine, business, and finance. Another issue is around bias, privacy, fairness in the results provided by the ML model.
5. Hard to show business value
Most companies do not have time and patience to invest in trials or proof of concepts using open source models. It takes long time really nail down an enterprise use case when ML can benefits the bottom line.
How do we close the adoption gap?
Here are 7 tips to reduce the barrier to adoption:
1.Business managers must understand capabilities and limitations of ML
Businesses should invest into increasing their personnel’s understanding about the type of problems ML is good for, as well as what it can and cannot do. This can be achieved by investing in training programs or online courses (MOOCS), attending ML conferences and interactive workshops that can help them recognize areas where an ML solution is ideal.
2. Develop a standard way to find, share and use models
To make ML research more accessible and accelerate its adoption, there needs to be a standard way for researchers to share their models and for developers and data scientists to easily access them without the need for re-training or complex environment setups. This could also be a way for researchers to track usage and further improve their models, as well as capture economic value from innovations without much additional effort.
3. Reduce need of training data
Training a machine learning model can require up to millions of data elements, and acquiring and labeling this data can be time consuming and costly for enterprises. However, we’ve seen a number of techniques emerging for reducing the amount of training data required for machine learning. Some use synthetic data, generated with algorithms to mimic the characteristics of the real data, and have seen strong results.
Enterprises can also use transfer learning, an approach in which a machine learning model is pre-trained on one dataset as a shortcut to learning on a new dataset in a similar domain, such as language translation or image recognition.
4. Focus on automating data science using data science workflow
There is need to be an increase in investments in out-of-the-box solutions or tools that simplify the implementation and deployment of ML. This would enable developers even without a strong ML background to benefit from it. Facebook is an example of a company that has heavily invested in making ML accessible internally through their FBLearner platform, which automates the building, training and scaling of ML algorithms. This platform is used by more than 25% of Facebook’s engineering team and has trained over a million models.
The image above explains how software engineers can expedite the work of data scientists by create fully automated machine learning system which perform the repetitive tasks of data scientists in full automated fashion. At this point data scientists are open to use their time to solve newer problems and just keep an eye of the automated system to make sure it is working as their expectation.
5. Ability to explain results
Trust and fairness is a big challenge is some complex models. MIT researchers have developed a method of training a neural network that delivers accurate predictions along with the rationales for those predictions. These kind of tools will allow companies in highly regulated industries to find more opportunities to use machine learning, the report said, including in areas such as credit scoring, recommendation engines, fraud detection, and disease diagnosis and treatment.
6. Use Cloud AI/ML APIs to solve your business problem
Various organization i.e. Google (i.e. CloudML), H2O (i.e. AutoML) has created automated machine learning software which can be utilized by any organization. There are open sources packages also available i.e. Auto-SKLearn, TPOT.
7. Analytics on AI: Build Value Realization Dashboard
Set your business goals and identify the KPIs that you will use to track the progress. Plot the KPIs on a trend charts to show the weekly/monthly move. Convert productivity increase, time saving, CSAT increase, cost reductions in to $ saving or revenue increased and show it on your dashboard. Automate the value realization collection and share it with your stakeholders.
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