Date2022-02-04 11:03

The 5 Reasons why 2022 is the Year of Machine Learning Success

The 5 Reasons why 2022 is the Year of Machine Learning Success

All tech trends are not made equal. Some trends last for a couple of years and are worth avoiding….remember the excitement over the Smart Suitcase (the lithium battery powered smart luggage just before stricter aircraft restrictions were introduced) or the Google Glass (discontinued due to new privacy laws and not to mention the extravagant price!).

But every few years a tectonic shift caused by a disruptive technology has the potential to transform operational and executive decisions, the products and solutions you offer your customers, and the fundamental way you do business. Those that embrace this transformative innovation are able to increase customer satisfaction, drive profitability and stay ahead of competition.  Those that fail, risk being left behind and cease to grow.

Data Centric Artificial Intelligence is the present tectonic shift. The focus on high quality data training models that can determine how fast and how successfully you deliver production level machine learning solutions.         

Here are the 5 reasons why 2022 needs to be the year you partner with ML experts:

1. “Responsible AI” is no longer a buzzword but now an operational term

Unfortunately, many AI systems and solutions today have several hidden biases that have not been carefully managed with more diverse datasets and sharper algorithms. Regulators are now beginning to hold such companies accountable. Companies must ensure that their AI systems remain fair and responsible. Those that fail to do so risk damaging their reputations and are culpable for exacerbating inequality.   

2. Greater spotlight on implementing a robust Data Governance Program

In 2022 and beyond, more companies will scale their data governance program and adopt new and modern tools to monitor and detect data quality issues. One of the trends is including machine learning model management and feature management into the data governance solution. In addition, data governance will focus on the alignment between data governance platform providers, computing vendors, and platform vendors to drive usage and adoption.   

3. Data becomes central to key stages in the AI lifecycle

This year, data management for AI will take center stage in the machine learning conversation. By addressing the importance of data management in the AI lifecycle, it will not only be a great way to achieve superior results but also a good measure through which to build trust in AI systems. Collaborating with partners that focus on dataset management will be key to delivering production level ML solutions.    

4. Greater expectations of AI and ML initiatives

In 2022,AI experts believe the IT industry will see a great focus in making AI and ML development processes reproducible and adaptive to changing business environments. 40% of companies have already implemented AI in some form and this year they will be focusing on reviewing and optimising those AI applications to truly provide business value.

5. Reviewing the Workforce Structure

With the ‘Great Resignation’ in the global backdrop, the impact of the talent crunch becomes even more severe. Companies will continue to invest heavily in learning and development programs in 2022. The benefits of such L&D programs will focus on upskilling the current workforce and ensuring internal teams focus on their priority projects and tasks like data labelling are outsourced if needed.      

With 2022 well underway - explore how your business is shifting to 'good data' and robust training models to ensure you deliver successful machine learning solutions. 

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