- “Low-code machine learning is gaining popularity with tools like PyCaret, H2O.ai and DataRobot, allowing data scientists to run pre-canned patterns for feature engineering, data cleansing, model development and statistical performance comparison. However, often the missing pieces of these packages are patterns around responsible AI that evaluates ML models for fairness, transparency, explainability, causality and more.”
- “Builder.ai, the AI startup that has built a low-code/no-code app development platform, has raised $100 million in a Series C funding round led by Insight Partners.
- According to the startup, Builder.ai builds software and apps that are up to 6x faster and is around 70 percent cheaper as compared to other avenues. It claims that its revenue increased by over 300 percent and deployed more than 40,000 features in the last one year.
- Jungle Ventures Founding Partner Amit Anand said, ‘Organisations today, more than ever, are in need to be changing, innovating and deploying new ideas fast. This requires a stable but agile software development platform that can leverage innovative technologies such as AI and low-code/no-code to enable rapid digital transformation and create real time impact for stakeholders.’”
- “Domain expertise is key; machines don’t have the depth of context that people have, and people need to know the business and data well enough to understand which actions to take based on any insights or recommendations that are surfaced.
- The 2021 Gartner® The State of Data Science and Machine Learning (DSML) report states that “client demand is shifting, with less-technical audiences wanting to apply DSML more easily, experts needing to improve productivity and enterprises requiring shorter time to value for their investments
- With a more iterative, revise-and-redeploy model building processes, people with business context can get value from AI faster—even deploying new models to thousands of users within days to weeks, instead of weeks to months. This is especially powerful for those teams whose unique challenges may not be a high priority for data science teams, but can benefit from the speed and thoroughness of AI analysis.
- McKinsey’s latest global survey on AI revealed that within 34% of high-performing organizations “a dedicated training center develops nontechnical personnel’s AI skills through hand-on learning,” compared to only 14% of all others surveyed. Additionally, in 39% of high-performing organizations “there are designated channels of communications and touchpoints between AI users and the organization’s data science team,” compared to only 20% of others.”
“Airtable was co-founded by Howie Liu, who sold his other start-up — enterprise software company Etacts — to Salesforce in 2011. As Airtable’s CEO, Liu’s mission is to make the complex world of software more user friendly. Up until companies like Airtable started creating low-code and no-code software, it wasn’t really accessible to most people. Now with no coding background required, workers can build apps and workflows.
The fast-growing code-for-everyone-else approach allows professionals who aren’t fluent in coding languages such as Java or Python, and don’t have their desk buried deep within the stack, to play a part in rethinking and remaking the consumer and client digital experience.”
- “Moreover, it is predicted that roughly 80% of businesses are investing in AI, with 47% of digitally advanced companies already defining AI strategies. Even more amazingly, AI tools should provide $2.9 trillion in corporate value in the foreseeable future.”