Machine learning is becoming more accessible than ever through open-source tools, APIs, and libraries. But how do companies overcome challenges when adopting machine learning? What are some pros and cons of this approach? We spoke to several developers who shared their experiences.
The growth of inaccessible machine learning has made it easier than ever for developers to incorporate machine learning into their applications without becoming experts themselves. Not only have that, but significant providers including Amazon, Google, and Microsoft opened up their machine learning capabilities as APIs. This means companies can quickly spin up utilities that incorporate sophisticated algorithms, which would have taken months to develop even a few years ago.
But many organizations are still hesitant about using these technologies due to fears around data privacy, security vulnerabilities, and other risks which come with machine learning. Machine learning is enabled through APIs by Google Cloud Natural Language API, IBM Watson Conversation Service API, Microsoft Cognitive Services API or Wit.ai for Facebook Messenger,” says Pradyumn Shroff, co-CEO of Mya Systems. “Consumers have access to the same sophisticated algorithms that were reserved for enterprises only a few years ago.
Easy access tools
However, many people don’t realize that because these tools are accessible via APIs rather than cumbersome software packages designed for developers, it makes them exponentially easier to integrate into web services and mobile applications—and fast. Companies can also take advantage of these tools without having internal data science teams because they interface like any other cloud service.
For example, Mark Zuckerberg announced the rollout of Wit.ai for Facebook Messenger last January. Users can type or chat using their voice to interact with services on Messenger—and all without having to download an app or even leave the Messenger interface. Meanwhile, developers can implement Wit’s text and voice recognition tools into apps that have billions of users across iOS, Android, web, car systems, smart TVs, etc., within just a few lines of code through APIs. There’s no need to hire data scientists anymore, Shroff says. “Today, nearly every developer knows how to use what were once considered esoteric languages.”
Hesitation in adopting machine learning
On the other hand “Sitting alongside these benefits is a concurrent set of drawbacks, which might explain why many companies still hesitate to adopt machine learning,” says Simon Rodgers, senior data scientist at Reply. “The first factor which often holds people back is security and privacy concerns. A user’s location, browsing history, or information about their contacts can be invaluable to an attacker who seeks to compromise sensitive corporate data.”
Machine learning models are not magical pixie dust. They’re just another software component that needs careful consideration before it is deployed into production systems,” continues Rodgers. “Not all models are necessarily well designed or optimized for certain use cases, so it’s important that organizations don’t assume that the latest model will always perform better than traditional ones.”
Another downside of machine learning is that it can’t always perform optimally say RemoteDBA.com experts. “For example, models are not necessarily well designed or optimized for certain use cases,” Rodgers says. Sometimes high accuracy does not equate to optimal performance—especially in mission-critical systems.
Challenges of machine learning
Finally, training the underlying models presents challenges of its own. Manual configuration is tedious and error-prone, while automated configuration doesn’t produce the best models every time. And sometimes, different teams within an organization require slightly different algorithms to suit their needs—which mean they need to train multiple machine learning models separately instead of train one model for all situations.
Organizations need smart people who are good at mathematics, enjoy thinking through problems systematically, who is meticulous when it comes to data processing, and can understand how to debug errors in algorithms. The question now is how many of those people do you have?”
What are the challenges in adopting machine learning? ML implementation is fast, easy,, and cheap; On the other hand, security and privacy concerns arise; Implementing ML models still require someone skilled; Finally, training different teams separately presents challenges.
It’s much easier for companies to integrate machine learning into their web services/mobile apps because it only takes one line of code to initiate via APIs; On the other hand, there are still security/privacy concerns that need to be handled with care because APIs reach a larger audience than any app could ever dream of reaching without implementing a single line of code; Implementing machine learning models still requires someone skilled to monitor the models’ progress/tweaks because they are not one-size-fits-all, but instead need to be customized for specific use cases/audiences. Finally, different training teams separately present challenges of its own.
What are the challenges in adopting machine learning? Machine learning models can be insecure/dangerous if not implemented correctly; On the other hand, these concerns have to do with its implementation—not necessarily because of what it is/does. Implementing machine learning models still requires someone skilled to utilize them properly because they’re customized for specific use cases; different Training teams separately presents challenges of its own. Check Cartoons Torrent Websites
Growth of Machine Learning
The practice of machine learning has grown exponentially over the past few years, but not all companies are quick to adopt. What are some of the main reasons behind this hesitation? How can companies overcome these hurdles? Why would machine learning help an organization? What are the challenges in adopting machine learning Machine learning models can be insecure/dangerous if not implemented correctly; Implementing machine learning models still requires someone skilled; Training different teams separately presents challenges of its own.
- As machine learning models are not magical pixie dust, they require careful consideration before being deployed into production systems.
- The practice of machine learning has grown exponentially over the past few years, but some companies hesitate to adopt it because certain aspects are tedious and time-consuming.
- Machine learning can be beneficial for organizations because it’s faster/more accessible than traditional methods.
- Not all organizations have “smart people” who enjoy solving problems or are meticulous with data management—which means that implementing machine learning models is more difficult without these skillsets on staff.
Machine learning models can be insecure/dangerous if not implemented correctly; implementing machine learning models still requires someone skilled; different Training teams separately presents challenges of its own. The practice of machine learning has grown exponentially over the past few years, but some companies hesitate to adopt it because certain aspects are tedious and time-consuming. As machine learning models are not magical pixie dust, they require careful consideration before being deployed into production systems.
