Industry AI, Analytics, Machine Learning, Data Science Predictions for 2020
Our founder and CEO shares his AutoML 2020 predictions with @kdnuggets http://bit.ly/38EzBjX @dotDataUS #automl #2020Predictions
The AI/Analytics/DS/ML industry companies provided predictions for 2020 based on themes such as Data, Business, democratization of Data Science, AutoML, NLP, Cloud, and DataOps. Machine learning with models is becoming more common, but not yet a dominant framework exists. In 2020, PyTorch or Tensorflow will dominate the broader model training space.
In 2020, we’ll see more containers in the analytics stack, and more people will use Kubernetes for stateless applications such as web servers. Today’s Hadoop platform teams will become the new foundation of the data organization, and the data, AI, and analytics teams will collaborate to derive value from the same data.
In 2020, the data scientist will become a self-service analytics tool, and businesses will finally be able to access their data. Data integration enables self-service analytics, which will result in a larger volume of tasks being performed and a positive impact on the business.
In 2020, natural language processing advances will enable more companies to build AI applications like service chatbots, online question & answer, sentiment analysis, etc. Security and ethics will be a growing concern as companies move data into the cloud.
In 2019 business leaders realized they needed to be more mature in their analytics. In 2020 they will leverage data spiders, bots, artificial intelligence, and NLP to get insights faster.
IoT data will come to fruition, helping lower costs, mitigate downtime and prevent problems before they happen. Container environments will emit a massive volume of metrics that will require a more robust monitoring infrastructure, along with a more innovative storage solution.
New challengers will rise faster in this next decade, and incumbent leaders will fall just as fast, as the ability to harness the power of data will accelerate disruption across the economy.
The Clean Power Movement will create a deluge of data and new analytics use cases over the next decade. Data scientists will need sophisticated data operations and orchestration systems to help manage and utilize this data.
In 2020, the burden of data projects will be removed from highly skilled workers, and the responsibility of data analysis will be democratized, allowing the end-user to discover valuable insights.
In 2020, we’ll see affordable BI tools with NLP capabilities and self-service functionality similar in power and functionality to expensive BI tools.
AI becomes more accessible across the workplace, is deployed in low power, low-cost devices, and is used in real-world industrial applications. Artificial Intelligence (AI) will help with the data quality and training of AI models.
DataOps will gain recognition as an organizational practice in 2020 and beyond and will be a practice data-driven organizations referred to by name in 2020.
In 2020, there will be a skills gap for specialized skills in Apache Spark, and businesses will need tools to solve core business problems and extract insights from data without having a technical understanding. Read the full article at KDNuggets.