In today’s world, the relationship between artificial intelligence (AI) and data science has strengthened into a powerful force that is able to improve decision-making and innovation since large amount of data are now being made available. Once upon a time, the data science workflow process was long and tedious, as it utilized extensive manual processes in the collection of data (often from multiple data stores); cleaning the data (including identifying missing values, correcting inconsistencies and errors in the data, etc.); building a predictive model; and deploying a predictive model. Presently, with the increased collaboration of AI in the workflow, data science workflows are becoming smarter, shorter, and more scalable. AI is not merely supportive of the workflow process; AI is an integral aspect that optimally improves each aspect of the data science life cycle. One of the most labor daunting aspects in every data science project is performing data cleaning and preprocessing. When collecting data, it is not uncommon for the researcher to experience an extraordinary amount of missing observations, and/or errors in the data as well; not to mention the sometimes significant changes in the data collection process, that would require consistency, to say nothing of extracting/transforming a large amount of raw data into something functional. AI tools can automate the basic cleaning process by highlighting patterns, anomalies, and potential errors without human intervention. With the use of field dependent natural language processing (NLP), AI can literally clean unstructured textual data by recognizing entities, correcting grammar, and removing unwanted text. These AI functions allow data scientist to spend the time on insights and not worrying about data wrangling. Cleaning data through AI will also greatly reduce the potential para-data bias by leaving data as “raw” as possible. The presence of AI, will reduce precious processing and pre-processing time for a data scientist by fully automating aspects of the workflow process and thus, optimize the workflow overall. At the modeling stage, AI offers unmatched speed and accuracy. Machine learning processes huge amounts of data from the database, recognizing patterns and generating models that successfully predicts outcomes. If we add an automated machine learning (AutoML) product, we can build data scientist-level models with even less effort by automatically selecting features, hyperparameter tuning, and evaluating models (all automatically, like magic, and without writing a single line of code). For a learner or professionals that would like to learn and master these areas, I recommend you join an [Artificial intelligence Course in Pune](https://www.sevenmentor.com/artificial-intelligence-training-courses-in-pune.php) to gain a solid understanding of the theory and practice of these tools. Feature engineering may be the most challenging task, and perhaps even an art, in data science; however, AI has taken significant leaps in this area too. AI technology applies deep learning processes to obtain informative features from raw data. Types of data where this is very applicable include image data, voice data, and time-series data. Auto-generating intelligent features not only saves an incredible amount of time, but often recognizes insightful and meaningful features that inform your data no matter how much time you dedicate to the manual method. Using intelligent systems, such as convolutional neural networks and recurrent neural networks, allows data scientists to feel comfortable with the application of predictive modelling to difficult problems. Model interpretation and explainability, essential in regulated industries such as healthcare and finance, have also been boosted through AI. More advanced methods like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) assist in uncovering what happens in machine learning models to give stakeholders a better understanding of their predictions and contribute to the transparency of how AI predictions are made. With an improved understanding of its techniques and more trust in AI-generated decisions, even more organizations are finding more ways to use new AI experiences in secure and sensitive, high-value environments. Most people who have taken [Artificial Intelligence Training in Pune](https://www.sevenmentor.com/artificial-intelligence-training-courses-in-pune.php) have learned about those concepts, so they can build explainable AI models with some level of performance and compliance. AI plays a role after modeling and during model deployment and monitoring. Intelligent systems are often capable of continually learning as new data comes in, resulting in models that evolve automatically without the effort associated with retraining. The monitoring of models is made possible by AI recognizing the need to an update due to drift in data, concept, or degradation of performance over time. As a result, models can be used with confidence if the data or business context evolves. As workflows adapt in this way, they illustrate how AI has moved data science from a linear, discrete series of steps to a cyclical set of iterative improvements. For students and professionals wanting to shift into this fast-developing field, enrolling in [Artificial Intelligence Classes in Pune](https://www.sevenmentor.com/artificial-intelligence-training-courses-in-pune.php) will provide valuable hands-on exposure. These classes will train students on the theoretical foundations of AI and machine learning but, just as importantly, they offer real-world experience - walking learners through projects involving data preprocessing, building, validation and deployment. Students participating with these AI-enhanced workflows will guarantee that they are "work-ready" and capable of being productive in today's data science teams. Ultimately, the introduction of AI into data science workflows isn't an evolution; it's a revolution. AI mechanizes routine tasks, enhances prediction accuracy, improves interpretation, and encourages a continuous learning loop, which will make the application of data science more sustainable, successful, and obtainable. As organizations continually use data to shape their strategic decision making, there will always be a need for professionals who understand both AI and its impacts upon data science workflows. Those who learn this will not only be well-positioned for the present and future, but they will be participants in the evolution of tomorrow's intelligent systems.