Aatish has over 20 years of experience working on several cross-country and strategic collaboration projects in the areas of semiconductors & academic research, energy & utilities, and digital & agile business transformation. He has authored 18 publications in the above areas in several international peer-reviewed journals and conferences. He has completed Bachelors and Masters of Technology in Electrical Engineering from Indian Institute of Technology (IIT) Bombay, India, and Master of Business Administration in Digital Business and Innovation from Vrije Universiteit (VU) Amsterdam, the Netherlands. Next to his role as a digital accelerator at Eneco Energy Trade, Aatish is leading a team of data professionals to create a future-ready digital analytics and storage platform which is able to drive and sustain several digital acceleration initiatives by fostering symbiotic and synergistic associations between the business and IT. Due to his amicable personality and people management skills, Aatish is able to stimulate and motivate team members to create and sustain high-performing teams with a growth mindset. Aatish is a certified Professional Scrum Product Owner and Professional Scrum Master. Next to being a passionate Agile Scrum practitioner, he is a motivated digital transformation enthusiast and has completed several cloud (Azure/Data/AI Fundamentals and Azure Data Scientist Associate) certifications. A firm believer in life-long learning, Aatish continuously upskills himself by following webinars and massive online open courses in diverse areas such as negotiations, e-commerce, machine/deep learning, and cloud technologies. He is involved in several knowledge dissemination initiatives within his direct team as well as outside by conducting focused workshops and sessions. Specialties Cloud Technologies | Business/IT Transformation | Business Development | Stakeholder Management | Growth Hacking | Digital Acceleration | Agile | Scrum

My Mentoring Topics

  • Data Science
  • Data Engineering
  • Azure
  • Agile
  • Scrum
  • Kanban
  • Product Management
  • Project Management
  • Digital Transformation
  • Team Management
  • Team Leadership
l.
12.March 2024

U.
3.February 2024

I had an incredibly insightful session with Aatish. He patiently listened to my questions and concerns about choosing an Executive MBA specialization. His detailed insights and guidance were invaluable, and I truly appreciate his amicable and supportive approach. The session has helped me feel much more confident and clear about my path forward

N.
28.January 2024

Aatish is an exceptionally knowledgeable and insightful mentor in the data science field. Our conversation was not only a pleasure but also incredibly enlightening. He provided deep and thoughtful insights into my career goals and aspirations, offering practical advice tailored to my specific interests in energy procurement and trading analysis. His expertise in data science and its application in the energy sector is evident and invaluable. His guidance has given me a clearer perspective on my career path and the steps I need to take to achieve my objectives. Truly grateful for the opportunity to learn from him.

e.
2.July 2023

Dear Mentor Aatish is well-mannered, friendly person. I felt like I knew him many years ago during the our conversation. He is an IT professional in the Netherland. He is ready to aid to you in the data science career journey. He shared with some significant resources in the excel. He advised to me important tips on how to find entry level jobs in Netherland. Do not hesitate ask him. Thank you for your meaningful support. Best Regards

S.
9.May 2023

I love the insight and suggestions from the session. It gave me path to move further with my masters and internship. Loved it!!

Data Mesh
Zhamak Dehghani

Key Facts and Insights from Data Mesh Decentralization: The concept of decentralized data ownership is a key theme in this book, advocating for a shift from traditional monolithic architectures to distributed data domains. Domain-oriented data: The book emphasizes on the need for domain-oriented decentralized data teams, breaking down silos and improving efficiency. Data as a product: Data Mesh encourages treating data as a product with its own lifecycle, instead of a by-product of operations. Data governance: The book introduces a federated computational governance model, shifting away from the centralized model of data governance. Data discovery: The book emphasizes the importance of data discovery and making data easily accessible and understandable for all stakeholders. Scalability: Data Mesh's decentralized approach offers a scalable solution to handling large volumes of data. Technological independence: The book argues for the need of technological independence for each data product team. Infrastructure automation: Data Mesh recognizes the role of automated infrastructure in improving productivity and reducing manual errors. Data quality: The book emphasizes the significance of data quality and the role of data product owners in maintaining it. Team autonomy: Data Mesh promotes team autonomy and cross-functional teams for effective data management. Interoperability: The book underscores the importance of interoperability and standardization in a distributed system. An In-depth Analysis of Data Mesh In her groundbreaking book, Zhamak Dehghani introduces Data Mesh, a novel paradigm for data architecture and management. Through this paradigm, Dehghani challenges the traditional centralized, monolithic data architecture that has been prevalent for decades. The core principle of Data Mesh is decentralization. The book argues that by distributing data ownership among multiple domain-oriented teams, organizations can break down silos and improve the efficiency of data operations. This is a stark departure from the traditional approach where a centralized team is responsible for managing all data. From my years of experience in the field, I can attest that such a shift can indeed lead to improved agility and speed in data operations. Another significant concept introduced in the book is treating data as a product. This implies that data should have its own lifecycle, with a dedicated team responsible for its management, instead of being considered as a by-product of operations. This paradigm shift aligns with the broader trend in the industry to value data as a strategic asset. The book also provides insights into the governance of data in a decentralized environment. It introduces a federated computational governance model, which is a marked change from the centralized model of data governance. This model emphasizes the role of data product owners in ensuring data quality, a critical factor in any data-driven decision-making process. At the heart of the book is the idea of technological independence for each data product team. This means that each team has the liberty to choose the technology stack that is best suited for their specific requirements. In my view, this can lead to innovation and better performance, but also requires a robust framework for interoperability to ensure seamless integration of various data products. Infrastructure automation also plays a significant role in the Data Mesh model. The book recognizes the role of automated infrastructure in improving productivity and reducing manual errors. This aligns with the broader trends in the industry towards DevOps and Infrastructure as Code (IaC). Last but not least, the book underscores the importance of data discovery in a distributed system. It emphasizes the need to make data easily accessible and understandable for all stakeholders, which is a key requirement for any successful data-driven organization. Overall, Data Mesh offers a fresh perspective on the challenges of data management in the modern world and provides practical solutions to address them. Its emphasis on decentralization, domain-oriented teams, and treating data as a product is a valuable contribution to the field. Though the implementation of these concepts would require significant changes in the organizational structure and culture, the potential benefits make it worth considering.

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Making Data Work - Enabling Digital Transformation, Empowering People and Advancing Organisational Success
Edosa Odaro

Key Facts and Insights from the Book: Data is the new oil: The book emphasizes the importance of data in the digital age, comparing it to oil in terms of its value and potential to drive growth and innovation. Digital Transformation: The author defines digital transformation as a process of integrating digital technology into all areas of a business. It changes how businesses operate and deliver value to customers. Data-driven Decision Making: The book highlights the importance of using data to make informed decisions. It suggests that data-driven organizations are more successful than their counterparts. Empowering People: The author argues that it's not just about the data or the technology, but also about the people. He underscores the need to empower employees with the right tools and knowledge to leverage data effectively. Data Governance: The book delves into the principles of data governance and its role in maintaining data quality, privacy, and security. Role of Leadership: The author recognizes the role of leadership in driving digital transformation and data-driven culture. Impact on Organisation's Success: The book underscores the impact of effective data management and digital transformation on an organisation's success. Case Studies: The book provides several real-world case studies to demonstrate the application of the concepts discussed. A Roadmap for Implementation: The book provides a step-by-step guide for implementing a data strategy and driving digital transformation in an organization. Data Literacy: The author stresses the importance of data literacy across all levels of an organization. In-Depth Summary and Analysis: "Making Data Work - Enabling Digital Transformation, Empowering People and Advancing Organisational Success" by Edosa Odaro is a comprehensive guide for any individual or organization looking to leverage data for digital transformation. The book is not just about the technological aspects of data management, but also about the human element - the people who use and manage the data. The author begins by emphasizing the importance and value of data in the digital age. He compares data to oil, in terms of its potential to drive growth and innovation. This analogy is quite apt, as just like oil, data too needs to be refined to extract valuable insights from it. The concept of digital transformation is thoroughly discussed in the book. Odaro defines it as a process of integrating digital technology into all areas of a business, fundamentally changing how businesses operate and deliver value to customers. He underscores the importance of a data-driven approach in this transformation journey. Data-driven organizations, as the book suggests, are often more successful than their counterparts as they base their decisions on concrete data rather than intuition or assumptions. The book also underlines the significance of empowering people in an organization. The author argues that it's not just about the data or the technology, but also about the people who use the data. Employees need to be empowered with the right tools and knowledge to leverage data effectively. This is where data literacy comes into play. The author stresses the need for data literacy across all levels of an organization. The principles of data governance are also explored in the book. It explains how data governance plays a crucial role in maintaining data quality, privacy, and security. It ensures that data is managed in a way that it can be trusted and used effectively to drive decision-making. The author recognizes the role of leadership in driving digital transformation and a data-driven culture. Leaders should champion the use of data, promote data literacy, and foster a culture of data-driven decision making. The impact of effective data management and digital transformation on an organisation's success is underscored throughout the book. It provides several real-world case studies to demonstrate the application of the concepts discussed. Lastly, the book provides a step-by-step guide for implementing a data strategy and driving digital transformation in an organization. This roadmap can be immensely helpful for organizations embarking on their data and digital transformation journey. In conclusion, "Making Data Work - Enabling Digital Transformation, Empowering People and Advancing Organisational Success" by Edosa Odaro is a must-read for anyone interested in data management, digital transformation, and organizational success. The book provides valuable insights, practical guidance, and a clear roadmap for leveraging data to drive digital transformation and organizational success.

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The Kaggle Book - Data analysis and machine learning for competitive data science
Konrad Banachewicz, Luca Massaron, Anthony Goldbloom

Key Insights from "The Kaggle Book - Data analysis and machine learning for competitive data science" Data Analysis and Machine Learning are integral to Competitive Data Science: The book discusses how data analysis and machine learning are applied in competitive data science using Kaggle platform. Competitions are Learning Opportunities: The authors highlight that participating in Kaggle competitions is a great opportunity to learn and improve one's data science skills. Understanding and Preparing the Data: The book provides a comprehensive guide to understanding and preparing data for analysis, including data cleaning, data transformation, and feature extraction. Model Selection and Validation: The authors discuss various machine learning models, their suitability for different types of data, and strategies for model validation. Ensemble Methods: The authors delve into ensemble methods, which combine multiple algorithms to achieve better predictive performance than could be obtained from any of the constituent learning algorithms alone. Optimization Techniques: The book explores various optimization techniques that can be used to improve the performance of machine learning models. Practical Advice from Kaggle Masters: The book includes interviews with Kaggle masters who share their experiences and provide practical advice for succeeding in Kaggle competitions. Case Studies: The book features several case studies that demonstrate how to apply the concepts and techniques discussed in real Kaggle competitions. Learning Resources: The authors provide a list of recommended resources for further learning, including books, online courses, and blogs. Focus on Continuous Learning: The authors emphasize the importance of continuous learning in the field of data science. Kaggle as a Launchpad: The book highlights how Kaggle can serve as a launchpad for a successful career in data science. An In-depth Analysis of "The Kaggle Book - Data analysis and machine learning for competitive data science" Written by Konrad Banachewicz, Luca Massaron, and Anthony Goldbloom, "The Kaggle Book" is a comprehensive guide to competitive data science. It is written with the central idea that data analysis and machine learning are not only integral to competitive data science but also serve as a foundation for the entire data science field. Kaggle as a Learning Platform The authors rightly establish that Kaggle, being the world's largest data science community with powerful tools and resources, is an excellent platform for learning and honing data science skills. By participating in Kaggle competitions, one can gain practical experience, learn from the community, and even earn recognition. This experiential learning is more effective than traditional classroom learning, as it offers real-world problems to solve. Data Understanding and Preparation The authors dedicate a significant portion of the book to data understanding and preparation, which is often the most time-consuming part of a data science project. They provide insightful techniques for data cleaning, transformation, and feature extraction, emphasizing the importance of these steps in building effective machine learning models. Model Selection and Validation "The Kaggle Book" offers a detailed exploration of various machine learning models and their suitability for different types of data. It also provides strategies for model validation, thereby ensuring that the chosen model is a good fit for the data and the problem at hand. Ensemble Methods and Optimization Techniques Ensemble methods and optimization techniques form the core of competitive data science, as they can significantly improve the performance of machine learning models. The authors explain these concepts in a simple and approachable way, making them easy to understand even for beginners. Practical Advice and Case Studies The authors complement the theoretical discussion with practical advice from Kaggle masters and case studies from real Kaggle competitions. These insights provide a window into the world of competitive data science, giving readers a taste of what it's like to participate in Kaggle competitions. Continuous Learning Throughout the book, the authors emphasize the importance of continuous learning in the fast-paced field of data science. They provide a list of recommended resources for further learning, encouraging readers to keep updating their skills and knowledge. In conclusion, "The Kaggle Book - Data analysis and machine learning for competitive data science" is a valuable resource for anyone interested in competitive data science. It provides a thorough understanding of the subject, along with practical advice and insights from experts in the field. The authors' emphasis on experiential learning through Kaggle competitions and continuous learning echoes my own belief that data science is a field of constant exploration and growth.

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Implementing MLOps in the Enterprise - A Production-First Approach
Yaron Haviv, Noah Gift

Key Insights from the Book: MLOps, or Machine Learning Operations, is a practice for collaboration and communication between data scientists and operations professionals to help manage production ML lifecycle. The book emphasizes a Production-First Approach, which involves thinking about the end goal - production, from the very beginning of the ML project. Model Management is a vital aspect of MLOps, which includes versioning, packaging, validation, and distribution of models. The book details the Continuous Integration and Continuous Deployment (CI/CD) pipelines for ML models, which is essential for effective MLOps. The book also covers monitoring and governance in the context of MLOps, including model performance tracking, data drift detection, and model explainability. Yaron Haviv and Noah Gift discuss the importance of collaborating between various roles like data scientists, ML engineers, and business stakeholders for successful MLOps. Automation is another key aspect highlighted in the book, which can drastically improve the efficiency and effectiveness of ML operations. The authors also touch upon the challenges in implementing MLOps, including technical debt, cultural resistance, and the lack of standardized tools and practices. The book provides real-world examples and case studies to illustrate the application of MLOps in different business scenarios. Finally, the book offers a roadmap for enterprises to implement MLOps, starting from defining the business problem to deploying and monitoring the model in production. In-depth Analysis: "Implementing MLOps in the Enterprise - A Production-First Approach" by Yaron Haviv and Noah Gift is an indispensable guide for anyone interested in understanding and implementing MLOps in their organization. The book begins by introducing the concept of MLOps, pointing it as a practice that brings together data scientists and operations professionals to manage the production ML lifecycle. This definition is in line with the growing recognition that ML models' success depends as much on the operational aspects as on the quality of the algorithms and data. The book's central theme is the Production-First Approach. The authors argue that thinking about production from the beginning of the ML project can save a lot of headaches down the line. This approach resonates with the principles of DevOps, where the focus is on the end-to-end delivery of software products. It also aligns with my long-held belief that ML projects should be driven by business needs rather than technological prowess. Model Management is another critical topic covered in the book. It involves versioning, packaging, validation, and distribution of models. The authors convincingly argue that without proper model management, it would be challenging to maintain, update, and monitor models in production. This insight is particularly relevant in today's fast-paced business environment, where models need to be updated frequently to stay relevant. The authors provide a comprehensive discussion on CI/CD pipelines for ML models. CI/CD, or Continuous Integration and Continuous Deployment, is a software engineering practice where developers integrate their changes back to the main branch as often as possible. The authors adapt this practice for ML models, highlighting its importance in MLOps. They provide a detailed guide on how to set up CI/CD pipelines for ML models, which I consider a valuable resource for practitioners. The book also delves into monitoring and governance of ML models. Here, the authors discuss tracking model performance, detecting data drift, and explaining models. This section is particularly noteworthy because these are areas often overlooked in traditional ML projects but are critical for the success of ML models in production. The authors emphasize the importance of collaboration between various roles like data scientists, ML engineers, and business stakeholders. I agree with this viewpoint as ML projects are inherently cross-functional, and effective collaboration can significantly improve the project's outcomes. The book also highlights the role of automation in MLOps. Automation can help improve efficiency, reduce errors, and free up time for more strategic tasks. The authors provide practical tips and tools for automating various parts of the ML lifecycle, from data collection to model deployment. The authors are not shy about discussing the challenges in implementing MLOps. They cover a range of issues, from technical debt to cultural resistance. This discussion is essential as it prepares readers for the potential roadblocks they might encounter on their MLOps journey. The book includes several real-world examples and case studies, which bring the concepts to life. These examples can help readers understand the practical applications of MLOps and how it can benefit their organizations. Finally, the book provides a roadmap for enterprises to implement MLOps. This roadmap is a step-by-step guide, starting from defining the business problem to deploying and monitoring the model in production. This roadmap can serve as a practical guide for organizations embarking on their MLOps journey. In conclusion, "Implementing MLOps in the Enterprise - A Production-First Approach" is a comprehensive guide to MLOps, filled with practical insights and actionable advice. It is a must-read for anyone involved in ML projects, from data scientists and ML engineers to business leaders and IT professionals.

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