Sagarmala is 1st maritime NBFC
New Delhi: India on Thursday floated its first maritime sector Non Banking Financial Company (NBFC) Sagarmala Finance Corporation (SMFCL). Shipping minister
Sarbananda Sonowal
said the company will bridge crucial financing gaps and offer sector-specific financial solutions, empowering ports, startups, and institutions. TNN
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Hans India
26 minutes ago
- Hans India
AI Tools & Skills Every Data Engineer Should Know in 2025
The lines between data engineering and artificial intelligence are increasingly blurred. As enterprises pivot towards intelligent automation, data engineers are increasingly expected to work alongside AI models, integrate machine learning systems, and build scalable pipelines that support real-time, AI-driven decision-making. Whether you're enrolled in a data engineer online course or exploring the intersection of data engineering for machine learning, the future is AI-centric, and it's happening now. In this guide, we explore the core concepts, essential skills, and advanced tools every modern AI engineer or data engineer should master to remain competitive in this evolving landscape. Foundational AI Concepts in Data Engineering Before diving into tools and frameworks, it's crucial to understand the foundational AI and ML concepts shaping the modern data engineer online course. AI isn't just about smart algorithms—it's about building systems that can learn, predict, and improve over time. That's where data engineers play a central role: preparing clean, structured, and scalable data systems that fuel AI. To support AI and machine learning, engineers must understand: Supervised and unsupervised learning models Feature engineering and data labeling Data pipelines that serve AI in real-time ETL/ELT frameworks tailored for model training Courses like an AI and Machine Learning Course or a machine learning engineer course can help engineers bridge their current skills with AI expertise. As a result, many professionals are now pursuing AI and ML certification to validate their cross-functional capabilities. One key trend? Engineers are building pipelines not just for reporting, but to feed AI models dynamically, especially in applications like recommendation engines, anomaly detection, and real-time personalization. Top AI Tools Every Data Engineer Needs to Know Staying ahead of the rapidly changing data engineering world means having the right tools that speed up your workflows, make them smarter, and more efficient. Here is a carefully curated list of some of the most effective AI-powered tools specifically built to complement and boost data engineering work, from coding and improving code to constructing machine learning pipelines at scale. 1. DeepCode AI DeepCode AI is like a turbocharged code reviewer. It reviews your codebase and indicates bugs, potential security flaws, and performance bottlenecks in real-time. Why it's helpful: It assists data engineers with keeping clean, safe code in big-scale projects. Pros: Works in real-time, supports multiple languages, and integrates well with popular IDEs. Cons: Its performance is highly dependent on the quality of the training data. Best For: Developers aiming to increase code dependability and uphold secure data streams. 2. GitHub Copilot Created by GitHub and OpenAI, Copilot acts like a clever coding buddy. It predicts lines or chunks of code as you type and assists you in writing and discovering code more efficiently. Why it's helpful: Saves time and lessens mental burden, particularly when coding in unknown codebases. Pros: Minimally supported languages and frameworks; can even suggest whole functions. Cons: Suggestions aren't perfect—code review still required. Best For: Data engineers who jump back and forth between languages or work with complex scripts. 3. Tabnine Tabnine provides context-aware intelligent code completion. It picks up on your current code habits and suggests completions that align with your style. Why it's useful: Accelerates repetitive coding tasks while ensuring consistency. Pros: Lightweight, easy to install, supports many IDEs and languages. Cons: Occasionally can propose irrelevant or too generic completions. Best For: Engineers who desire to speed up their coding with little resistance. 4. Apache MXNet MXNet is a deep learning framework capable of symbolic and imperative programming. It's scalable, fast, and versatile. Why it's useful: It's very effective when dealing with big, complicated deep learning models. Pros: Support for multiple languages, effective GPU use, and scalability. Cons: Smaller community compared to TensorFlow or PyTorch, hence less learning materials. Best For: Engineers preferring flexibility in developing deep learning systems in various languages. 5. TensorFlow TensorFlow continues to be a force to be reckoned with for machine learning and deep learning. From Google, it's an engineer's preferred choice for model training, deployment, and big data science. Why it's useful: Provides unparalleled flexibility when it comes to developing tailor-made ML models. Pros: Massive ecosystem, robust community, production-ready. Cons: Steep learning curve for beginners. Best For: Data engineers and scientists working with advanced ML pipelines. 6. TensorFlow Extended (TFX) TFX is an extension of TensorFlow that provides a full-stack ML platform for data ingestion, model training, validation, and deployment. Why it's useful: Automates many parts of the ML lifecycle, including data validation and deployment. Key Features: Distributed training, pipeline orchestration, and built-in data quality checks. Best For: Engineers who operate end-to-end ML pipelines in production environments. 7. Kubeflow Kubeflow leverages the power of Kubernetes for machine learning. It enables teams to develop, deploy, and manage ML workflows at scale. Why it's useful: Makes the deployment of sophisticated ML models easier in containerized environments. Key Features: Automates model training and deployment, native integration with Kubernetes. Best For: Teams who are already operating in a Kubernetes ecosystem and want to integrate AI seamlessly. 8. Paxata Paxata is an AI-powered data prep platform that streamlines data transformation and cleaning. It's particularly useful when dealing with big, dirty datasets. How it's useful: Automates tedious hours of data preparation with intelligent automation. Major Features: Recommends transformations, facilitates collaboration, and integrates real-time workflows. Ideal For: Data engineers who want to prepare data for analytics or ML. 9. Dataiku Dataiku is a full-stack AI and data science platform. You can visually create data pipelines and has AI optimization suggestions. Why it's useful: Simplifies managing the complexity of ML workflows and facilitates collaboration. Key Features: Visual pipeline builder, AI-based data cleaning, big data integration. Best For: Big teams dealing with complex, scalable data operations. 10. Fivetran Fivetran is an enterprise-managed data integration platform. With enhanced AI capabilities in 2024, it automatically scales sync procedures and manages schema changes with minimal human intervention. Why it's useful: Automates time-consuming ETL/ELT processes and makes data pipelines operate efficiently. Key Features: Intelligent scheduling, AI-driven error handling, and support for schema evolution. Best For: Engineers running multi-source data pipelines for warehousing or BI. These tools aren't fashionable – they're revolutionizing the way data engineering is done. Whether you're reading code, creating scalable ML pipelines, or handling large data workflows, there's a tool here that can Best suited for data engineers and ML scientists working on large-scale machine learning pipelines, especially those involving complex deep learning models. Feature / Tool DeepCode AI GitHub Copilot Tabnine Apache MXNet TensorFlow Primary Use Code Review Code Assistance Code Completion Deep Learning Machine Learning Language Support Multiple Multiple Multiple Multiple Multiple Ideal for Code Quality Coding Efficiency Coding Speed Large-Scale Models Advanced ML Models Real-Time Assistance Yes Yes Yes No No Integration Various IDEs Various IDEs Various IDEs Flexible Flexible Learning Curve Moderate Moderate Easy Steep Steep Hands-On AI Skills Every Data Engineer Should Develop Being AI-aware is no longer enough. Companies are seeking data engineers who can also prototype and support ML pipelines. Below are essential hands-on skills to master: 1. Programming Proficiency in Python and SQL Python remains the primary language for AI and ML. Libraries like Pandas, NumPy, and Scikit-learn are foundational. Additionally, strong SQL skills are still vital for querying and aggregating large datasets from warehouses like Snowflake, BigQuery, or Redshift. 2. Frameworks & Tools Learn how to integrate popular AI/ML tools into your stack: TensorFlow and PyTorch for building and training models and for building and training models MLflow for managing the ML lifecycle for managing the ML lifecycle Airflow or Dagster for orchestrating AI pipelines or for orchestrating AI pipelines Docker and Kubernetes for containerization and model deployment These tools are often highlighted in structured data engineering courses focused on production-grade AI implementation. 3. Model Serving & APIs Understand how to serve trained AI models using REST APIs or tools like FastAPI, Flask, or TensorFlow Serving. This allows models to be accessed by applications or business intelligence tools in real time. 4. Version Control for Data and Models AI projects require versioning not only of code but also of data and models. Tools like DVC (Data Version Control) are increasingly being adopted by engineers working with ML teams. If you're serious about excelling in this space, enrolling in a specialized data engineer training or data engineer online course that covers AI integration is a strategic move. Integrating Generative AI & LLMs into Modern Data Engineering The advent of Generative AI and Large Language Models (LLMs) like GPT and BERT has redefined what's possible in AI-powered data pipelines. For data engineers, this means learning how to integrate LLMs for tasks such as: Data summarization and text classification and Anomaly detection in unstructured logs or customer data in unstructured logs or customer data Metadata enrichment using AI-powered tagging using AI-powered tagging Chatbot and voice assistant data pipelines To support these complex models, engineers need to create low-latency, high-throughput pipelines and use vector databases (like Pinecone or Weaviate) for embedding storage and retrieval. Additionally, understanding transformer architectures and prompt engineering—even at a basic level—empowers data engineers to collaborate more effectively with AI and machine learning teams. If you're a Microsoft Fabric Data Engineer, it's worth noting that tools like Microsoft Synapse and Azure OpenAI are offering native support for LLM-driven insights, making it easier than ever to build generative AI use cases within unified data platforms. Want to sharpen your cloud integration skills too? Consider upskilling with niche courses like cloud engineer courses or AWS data engineer courses to broaden your toolset. Creating an AI-Centric Data Engineering Portfolio In a competitive job market, it's not just about what you know—it's about what you've built. As a data engineer aiming to specialize in AI, your portfolio must reflect real-world experience and proficiency. What to Include: End-to-end ML pipeline : From data ingestion to model serving : From data ingestion to model serving AI model integration : Real-time dashboards powered by predictive analytics : Real-time dashboards powered by predictive analytics LLM-based project : Chatbot, intelligent document parsing, or content recommendation : Chatbot, intelligent document parsing, or content recommendation Data quality and observability: Showcase how you monitor and improve AI pipelines Your GitHub should be as well-maintained as your résumé. If you've taken a data engineering certification online or completed an AI ML Course, be sure to back it up with publicly available, working code. Remember: Recruiters are increasingly valuing hybrid profiles. Those who combine data engineering for machine learning with AI deployment skills are poised for the most in-demand roles of the future. Pro tip: Complement your technical portfolio with a capstone project from a top-rated Data Analysis Course to demonstrate your ability to derive insights from model outputs. Conclusion AI is not a separate domain anymore—it's embedded in the very core of modern data engineering. As a data engineer, your role is expanding into new territory that blends system design, ML integration, and real-time decision-making. To thrive in this future, embrace continuous learning through AI and Machine Learning Courses, seek certifications like AI ML certification, and explore hands-on data engineering courses tailored for AI integration. Whether you're starting out or upskilling, taking a solid data engineer online course with an AI focus is your ticket to relevance. Platforms like Prepzee make it easier by offering curated, industry-relevant programs designed to help you stay ahead of the curve. The fusion of AI tools and data engineering isn't just a trend—it's the new standard. So gear up, build smart, and lead the future of intelligent data systems with confidence and clarity.
Time of India
35 minutes ago
- Time of India
Credila Financial Services files draft papers for Rs 5,000 crore IPO with Sebi
Credila Financial Services , an education finance company, has filed a UDRHP-I with SEBI to raise Rs 5,000 crore through an IPO. The company specializes in education loans for students pursuing higher studies in India and abroad. The total issue size aggregating up to Rs 5,000 crore comprises a fresh issue of equity shares aggregating up to Rs 3,000 crore and an offer for sale of equity shares aggregating up to Rs 2,000 crore of face value of Rs 10 per equity share. Also Read | JioBlackRock Broking receives SEBI approval to commence brokerage business The offer for sale of equity shares aggregating up to Rs 2,000 crore (face value of Rs 10 each) comprises up to Rs 950 crore by Kopvoorn B.V. ('Promoter Selling Shareholder') and up to Rs 1,050 crore by HDFC Bank Limited ('Other Selling Shareholder'). Live Events A pre-IPO placement of the specified securities may be undertaken by the company, in consultation with the BRLMs, prior to the filing of the Red Herring Prospectus with the ROC for an aggregate amount not exceeding Rs 600 crore ('Pre-IPO Placement'). The Pre-IPO Placement, if undertaken, will be at a price to be decided by the company in consultation with the BRLMs. If the Pre-IPO Placement is undertaken, the amount raised from the Pre-IPO Placement will be reduced from the fresh issue, subject to the offer complying with rule 19(2)(B) of the Securities Contracts (Regulation) rules, 1957, as amended ('SCRR'). The Pre-IPO Placement, if undertaken, shall not exceed 20% of the fresh issue. Axis Capital Limited , Citigroup Global Markets India Private Limited, Goldman Sachs (India) Securities Private Limited, IIFL Capital Services Limited ( formerly known as IIFL Securities Limited ) and Jefferies India Private Limited are the Book Running Lead Managers to the issue. The Company proposes to utilise the net proceeds towards augmenting the capital base to meet the Company's future capital requirements arising out of the growth of its business and assets. Credila Financial Services Limited is the second fastest growing education-focused NBFC in India in terms of year-on-year growth of Net Loans, with a year-on-year growth of 47.67% between the Financial Years 2024 and 2025. Also Read | Wakefit Innovations files DRHP with SEBI, plans to raise Rs 468 crore via fresh issue The company is the largest education-focused non-banking financial company in India (which peer set comprises three companies, including Credila), with Net Loans of Rs 41,469 crore as of March 31, 2025; restated net profit after tax of Rs 990 crore for the Financial Year 2025; and the highest disbursements of Rs 14,089 crore for the Financial Year 2024 (assessment performed for Financial Year 2024 given unavailability of peer data for Financial Year 2025). The company is the fastest growing education-focused NBFC in India (comprising three companies, including including Credila) with a CAGR of 64.96% in Net Loans between the Financial Years 2023 and 2025 and a year-on-year growth of 84.26% in assets under management between March 31, 2023 and March 31, 2024 (assessment performed for Financial Year 2024 given unavailability of peer data for Financial Year 2025).
Hans India
an hour ago
- Hans India
AI Tools & Skills Every Data Engineer Should Know
The lines between data engineering and artificial intelligence are increasingly blurred. As enterprises pivot towards intelligent automation, data engineers are increasingly expected to work alongside AI models, integrate machine learning systems, and build scalable pipelines that support real-time, AI-driven decision-making. Whether you're enrolled in a data engineer online course or exploring the intersection of data engineering for machine learning, the future is AI-centric, and it's happening now. In this guide, we explore the core concepts, essential skills, and advanced tools every modern AI engineer or data engineer should master to remain competitive in this evolving landscape. Foundational AI Concepts in Data Engineering Before diving into tools and frameworks, it's crucial to understand the foundational AI and ML concepts shaping the modern data engineer online course. AI isn't just about smart algorithms—it's about building systems that can learn, predict, and improve over time. That's where data engineers play a central role: preparing clean, structured, and scalable data systems that fuel AI. To support AI and machine learning, engineers must understand: Supervised and unsupervised learning models Feature engineering and data labeling Data pipelines that serve AI in real-time ETL/ELT frameworks tailored for model training Courses like an AI and Machine Learning Course or a machine learning engineer course can help engineers bridge their current skills with AI expertise. As a result, many professionals are now pursuing AI and ML certification to validate their cross-functional capabilities. One key trend? Engineers are building pipelines not just for reporting, but to feed AI models dynamically, especially in applications like recommendation engines, anomaly detection, and real-time personalization. Top AI Tools Every Data Engineer Needs to Know Staying ahead of the rapidly changing data engineering world means having the right tools that speed up your workflows, make them smarter, and more efficient. Here is a carefully curated list of some of the most effective AI-powered tools specifically built to complement and boost data engineering work, from coding and improving code to constructing machine learning pipelines at scale. 1. DeepCode AI DeepCode AI is like a turbocharged code reviewer. It reviews your codebase and indicates bugs, potential security flaws, and performance bottlenecks in real-time. Why it's helpful: It assists data engineers with keeping clean, safe code in big-scale projects. Pros: Works in real-time, supports multiple languages, and integrates well with popular IDEs. Cons: Its performance is highly dependent on the quality of the training data. Best For: Developers aiming to increase code dependability and uphold secure data streams. 2. GitHub Copilot Created by GitHub and OpenAI, Copilot acts like a clever coding buddy. It predicts lines or chunks of code as you type and assists you in writing and discovering code more efficiently. Why it's helpful: Saves time and lessens mental burden, particularly when coding in unknown codebases. Pros: Minimally supported languages and frameworks; can even suggest whole functions. Cons: Suggestions aren't perfect—code review still required. Best For: Data engineers who jump back and forth between languages or work with complex scripts. 3. Tabnine Tabnine provides context-aware intelligent code completion. It picks up on your current code habits and suggests completions that align with your style. Why it's useful: Accelerates repetitive coding tasks while ensuring consistency. Pros: Lightweight, easy to install, supports many IDEs and languages. Cons: Occasionally can propose irrelevant or too generic completions. Best For: Engineers who desire to speed up their coding with little resistance. 4. Apache MXNet MXNet is a deep learning framework capable of symbolic and imperative programming. It's scalable, fast, and versatile. Why it's useful: It's very effective when dealing with big, complicated deep learning models. Pros: Support for multiple languages, effective GPU use, and scalability. Cons: Smaller community compared to TensorFlow or PyTorch, hence less learning materials. Best For: Engineers preferring flexibility in developing deep learning systems in various languages. 5. TensorFlow TensorFlow continues to be a force to be reckoned with for machine learning and deep learning. From Google, it's an engineer's preferred choice for model training, deployment, and big data science. Why it's useful: Provides unparalleled flexibility when it comes to developing tailor-made ML models. Pros: Massive ecosystem, robust community, production-ready. Cons: Steep learning curve for beginners. Best For: Data engineers and scientists working with advanced ML pipelines. 6. TensorFlow Extended (TFX) TFX is an extension of TensorFlow that provides a full-stack ML platform for data ingestion, model training, validation, and deployment. Why it's useful: Automates many parts of the ML lifecycle, including data validation and deployment. Key Features: Distributed training, pipeline orchestration, and built-in data quality checks. Best For: Engineers who operate end-to-end ML pipelines in production environments. 7. Kubeflow Kubeflow leverages the power of Kubernetes for machine learning. It enables teams to develop, deploy, and manage ML workflows at scale. Why it's useful: Makes the deployment of sophisticated ML models easier in containerized environments. Key Features: Automates model training and deployment, native integration with Kubernetes. Best For: Teams who are already operating in a Kubernetes ecosystem and want to integrate AI seamlessly. 8. Paxata Paxata is an AI-powered data prep platform that streamlines data transformation and cleaning. It's particularly useful when dealing with big, dirty datasets. How it's useful: Automates tedious hours of data preparation with intelligent automation. Major Features: Recommends transformations, facilitates collaboration, and integrates real-time workflows. Ideal For: Data engineers who want to prepare data for analytics or ML. 9. Dataiku Dataiku is a full-stack AI and data science platform. You can visually create data pipelines and has AI optimization suggestions. Why it's useful: Simplifies managing the complexity of ML workflows and facilitates collaboration. Key Features: Visual pipeline builder, AI-based data cleaning, big data integration. Best For: Big teams dealing with complex, scalable data operations. 10. Fivetran Fivetran is an enterprise-managed data integration platform. With enhanced AI capabilities in 2024, it automatically scales sync procedures and manages schema changes with minimal human intervention. Why it's useful: Automates time-consuming ETL/ELT processes and makes data pipelines operate efficiently. Key Features: Intelligent scheduling, AI-driven error handling, and support for schema evolution. Best For: Engineers running multi-source data pipelines for warehousing or BI. These tools aren't fashionable – they're revolutionizing the way data engineering is done. Whether you're reading code, creating scalable ML pipelines, or handling large data workflows, there's a tool here that can Best suited for data engineers and ML scientists working on large-scale machine learning pipelines, especially those involving complex deep learning models. Feature / Tool DeepCode AI GitHub Copilot Tabnine Apache MXNet TensorFlow Primary Use Code Review Code Assistance Code Completion Deep Learning Machine Learning Language Support Multiple Multiple Multiple Multiple Multiple Ideal for Code Quality Coding Efficiency Coding Speed Large-Scale Models Advanced ML Models Real-Time Assistance Yes Yes Yes No No Integration Various IDEs Various IDEs Various IDEs Flexible Flexible Learning Curve Moderate Moderate Easy Steep Steep Hands-On AI Skills Every Data Engineer Should Develop Being AI-aware is no longer enough. Companies are seeking data engineers who can also prototype and support ML pipelines. Below are essential hands-on skills to master: 1. Programming Proficiency in Python and SQL Python remains the primary language for AI and ML. Libraries like Pandas, NumPy, and Scikit-learn are foundational. Additionally, strong SQL skills are still vital for querying and aggregating large datasets from warehouses like Snowflake, BigQuery, or Redshift. 2. Frameworks & Tools Learn how to integrate popular AI/ML tools into your stack: TensorFlow and PyTorch for building and training models and for building and training models MLflow for managing the ML lifecycle for managing the ML lifecycle Airflow or Dagster for orchestrating AI pipelines or for orchestrating AI pipelines Docker and Kubernetes for containerization and model deployment These tools are often highlighted in structured data engineering courses focused on production-grade AI implementation. 3. Model Serving & APIs Understand how to serve trained AI models using REST APIs or tools like FastAPI, Flask, or TensorFlow Serving. This allows models to be accessed by applications or business intelligence tools in real time. 4. Version Control for Data and Models AI projects require versioning not only of code but also of data and models. Tools like DVC (Data Version Control) are increasingly being adopted by engineers working with ML teams. If you're serious about excelling in this space, enrolling in a specialized data engineer training or data engineer online course that covers AI integration is a strategic move. Integrating Generative AI & LLMs into Modern Data Engineering The advent of Generative AI and Large Language Models (LLMs) like GPT and BERT has redefined what's possible in AI-powered data pipelines. For data engineers, this means learning how to integrate LLMs for tasks such as: Data summarization and text classification and Anomaly detection in unstructured logs or customer data in unstructured logs or customer data Metadata enrichment using AI-powered tagging using AI-powered tagging Chatbot and voice assistant data pipelines To support these complex models, engineers need to create low-latency, high-throughput pipelines and use vector databases (like Pinecone or Weaviate) for embedding storage and retrieval. Additionally, understanding transformer architectures and prompt engineering—even at a basic level—empowers data engineers to collaborate more effectively with AI and machine learning teams. If you're a Microsoft Fabric Data Engineer, it's worth noting that tools like Microsoft Synapse and Azure OpenAI are offering native support for LLM-driven insights, making it easier than ever to build generative AI use cases within unified data platforms. Want to sharpen your cloud integration skills too? Consider upskilling with niche courses like cloud engineer courses or AWS data engineer courses to broaden your toolset. Creating an AI-Centric Data Engineering Portfolio In a competitive job market, it's not just about what you know—it's about what you've built. As a data engineer aiming to specialize in AI, your portfolio must reflect real-world experience and proficiency. What to Include: End-to-end ML pipeline : From data ingestion to model serving : From data ingestion to model serving AI model integration : Real-time dashboards powered by predictive analytics : Real-time dashboards powered by predictive analytics LLM-based project : Chatbot, intelligent document parsing, or content recommendation : Chatbot, intelligent document parsing, or content recommendation Data quality and observability: Showcase how you monitor and improve AI pipelines Your GitHub should be as well-maintained as your résumé. If you've taken a data engineering certification online or completed an AI ML Course, be sure to back it up with publicly available, working code. Remember: Recruiters are increasingly valuing hybrid profiles. Those who combine data engineering for machine learning with AI deployment skills are poised for the most in-demand roles of the future. Pro tip: Complement your technical portfolio with a capstone project from a top-rated Data Analysis Course to demonstrate your ability to derive insights from model outputs. Conclusion AI is not a separate domain anymore—it's embedded in the very core of modern data engineering. As a data engineer, your role is expanding into new territory that blends system design, ML integration, and real-time decision-making. To thrive in this future, embrace continuous learning through AI and Machine Learning Courses, seek certifications like AI ML certification, and explore hands-on data engineering courses tailored for AI integration. Whether you're starting out or upskilling, taking a solid data engineer online course with an AI focus is your ticket to relevance. Platforms like Prepzee make it easier by offering curated, industry-relevant programs designed to help you stay ahead of the curve. The fusion of AI tools and data engineering isn't just a trend—it's the new standard. So gear up, build smart, and lead the future of intelligent data systems with confidence and clarity.
