Anand Ankit (41 results)

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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condition: New.

Condition: New. pp. 252.

Condition: New. pp. 252 3:B&W 7.5 x 9.25 in or 235 x 191 mm Perfect Bound on White w/Gloss Lam.

Condition: New. pp. 252.

Condition: New. pp. 121.

Condition: New. pp. 121.

Hardcover. Condition: New. Abiotic stresses, such as low or high temperature, deficient or excessive water, high salinity, heavy metals, and ultraviolet radiation, are hostile to plant growth and development, leading to great crop yield penalty worldwide.Abiotic stress factors are the non-living factors influencing the metabolism, growth and development of plants tissues at that particular time when such abiotic stress affect them. This triggers a wide range of plant responses, such as altered gene expression, cellular metabolism, changes in growth rates, and crop yields.Various physio-biochemical and molecular responses are being formulated in plants in response to abiotic stress, so the plant can survive even in such extreme environmental conditions. These stress response,are orchestrated by a complex regulatory network involving upstream signalling molecules including stress hormones, reactive oxygen species, gasotransmitters, polyamines, phytochromes, and calcium, as well as downstream gene regulation factors, particularly transcription factors.The present edited book is a modest attempt to present. Diverse physio-molecular responses in plants in reciprocation to abiotic stress. Conceptual approaches being used to quantify the expression of the multiple stress responses in plants. Modus operandi illustrated in understandable pattern for the benefit of researchers, teachers and students.…

Condition: New. pp. 121.

Condition: New. pp. 56.

Condition: New. pp. 104.

Condition: New. pp. 136.

Condition: New. pp. 200.

Paperback. Condition: New.

Taschenbuch. Condition: Neu. Gastroretentive Nanoparticulate Drug Delivery Systems | Novel Approaches Of Nanotechnology | Ankit Anand Kharia (u. a.) | Taschenbuch | 200 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783846514993 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.…

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Paperback. Condition: new. Paperback. Transform your Machine Learning Operations (MLOps) projects into reliable and scalable data products that meet the complex demands of data science.This practical guide is for data scientists, machine learning engineers, data leaders, and analytics professionals who want to move beyond notebooks, experiments, and one-time models. Analyze the real reason so many machine learning projects fail, and you will find that the problem is often not the algorithm. It is the data pipeline, the deployment process, the missing monitoring, the weak governance, or the lack of business ownership. This book shows how to treat models as living data products that must be designed, deployed, monitored, improved, and trusted.Explore the full MLOps lifecycle, from data strategy and data contracts to model engineering, CI/CD pipelines, cloud infrastructure, model observability, and production machine learning. Design systems that can handle schema changes, data drift, feature drift, silent failures, unreliable data feeds, and changing business needs. Apply practical thinking to modern data platforms, data warehouses, data lakes, lakehouses, streaming architecture, automated retraining, model registries, and the tools that help data teams build dependable AI systems.Evaluate the next frontier of applied AI with chapters on LLMOps, generative AI, prompt engineering, Retrieval-Augmented Generation (RAG), hallucination monitoring, explainable AI (XAI), Human-in-the-Loop (HITL) systems, and responsible AI governance. Create better enterprise AI applications by understanding how large language models change the deployment game while still requiring the same discipline, testing, observability, cost management, and accountability that define strong MLOps.Assess your role not just as a model builder, but as an owner of business outcomes. The Deployed Data Scientist helps readers connect data science, machine learning, data governance, AI strategy, model deployment, cloud architecture, and business value into one practical roadmap. Whether you are building your first production model or leading a team responsible for enterprise AI, this book gives you the mindset, methods, and language to turn data science into systems that work. Transform your Machine Learning Operations (MLOps) projects into reliable and scalable data products that meet the complex demands of data science. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condition: New. Print on Demand pp. 56 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Condition: New. Print on Demand pp. 104 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: KHARIA ANKIT ANANDAnkit A. Kharia M.Pharm.(Gold Medalist)(Ph.D), working as Asst.Prof. at Oriental College of Pharmacy, Bhopal. His biography is included in 2000 Outstanding Intellectual of 21st Century by IBC,Cambridge,UK. His a.…

Condition: New. PRINT ON DEMAND pp. 56.

Paperback. Condition: new. Paperback. Transform your Machine Learning Operations (MLOps) projects into reliable and scalable data products that meet the complex demands of data science.This practical guide is for data scientists, machine learning engineers, data leaders, and analytics professionals who want to move beyond notebooks, experiments, and one-time models. Analyze the real reason so many machine learning projects fail, and you will find that the problem is often not the algorithm. It is the data pipeline, the deployment process, the missing monitoring, the weak governance, or the lack of business ownership. This book shows how to treat models as living data products that must be designed, deployed, monitored, improved, and trusted.Explore the full MLOps lifecycle, from data strategy and data contracts to model engineering, CI/CD pipelines, cloud infrastructure, model observability, and production machine learning. Design systems that can handle schema changes, data drift, feature drift, silent failures, unreliable data feeds, and changing business needs. Apply practical thinking to modern data platforms, data warehouses, data lakes, lakehouses, streaming architecture, automated retraining, model registries, and the tools that help data teams build dependable AI systems.Evaluate the next frontier of applied AI with chapters on LLMOps, generative AI, prompt engineering, Retrieval-Augmented Generation (RAG), hallucination monitoring, explainable AI (XAI), Human-in-the-Loop (HITL) systems, and responsible AI governance. Create better enterprise AI applications by understanding how large language models change the deployment game while still requiring the same discipline, testing, observability, cost management, and accountability that define strong MLOps.Assess your role not just as a model builder, but as an owner of business outcomes. The Deployed Data Scientist helps readers connect data science, machine learning, data governance, AI strategy, model deployment, cloud architecture, and business value into one practical roadmap. Whether you are building your first production model or leading a team responsible for enterprise AI, this book gives you the mindset, methods, and language to turn data science into systems that work. Transform your Machine Learning Operations (MLOps) projects into reliable and scalable data products that meet the complex demands of data science. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condition: new. Paperback. Transform your Machine Learning Operations (MLOps) projects into reliable and scalable data products that meet the complex demands of data science.This practical guide is for data scientists, machine learning engineers, data leaders, and analytics professionals who want to move beyond notebooks, experiments, and one-time models. Analyze the real reason so many machine learning projects fail, and you will find that the problem is often not the algorithm. It is the data pipeline, the deployment process, the missing monitoring, the weak governance, or the lack of business ownership. This book shows how to treat models as living data products that must be designed, deployed, monitored, improved, and trusted.Explore the full MLOps lifecycle, from data strategy and data contracts to model engineering, CI/CD pipelines, cloud infrastructure, model observability, and production machine learning. Design systems that can handle schema changes, data drift, feature drift, silent failures, unreliable data feeds, and changing business needs. Apply practical thinking to modern data platforms, data warehouses, data lakes, lakehouses, streaming architecture, automated retraining, model registries, and the tools that help data teams build dependable AI systems.Evaluate the next frontier of applied AI with chapters on LLMOps, generative AI, prompt engineering, Retrieval-Augmented Generation (RAG), hallucination monitoring, explainable AI (XAI), Human-in-the-Loop (HITL) systems, and responsible AI governance. Create better enterprise AI applications by understanding how large language models change the deployment game while still requiring the same discipline, testing, observability, cost management, and accountability that define strong MLOps.Assess your role not just as a model builder, but as an owner of business outcomes. The Deployed Data Scientist helps readers connect data science, machine learning, data governance, AI strategy, model deployment, cloud architecture, and business value into one practical roadmap. Whether you are building your first production model or leading a team responsible for enterprise AI, this book gives you the mindset, methods, and language to turn data science into systems that work. Transform your Machine Learning Operations (MLOps) projects into reliable and scalable data products that meet the complex demands of data science. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Condition: New. PRINT ON DEMAND pp. 104.