Productionizing AI

Chapter 1:  Introduction to AI & the AI EcosystemChapter Goal:  Embracing the hype and the pitfalls, introduces the reader to current and emerging trends in AI and how many businesses and organisations are struggling to get machine and deep learning operationalizedNo of pages: 30Sub -Topics1.The AI ecosystem2.Applications of AI3.AI pipelines4.Machine learning5.Neural networks & deep learning6.Productionizing AI

Chapter 2:  AI Best Practise & DataOpsChapter Goal: Help the reader understand the wider context for AI, key stakeholders, the importance of collaboration, adaptability and re-use as well as DataOps best practice in delivering high-performance solutionsNo of pages: 20Sub - Topics1.Introduction to DataOps and MLOps  2.Agile development3.Collaboration and adaptability4. Code repositories5. Module 4: Data pipeline orchestration6.CI / CD7.Testing, performance evaluation & monitoring

Chapter 3: Data Ingestion for AIChapter Goal: Inform on best practice and the right (cloud) data architectures and orchestration requirements to ensure the successful delivery of an AI project.No of pages : 20Sub - Topics: 1.Introduction to data ingestion2.Data stores for AI3. Data lakes, warehousing & streaming4.  Data pipeline orchestration

Chapter 4: Machine Learning on CloudChapter Goal: Top-down ML model building from design thinking, through high level process, data wrangling, unsupervised clustering techniques, supervised classification, regression and time series approaches before interpreting results and algorithmic performance No of pages: 20Sub - Topics: 1. ML fundamentals2. EDA & data wrangling3. Supervised & unsupervised machine learning4. Python Implementation5. Unsupervised clustering, pattern & anomaly detection6. Supervised classification & regression case studies: churn & retention modelling, risk engines, social media sentiment analysis7. Time series forecasting and comparison with fbprophet

Chapter 5: Neural Networks and Deep LearningChapter Goal: Help the reader establish the right artificial neural network architecture, data orchestration and infrastructure for deep learning with TensorFlow, Keras and PyTorch on CloudNo of pages: 40Sub - Topics: 1. An introduction to deep learning2. Stochastic processes for deep learning3. Artificial neural networks4. Deep learning tools & frameworks5. Implementing a deep learning model6. Tuning a deep learning model7. Advanced topics in deep learning

Chapter 6: The Employer's Dream: AutoML, AutoAI and the rise of NoLo UIsChapter Goal: Building on acquired ML and DL skills, learn to leverage the growing ecosystem of AutoML, AutoAI and No/Low code user interfacesNo of pages: 20Sub - Topics: 1.AutoML2.Optimizing the AI pipeline3.Python-based libraries for automation4.Case Studies in Insurance, HR, FinTech & Trading, Cybersecurity and Healthcare5.Tools for AutoAI: IBM Cloud Pak for Data, Azure Machine Learning, Google Teachable Machines

Chapter 7: AI Full Stack: Application Development Chapter Goal: Starting from key business/organizational needs for AI, identify the correct solution and technologies to develop and deliver "Full Stack AI"No of pages: 20Su