- Managing Machine Learning Production Systems
- Deployment Pipelines
- Model Pipelines
- Data Pipelines
- Machine Learning Engineering for Production
- Human-level Performance (HLP)
- Concept Drift
- Model baseline
- Project Scoping and Design
- ML Deployment Challenges
- ML Metadata
- Convolutional Neural Network
Machine Learning Engineering for Production (MLOps)専門講座
Become a Machine Learning expert. Productionize your machine learning knowledge and expand your production engineering capabilities.
提供:


学習内容
Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements.
Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application.
Build data pipelines by gathering, cleaning, and validating datasets. Establish data lifecycle by using data lineage and provenance metadata tools.
Apply best practices and progressive delivery techniques to maintain and monitor a continuously operating production system.
習得するスキル
この専門講座について
応用学習プロジェクト
By the end, you'll be ready to
• Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements
• Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application
• Build data pipelines by gathering, cleaning, and validating datasets
• Implement feature engineering, transformation, and selection with TensorFlow Extended
• Establish data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas
• Apply techniques to manage modeling resources and best serve offline/online inference requests
• Use analytics to address model fairness, explainability issues, and mitigate bottlenecks
• Deliver deployment pipelines for model serving that require different infrastructures
• Apply best practices and progressive delivery techniques to maintain a continuously operating production system
• Some knowledge of AI / deep learning • Intermediate skills in Python • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
• Some knowledge of AI / deep learning • Intermediate skills in Python • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
専門講座の仕組み
コースを受講しましょう。
Courseraの専門講座は、一連のコース群であり、技術を身に付ける手助けとなります。開始するには、専門講座に直接登録するか、コースを確認して受講したいコースを選択してください。専門講座の一部であるコースにサブスクライブすると、自動的にすべての専門講座にサブスクライブされます。1つのコースを修了するだけでも結構です。いつでも、学習を一時停止したり、サブスクリプションを終了することができます。コースの登録状況や進捗を追跡するには、受講生のダッシュボードにアクセスしてください。
実践型プロジェクト
すべての専門講座には、実践型プロジェクトが含まれています。専門講座を完了して修了証を獲得するには、成功裏にプロジェクトを終了させる必要があります。専門講座に実践型プロジェクトに関する別のコースが含まれている場合、専門講座を開始するには、それら他のコースをそれぞれ終了させる必要があります。
修了証を取得
すべてのコースを終了し、実践型プロジェクトを完了すると、修了証を獲得します。この修了証は、今後採用企業やあなたの職業ネットワークと共有できます。

この専門講座には4コースあります。
Introduction to Machine Learning in Production
In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application.
Machine Learning Data Lifecycle in Production
In the second course of Machine Learning Engineering for Production Specialization, you will build data pipelines by gathering, cleaning, and validating datasets and assessing data quality; implement feature engineering, transformation, and selection with TensorFlow Extended and get the most predictive power out of your data; and establish the data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas.
Machine Learning Modeling Pipelines in Production
In the third course of Machine Learning Engineering for Production Specialization, you will build models for different serving environments; implement tools and techniques to effectively manage your modeling resources and best serve offline and online inference requests; and use analytics tools and performance metrics to address model fairness, explainability issues, and mitigate bottlenecks.
Deploying Machine Learning Models in Production
In the fourth course of Machine Learning Engineering for Production Specialization, you will learn how to deploy ML models and make them available to end-users. You will build scalable and reliable hardware infrastructure to deliver inference requests both in real-time and batch depending on the use case. You will also implement workflow automation and progressive delivery that complies with current MLOps practices to keep your production system running. Additionally, you will continuously monitor your system to detect model decay, remediate performance drops, and avoid system failures so it can continuously operate at all times.
提供:

deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
よくある質問
返金ポリシーについて教えてください。
1つのコースだけに登録することは可能ですか?
学資援助はありますか?
無料でコースを受講できますか?
このコースは100%オンラインで提供されますか?実際に出席する必要のあるクラスはありますか?
What is machine learning engineering for production? Why is it relevant?
What is the Machine Learning Engineering for Production (MLOps) Specialization about?
What will I be able to do after completing the Machine Learning Engineering in Production (MLOps) Specialization?
What background knowledge is necessary for the Machine Learning Engineering for Production (MLOps) Specialization?
Who is the Machine Learning Engineering for Production (MLOps) Specialization for?
How long does it take to complete the Machine Learning Engineering for Production (MLOps) Specialization?
Who is the Machine Learning Engineering for Production (MLOps) Specialization by?
Is this a standalone course or a Specialization?
Do I need to take the courses in a specific order?
Can I apply for financial aid?
Can I audit the Machine Learning Engineering for Production (MLOps) Specialization?
How do I get a receipt to get this reimbursed by my employer?
I want to purchase this Specialization for my employees. How can I do that?
専門講座を修了することで大学の単位は付与されますか?
さらに質問がある場合は、受講者ヘルプセンターにアクセスしてください。